ABSTRACT 
 
 
 
 
Title of Document: SOIL SLOPE FAILURE INVESTIGATION 
MANAGEMENT SYSTEM.   
  
 Raghav Sarathy Ramanathan, Master of Science, 
2012. 
  
Directed By: Professor Ahmet H. Aydilek, Department of 
Civil and Environmental Engineering. 
 
 
Highway slopes are exposed to environmental and climatic conditions, such as 
deforestation, cycles of freezing and thawing weather, heavy storms etc.  Over time 
these climatic conditions can influence slope stability in combination with other 
factors such as geological formations, slope angle and groundwater conditions. These 
factors contribute towards causing slope failures that are hazards to highway 
structures and the traveling public. Consequently, it is crucial to have a soil slope 
failure investigation management system to track, record, evaluate, analyze and 
review the soil slope failure data and soil slope remediation data so that cost effective 
and statistically efficient remedial plans may be developed. This paper presents the 
framework for developing such a system for The State of Maryland, using a GIS 
database and a collective overlay of maps to indicate potentially unstable highway 
slopes through spatial and statistical analysis.  
  
  
 
 
 
 
 
 
 
 
 
 
SOIL SLOPE FAILURE INVESTIGATION MANAGEMENT SYSTEM.    
 
 
 
By 
 
 
Raghav Sarathy Ramanathan. 
 
 
 
 
 
Thesis submitted to the Faculty of the Graduate School of the  
University of Maryland, College Park, in partial fulfillment 
of the requirements for the degree of 
Master of Science 
2012 
 
 
 
 
 
 
 
 
 
 
Advisory Committee: 
Professor Ahmet H. Aydilek, Chair 
Professor Sherif M. Aggour 
Professor Burak F. Tanyu 
 
  
 
 ii 
 
Acknowledgement 
 
I would like to first express my sincere gratitude to my advisor, Professor Ahmet 
H. Aydilek for his guidance and support throughout this study. His patience, 
knowledge and expert advice were instrumental in the completion of this study. 
I would also like to thank Mr. Haile Yerdaw, Dr. Xin Chen, and Dr. Sanjoon Han, 
from the Office of Material Technology (OMT) at the Maryland State Highway 
Administration (MDSHA), for their guidance, cooperation and valuable inputs 
through the course of this study. 
Special thanks to Professors Sherif M. Aggour and Burak F. Tanyu for serving on 
my thesis examination committee. I would also like to thank Mr. Rodney Wynn, Mr. 
Michel Sheffer, and Ms. Laurie Goudy from the MDSHA for their timely help and 
cooperation with parts of this study. 
Last but not the least; I would like to thank all faculty and staff members from the 
Civil and Environmental Engineering Department at University of Maryland, College 
Park and the MDSHA, for providing me with the opportunity to work together, and 
for their support and help. Most importantly, I express my love and thanks to my 
family and friends for their unconditional support. 
 
  
  
 
 iii 
 
Table of Contents 
 
 
Acknowledgement ....................................................................................................ii 
Table of Contents ................................................................................................... iii 
List of Tables .......................................................................................................... iv 
List of Figures .......................................................................................................... v 
CHAPTER 1 ............................................................................................................ 1 
INTRODUCTION ................................................................................................. 1 
CHAPTER 2 ............................................................................................................ 4 
2.1 INTRODUCTION ..................................................................................... 4 
2.2 BACKGROUND ....................................................................................... 5 
2.3 SMS COMPONENTS .............................................................................. 11 
2.3.1 MS Access database ............................................................................. 12 
2.3.2 Failure Field Sheet and Remediation Response Categorization ............. 15 
2.3.3 eGIS Slope Failure Content .................................................................. 20 
2.4 CONCLUSIONS ...................................................................................... 24 
CHAPTER 3 .......................................................................................................... 42 
3.1 INTRODUCTION ................................................................................... 42 
3.2 BACKGROUND ..................................................................................... 44 
3.3 DESCRIPTION OF STUDY AREA ........................................................ 47 
3.4 DATA SOURCES .................................................................................... 49 
3.5 PHYSICAL PARAMETERS ................................................................... 51 
3.6 DATA ANALYSIS .................................................................................. 52 
3.6.1 Elevation and slope angle ..................................................................... 52 
3.6.2 Land cover ........................................................................................... 53 
3.6.3 Storm event precipitation ...................................................................... 54 
3.6.4 Slope failure inventory ......................................................................... 54 
3.6.5 Physiographic provinces and lithology ................................................. 55 
3.7 SLOPE INSTABILITY MAPPING .......................................................... 56 
3.7.1 Logistic regression ............................................................................... 56 
3.7.2 Qualitative index overlay ...................................................................... 57 
3.8 CONCLUSIONS ...................................................................................... 60 
CHAPTER 4 .......................................................................................................... 81 
4.1 SUMMARY ............................................................................................. 81 
4.2 RECOMMENDATIONS FOR FUTURE WORK .................................... 85 
APPENDIX A ........................................................................................................ 88 
APPENDIX B......................................................................................................... 96 
APPENDIX C ...................................................................................................... 100 
REFERENCES .................................................................................................... 107 
 
 
 
 iv 
 
List of Tables 
 
Table 2.1: List of landslide causal factors (Source: Popescu, 1994) .......................... 27 
Table 2.2: List of existing SMS at different DOTs (Source: Lowell et al., 2002) ...... 28 
Table 2.3: List of Pros and Cons of other unstable slope management programs 
currently adopted in the USA ................................................................................... 29 
Table 3.1: Classification of soil movements by Varnes (1978) ................................. 62 
Table 3.2: Various mapping techniques- their scale of use, advantages and 
disadvantages .......................................................................................................... 63 
Table 3.3: List of parameters considered and their data sources ............................... 64 
Table 3.4: Physical parameters classified into sub-categories along with the density 
and normalized indices for each sub categories ........................................................ 65 
Table 3.5: The arbitrary weightage scheme assumed for the different test maps ....... 66 
 
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List of Figures 
Figure 2.1: Types of slope failures that have occurred in the State of Maryland ....... 30 
Figure 2.2: Types of landslides (USGS Fact Sheet 2004-3072) ................................ 31 
Figure 2.3: Proposed framework for developing a Slope Management System ......... 32 
Figure 2.4: The relationship tree for the MS Access database ................................... 33 
Figure 2.5: eGIS system architecture ....................................................................... 34 
Figure 2.6: Various components of the eGIS content ............................................... 35 
Figure 2.7: Workflow for the slope failure content within the OMT ......................... 36 
Figure 2.8: A screenshot of the create slope widget .................................................. 37 
Figure 2.9: A screenshot of the slope search widget ................................................. 38 
Figure 2.10: A screenshot of the slope details widget. .............................................. 39 
Figure 2.11: A screenshot of the editor widget.. ....................................................... 40 
Figure 2.12: The Oracle 11g database?s current relationship table ............................ 41 
Figure 3.1: Schematic showing the relationship between slope angle and height ...... 67 
Figure 3.2: A broad classification of all the slope instability mapping techniques .... 68 
Figure 3.3: A generalized geological map of the State of Maryland.......................... 69 
Figure 3.4: Distribution of failures in the different sub categories for (a) Elevation and 
(b) Slope angle ........................................................................................................ 70 
Figure 3.5: The distribution of slope failures for the different classes of land covers in 
the State of Maryland .............................................................................................. 71 
Figure 3.6: The failure distribution pattern for the different sub categories of (a) 2 
year 24 hour duration storm event and (b) 100 year 24 hour duration storm event .... 72 
Figure 3.7: Proposed failure type classification used by Maryland SHA based on 
Cruden and Varnes (1996) ....................................................................................... 73 
Figure 3.8: Failure distribution pattern for the different types of slope failures as per 
the modified Cruden and Varnes (1996) classification ............................................. 74 
Figure 3.9: Slope failure distribution patterns for different (a) Slope types and (b) 
Slope drainage section types .................................................................................... 75 
Figure 3.10: The slope failure distribution pattern for the different physiographic 
provinces ................................................................................................................. 76 
Figure 3.11: Schematic explaining the proposed mapping system. Currently the 
system uses the qualitative overlay model with a raster output. ................................ 77 
Figure 3.12: Variation of the failure density index and normalized failure density for 
the subclasses of parameters?????????????????????...78 
 
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Figure 3.13: Variation of failure density indices for the different parameter subclasses 
over area of the study region. ................................................................................... 79 
Figure 3.14: Failure density maps. ........................................................................... 80 
 
 
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CHAPTER 1 
INTRODUCTION 
 
Highway slopes are exposed to environmental and climatic conditions, such as cycles of 
freezing and thawing weather, heavy storms etc.  Over time these climatic conditions can 
influence slope stability in combination with other factors such as geological formations, slope 
angle and type of slope vegetation. These factors contribute towards causing slope failures that 
are hazards to highway structures and the traveling public.  The Federal Highway Administration 
(FHWA) has strongly suggested to all States that a landslide and rock slope inventory be 
developed so cost estimates and, eventually, remedial plans may be developed (Hopkins et al., 
2001). 
The focus now is on developing an early warning system, using a Geographic Information 
System (GIS) database and a collective overlay of maps that enables highway engineers to 
predict soil slides or slope failures in advance. The GIS database records and stores information 
about previous slope failures such as type and mode of failure, location of failure, slope gradient, 
slope vegetation, drainage type and remediation methodology. The collective overlay of maps 
consists of (i) Statewide state of nature maps that include geological formation maps, land cover 
data, highway slope failure inventory and elevation data, (ii) derivative maps that include data 
layers derived from the state of nature maps, e.g. Slope angle map, Storm event precipitation 
map, drainage section map. The system should also allocate weights to all these factors based on 
the extent of their influence over slope stability and slope failure history. 
Movement of soil mass along slopes can now be assessed by incorporating statistical analysis 
of data collected on the slopes into the assessment system. Many GIS based slope instability 
assessment systems use different methods to analyze the data collected. Each assessment system 
 
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may have different sets of parameters or ranking/weightage scheme, as they may be defined for 
different landscapes. Such a self sustaining system that analyzes the stability of slopes is called 
as slope stability management system (SMS) (Lee et al.,  2006). 
Different soil types and slope characteristics have varied levels of impact on parameters 
involved in analyzing stability of slopes. Although, it seems that many failures occur in highly 
plastic soils used in embankment construction; various soil slope instability mechanisms, such as 
surficial failure and rotational failures, have also been observed. Consequently, it is crucial to 
have a soil slope failure investigation management system to track, record, evaluate, analyze and 
review the soil slope failure and remediation data. This will provide data to evaluate the causes 
of soil slope failures and provide design, construction, and maintenance recommendations to 
minimize the potential of soil slope failures and repair.  
The primary objectives of this study were (i) gathering and evaluation of historical data on 
soil slope failures in the State of Maryland and developing the necessary protocols to incorporate 
that information into a GIS database, (ii) developing a database structure containing information 
relating to soil slope failures, and (iii) creating a quantitative model for predicting the probability 
of failure for highway slopes in the State of Maryland and translating the model output into color 
coded vulnerability maps. 
Currently, the State Highway Administration (SHA) of Maryland does not have a database or 
management system in place to evaluate and identify the details of highway slope failures, or 
track the remediation methods and costs. Hence, the immediate need to gather relevant 
information regarding current and previous highway slope failures is paramount to sustain an 
efficient SMS. Once all the relevant information relating to the slope failures is documented on 
site with the aid of adequate tools such as site survey sheets, handheld Global Positioning System 
 
 3 
 
(GPS) devices and other site investigation apparatus,  the data must be cataloged and stored in a 
comprehensive yet user friendly database. This enables faster retrieval of required information at 
any given time in the future. With this system of recording and storing information in a 
computerized format setup, the process of evaluating and analyzing data stored becomes a less 
complicated task with the database being significantly populated. 
 
 4 
 
CHAPTER 2 
 
2.1 INTRODUCTION 
SMS are early-warning systems that help formulate land-utilization regulations for minimizing 
the loss of life and property damage. The Office of Materials and Technology (OMT) has 
recognized a need to implement an electronic soil slope failure investigation management system 
to better track, record, evaluate, analyze and review the soil slope failure data and soil slope 
remediation data on Maryland SHA roadways. Over the years, many soil slope failures have 
occurred on or near SHA roadways. Figure 2.1 shows a collage of the different types of slope 
failures that have occurred in the state of Maryland. These soil slope failures have had a negative 
impact on the public safety and highway safety and cost SHA millions and thousands of dollars. 
For instance, the repair of the soil slope failure at MD 24 N/B from CSX Bridge to US 40 
Connector caused SHA approximately $1.5 million. 
Most of the current research focuses on developing an early warning system, using a GIS 
database and a collection of spatial data that enables prediction of soil slides or slope failures in 
advance. Such a system should have information on the various factors that influence slope 
stability and can be grouped into six categories: (i) Slope failure inventory data, (ii) information 
on geological formations, (iii) slope material and characteristics information, (iv) local 
topographic information such as slope height and slope angle, (v) remediation and maintenance 
information and (vi) weather condition information and data on other extraneous phenomenon 
The SHA currently does not have a database or management system in place to evaluate and 
identify the details of these failures, or track the remediation methods and costs. Popescu (1994) 
has listed a variety of ground conditions that may be conducive for slope instability such as 
highly plastic soils used in embankment construction, weak and collapsible material, contrast in 
 
 5 
 
permeability and stiffness within the fill material, e.g. stiff, dense material over plastic material.  
Popescu (2002) has also listed several natural geomorphologic processes and man-made physical 
processes that have a direct impact on soil mass movement in slopes (Table 2.1). The occurrence 
of these ground conditions and physiological processes individually or in combination can 
trigger different soil slope instability mechanisms, such as surficial erosion, rotational or 
transitional failures, rockfalls, slides, spread and debris flow (Cruden and Varnes, 1996). Figure 
2.2 shows the different type of soil slope instability mechanisms. Consequently, it is crucial to 
have a soil slope failure investigation management system to track, record, evaluate, analyze and 
review the soil slope failure data and soil slope remediation data. This will provide data to 
evaluate the causes of soil slope failures and provide design, construction, and maintenance 
recommendations to minimize the potential of soil slope failures and repair.  
While many publications discuss in depth on how to categorize slope failures into different 
hazard levels based on impact and consequences of failure (Pierson et. al, 1990; ODOT, 2001; 
UDOT, 2001; OHDOT, 2007; NYSDOT, 2007), few address issues regarding prioritizing 
remediation response towards these failed slopes. This research project has listed out vital factors 
that should be considered while deliberating upon the type of remediation technique that might 
be effective for each slope failure and also addresses prioritizing resource and budget allocations 
for these remediation projects. With this asset management and decision support tool, the SHA 
of Maryland will be able decide which slope failures would require immediate repair, thereby 
prioritizing and optimizing their remediation response to slope failures.  
 
2.2 BACKGROUND 
Different soil types and slope characteristics will have varied levels of impact on parameters 
involved in analyzing stability of slopes. Hence there are many SMSs currently in use in many 
 
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parts of the world. According to Rose (2005), as of 2005 ten states and four countries have 
adopted slope stability management programs to help identify unstable slopes for remediation. 
The following pages will discuss in detail, various slope management programs developed by 
different Departments of Transportation (DOTs) that are used in slope stability assessment. 
As discussed above, different SMSs have different methods of ranking and analysis. 
However data analysis methods used by SMSs are broadly classified into three types (Glade et 
al., 2005): (i) Expert analysis or heuristic analysis, (ii) statistical analysis of historic events, and 
(iii) mechanical approach. 
Heuristic or expert evaluation analysis makes use of past experience of experts who sets 
guidelines to analyze slope failures based on evolution of ground movements and failure modes 
and mechanisms that control the occurrence of such phenomenon. Even though the method is 
commonly used, it is a very subjective approach towards determining potentially unstable slopes 
and is the most widely used method up to the present (Glade et al., 2005).  
Statistical analysis makes use of regression functions and distribution curves to predict slope 
failure based on data collected from site or from the laboratory. It overcomes the insufficiencies 
inherent in the heuristic or experience based approach. Most of these models are created using 
GIS software by the use of probabilistic analysis by linearizing variables that are considered to 
affect slope stability (Hansen, 1984). 
Mechanical analysis involves calculating a stability coefficient, i.e. Factor of Safety from 
specific 1D, 2D or even 3D slope stability models. The choice of the model depends on the data 
availability of the various input parameters (Cruden and Fell, 2001). 
 
 7 
 
Various SMS have been developed and each system is unique in its own particular way, i.e. it 
may differ in the type of modeling technique used, or differ in reliability models used etc. But all 
these SMS have the following components, which are vital for any such assessment model: 
a) Data collection and verification system 
b) GIS database management system 
c)  Index maps 
d) Statistical or deterministic model 
e) Validation of model 
The first two components are complementary and used together. A data collection system 
needs to be integrated with a GIS database, thus making it easy for retrieval, manipulation and 
review. Also this integration of the two components creates a data mine that is compatible with 
all mapping software, thus enabling projection of the data onto maps. 
Figure 2.3 shows the proposed framework for developing the slope stability management 
system; based on the Unstable Slope Management Systems adopted by different states across the 
USA which have more or less a similar framework for the rating system. The SMS currently in 
use by other states were used as a benchmark or platform for selection of rating criterion and 
field classification of failures and other such input parameters.  
Most of the unstable Slope Management Systems are based on the Rockfall Hazard Rating 
system (RHRS) developed by the Oregon Dept. of Transportation (ODOT). This system was 
proposed by Pierson et al. (1990) with the intent of proactive rockfall site identification and 
prioritization. The project was funded by the Federal Highway Administration (FHWA) and ten 
other states.  
 
 8 
 
The FHWA has developed a Rockfall Database Management Program (RDMP) specifically 
for the RHRS. The program has a standalone database that does not require any supporting 
software, which offers the advantage of allowing rapid transfer of information (Pierson et al., 
1990). A total of 3000 slope were inventoried, and subjectively classified into categories A, B 
and C. Where A and B categories consists of rock slopes that have a higher probability of failure 
and may have severe consequences in the case of failures and are further classified based on a 
ranking system developed by the TDOT. Category C consists of slopes that are weathered by 
erosion and hence neglected. Categories A and B are further investigated and rated using field 
sheets and an exponential scoring system with a base of 3. 
Tennessee DOT has developed GIS application for the management of landslides along the 
Tennessee highways. This application includes the development of a statewide landslide 
database and the production of 31 thematic maps accessible via the Internet. Essential 
information pertaining to a landslide include: (i) attribute data e.g. type of slide, surficial 
geology, remedial actions taken, and associated costs, (ii) temporal data e.g. as dates of landslide 
activity and remedial actions, and (iii) spatial data e.g. geographic location of the landslide, site 
special geological conditions and nearby related features. The GIS landslide database is linked 
with the above-mentioned attribute, temporal and spatial data, in an geodatabase for cataloguing, 
visualizing and managing landslides along the State Routes and Interstate Highways (Rose, 
2005).  
Kentucky Transportation Cabinet (KyTC) together with the University of Kentucky has also 
carried out similar type of work. The database developed by KyTC consists of rock slope, 
landslide and soil and rock engineering data for risk management of landslides and rock slopes. 
More than 10,000 rock slopes were examined and were rated using the RHRS. The ratings 
 
 9 
 
provide a priority list of sites which require immediate remedial or mitigation measures. A 
geotechnical database was created using Oracle database software which stored rock slope and 
landslide attributes along with location information and site photographs. The rock slope and 
landslide segments of the geotechnical database establish a program for allocating funding for 
remediation of slopes that are identified as high risk (Hopkins, et al., 2001). 
The Washington State Department of Transportation (WSDOT) has developed the Unstable 
Slope Management System (USMS) since 1993, which can be used for both rock-falls and 
landslides. Both slope condition and economic assessment were incorporated in the strategy for 
managing slopes. The information used for assessing slope conditions included the location of 
slope, whether the slope is on left or right of centerline, type of instabilities and frequency of 
slope failure. Economic assessment includes the estimation of annual maintenance cost 
associated with mitigating the unstable slope (Lowell et al., 2002). 
The Ohio Department of Transportation uses the Geological Hazard Management System 
(GHMS) to manage geological hazards data and activities related to planning, design, 
construction, and maintenance. The geological hazards include abandoned underground mines, 
karsts, and shoreline erosion. In 2007, a landslide hazard rating system was developed for the 
Ohio DOT and incorporated into the GHMS (Liang, 2007). This system evaluates six landslide 
risk factors that potentially impact the safety and operation of a roadway and adjacent highway 
structures. Each of the risk factors is rated using a scoring system similar to ODOT. The 
numerical scores of 3, 9, 27 and 81 represent the increasing hazard of each factor. 
The Alaska Dept. of Transportation (AKDOT) has a three step procedure to rate slopes. The 
first step involves preliminary rating of slopes into three categories: A (high probability of 
failure), B (moderate probability of failure) and C (low probability of failure). The second step is 
 
 10 
 
the slope hazard assessment. In this step, the slopes categorized into A and B receive a detailed 
assessment based on the Hazard Score calculated from the information obtained through field 
visit. The final step is the slope risk assessment. Here the slopes are assessed based on the 
severity of hazard calculated from the previous step, maintenance frequency and annual 
maintenance cost (Huang et al., 2009).  
The SMS developed in Taiwan is built on a well designed Management Information Systems 
(MIS). The information stored in the MIS can be displayed using the functionalities of GIS.  
Then the influence of various factors on landslides is assessed. The SMS is capable of accepting 
more than one format of input. Also, maintenance and monitoring information of slopes is given 
priority in the frame work. All data collected is thoroughly and meticulously classified and 
indexed into different databases. Hence, this SMS has 4 different databases based on the 
categories of data collected. . It also allows for cross-database search process. The search engine 
can search for records with either administrative regions or data types as queries (Lee, et al., 
2006). 
In summary, all the SMS discussed above despite using quantitative analysis in calculating 
hazard indices for rating unstable slopes, have an inherent factor of subjectivity linked with the 
analysis. Table 2.2 lists some of the DOTs that have adopted SMSs and the number of slopes 
analyzed in respective studies. Table 2.3 highlights the pros and cons of the various SMS adopted 
by different state agencies in the USA. Most of the survey forms used by the DOTs to record 
failure information require the engineer on site to make expert judgments regarding slope failure 
characteristics and attributes. This might lead to over compensated hazard rating of relatively 
less hazardous slopes. It is this inherent drawback in the reviewed programs that is attempted to 
be addressed while developing the SMS for the State of Maryland. 
 
 11 
 
2.3 SMS COMPONENTS 
One of the crucial issues in GIS-based hazard assessment is the availability of suitable input data, 
which remain fundamentally inadequate in quantity and quality for the intended task (Huabin, et 
al., 2005). Since the GIS database is a central source for majority of information relating to slope 
failure data it is vital to review data collection procedures followed in the field for identifying the 
source of errors and uncertainty in site investigation techniques.  
Virtually all the instability factors collected in the field or derived in laboratory are affected 
by inaccuracies or errors whose magnitude cannot readily be estimated or controlled during the 
subsequent phase of data analysis or modeling (Carrara et al., 1995). Thus, it is important to 
preserve and maintain homogeneity throughout the process of data collection in the field. For 
this purpose, it is essential to have a systematic method of approach towards data collection and 
this requirement was satisfied by developing a slope failure field sheet which is discussed in 
detail in the latter part of the chapter.  
Two fundamental rules must be observed when creating a database (Leroi, 1997): the 
information must be homogeneous, i.e., it must have the same work scale and the same 
geographic projection system, and the database must be organized into basic monothematic 
layers, each of which contains homogeneous data (Carrara et al., 1999). 
A rough outline of tasks involved in developing the database system maybe listed as follows 
(Pierson et al., 1990): 
Preliminary data collection: All slope failures reported to the SHA office are visited by field 
engineers in person, and necessary data regarding these slope failures is collected using the 
failure field sheet. 
Database population: All the data recorded using the failure field sheet is keyed into the database 
 
 12 
 
using a simple Graphic User Interface (GUI). 
Design recommendation and cost estimate: Design recommendations and cost estimations are 
based on factors such as Highway classification, Ratio of maintenance cost to repair cost and 
Frequency of maintenance and related projects. 
Annual review and update: An annual report is prepared on the efficiency of the system in place 
and based on feedback from the report and engineers using the system necessary changes and 
updates are made to ensure enhanced performance of system. 
There are four primary components of the SMS developed for the State of Maryland. They 
are: (i) MS Access database, (ii) Failure field sheet and remediation response categorization, (iii) 
eGIS slope failure content, and (iv) Failure density mapping. The first 3 components will be 
discussed in detail in the following pages of this chapter and the final component will be 
discussed in the latter part of this document. 
2.3.1 MS Access database 
The database for the SMS was developed using MS Access and consists of 8 different tables. 
The first step taken towards developing the database was to decide on an efficient data-structure. 
Figure 2.4 shows the relationship tree of the database. The fields in each of the tables were 
arranged and grouped so that fields relevant to that particular table remained together. Each table 
represented similar fields that contributed to a particular aspect of slope stability management. 
Each table consists of a unique field that is assigned the role of a primary key, which enables the 
user or the software to uniquely identify any particular record. Each of these primary keys form 
links between the tables, thus making it easy to access information from more than one particular 
table at the same time.  
 
 13 
 
As shown in Figure 2.4, the primary key for each table is the Project ID field which is an 
auto-generated number by the software for each particular record. The data structure is such that 
there is one primary table to which all other tables are linked (Figure 2.4). This enhances data 
management tasks such as creating new records, editing and deleting existing records. The 8 
tables constituting the database are listed as follows: 
I. Failure type and location information table 
II. Dimensions of failure table 
III. Cause of failure information 
IV. Failure impact table 
V. Slope materials information table 
VI. Slope characteristics table 
VII. Remediation information table 
VIII. Vegetation information table 
Failure type and location information is the primary table, to which all other tables are 
linked. This table, as the name suggests, records and stores information relating to the location 
and type of failure. Information relating to location constitutes GPS coordinates, Northing and 
Easting values, Mile point information, Route number and name. Failure type information 
constitutes information about the mechanism of failure and weather conditions before failure, 
Project description provided for each case by the SHA, Contract # and FMIS# and other identity 
related information; which is the reason this table is assigned the role as the primary table. 
The dimensions of different aspects of the slope failure such as apparent depth of failure, 
scarp depth and width, distance of failure surface from original slope crown and toe, slope angle 
and slope height are recorded in the dimensions of failure table. These data are collected for 
 
 14 
 
different modes of failure. The cause of failure information table consists of information relating 
to natural or human activities that contributed towards the failure of the highway slope.  
   The failure impact table records information regarding the current and future potential of 
the slope failure to affect existing roadway and roadway structures. This section requires the 
engineer to make a subjective evaluation of the failure site and decide on the potential of the 
slope failure to affect roadway and structures beyond right of way of the Maryland SHA. 
      The slope materials information table records data pertaining to the origin of soil or rock 
type on slope, the soil type occurring on the failed slope and the physiographic classification of 
the failed slope. 
The slope characteristics information table records data relating to the slope aspect: convex 
or concave, slope gradient, vegetation density on slope, surface and sub surface drainage 
conditions, surface water conditions and groundwater conditions. Vegetation information table 
stores the percentage distribution of vegetation or land cover type present on the failed slope.  
   The remediation information table stores data relating to the existing remedial activities 
present on site, the suggested remediation methodology, the remediation start and end date and 
the remediation status of the failed slope and the cost of construction of remediation. Vegetation 
information table stores the percentage distribution of vegetation or land cover type present on 
the failed slope.  
These tables consist of information collected from the field using failure field sheets 
(Appendix A). The Failure Field Sheet in essence is similar to the survey sheets used by 
engineers on site. The Field Sheet is a form which records only that information related to slope 
failure which may be used for further analysis and aid in hazard rating of that particular failure, 
 
 15 
 
thereby eliminating the collection of data that may be irrelevant or not directly related to stability 
analysis and slope hazard rating and is discussed in detail in the following section. 
2.3.2 Failure Field Sheet and Remediation Response Categorization 
 
2.3.2.1 Failure Field Sheet 
 
The Failure Field Sheet in essence is similar to the survey sheets used by engineers on site. In a 
broad sense, it may also be referred to as the input for the SMS. The Field Sheet is a form which 
records only that information related to slope failure which may be used for further analysis and 
aid in hazard rating of that particular failure, thereby eliminating the collection of data that may 
be irrelevant or not directly related to stability analysis and slope hazard rating. 
The purpose of the Failure Field Sheet is to standardize the current approach followed by 
engineers on site with regard to highway slope failures reported to and dealt with by the SHA, 
Maryland. It also ensures that data is collected in a consistent and uniform manner.  
The failure field sheet has provisions to record parameters such as the type of slope (cut, fill, 
mechanically-stabilized fill etc), types of failures, scale of failures, causes of failures, and 
mitigation methodologies. Information regarding these parameters is recognized as the most 
important data to evaluate failure potential as well as performance of slope stability (OHDOT, 
2007; AKDOT, 2009; WSDOT, 2002; Lee et al, 2006). These records will be collected to 
analyze the influence of factors of slope stabilization and evaluate mitigation performance.   
This is a vital component of any SMS. The Federal Highway Administration?s highway slope 
maintenance manual has such a form that may be used to survey slope failure sites and is called 
the slope inspection manual. The slope inspection manual has the provision to record the most 
basic and rudimentary information regarding slope failures. The failure field sheet derives upon 
the slope inspection manual provided by the Federal Highway Administration as a base content 
 
 16 
 
and adding upon it based on syntheses of expert opinions of Maryland SHA engineers and data 
collection practices followed by other state agencies.  
When a highway slope failure is reported to the Maryland SHA, engineers from the Office of 
Materials Technology visit the failure site to record initial failure information. Upon arrival at 
the failure site, the engineers perform a preliminary inspection of the failure site during which 
they fill in sections 1 and 2 of the failure field sheet. These include establishing general site 
information such as GPS coordinates- latitude and longitude in decimal degrees with a precision 
of at least 5 digits, recording Milepoint data, route information, location of failure with respect to 
roadway, type of failure based on the classification provided in the field sheet. If multiple 
failures occur along the same highway, a note must be made of the total number of failure sites 
along the particular highway. Also the weather conditions immediately before failure is 
retroactively determined and recorded. 
The engineers then proceed to measure the various dimensions of failure as illustrated in 
section 3 of the failure field sheet (Appendix A, page 3). Section 4 requires the engineers to 
provide a subjective evaluation regarding the potential of the highway slope failure to cause 
further damage to roadway and structures beyond right of way of the Maryland SHA. In this 
section, engineers are also required to measure the extent of slope movements by recording the 
dimensions of dips and cracks visible along the roadway. During the preliminary examination, 
the structures and utilities in the vicinity of failure are observed and recorded. Also, the type of 
land usage classification based on section 5 of the failure field sheet is recorded. In section 6, the 
engineers make note of those structures or utilities that are affected by the highway slope failure. 
Once this is complete, the engineers based on in-situ tests and expert opinions establish the 
slope characteristics information, record vegetation information and soil type data (sections 7 
 
 17 
 
and 8). Also, the cause of failure is determined and recorded using the provisions provided in 
section 10 of the failure field sheet.  
Section 9 is used by engineers to record the existing remediation activities observed on site. 
Section 10 provides a comprehensive list of slope remediation methodologies. The engineers 
may provide or suggest ideal remediation methods based on the list provided in this section. 
Section 11 is used to monitor the remediation phase of a highway slope failure. 
All information recorded in the failure field sheet is currently preserved in paper format. The 
data stored in paper format is transferred to electronic format, by keying in all information into 
the Oracle database through the eGIS application interface. The eGIS application developed by 
the Highway Information and Services Division (HISD) of the Maryland SHA is described in 
detail in the latter part of this chapter. 
 
2.3.2.2 Remediation Response Categorization  
 
The remediation response categorization is included as another component of the SMS to help 
prioritize the remediation response adopted for the various highway slope failures in the state of 
Maryland based on an extensive set of categories that are considered to affect the functionality of 
highways. For example, a particular highway slope failure with high potential to affect the 
roadway would require immediate attention when compared to another highway slope failure 
with lower potential to affect the roadway.  
As it is impossible to eliminate the subjective bias in determining which of the highway slope 
failures require immediate attention in relation to the other highway slope failures, a set of 
parameters that are considered to affect the remediation response for any highway slope failure is 
introduced to reduce the influence of subjectivity. 
 
 18 
 
The primary purpose of this component is to assign priority for remediation of certain 
highway slope failures over others based on a range of parameters considered to affect the proper 
functioning of the highway. Another purpose of this component is to help the Maryland SHA 
with budget allocation for remediation of highway slope failures. With this component in place, 
the Maryland SHA would be able to allocate their resources and plan their budgeting 
appropriately between the various remediation projects effectively; thus saving a significant 
amount on time and money. 
With the SMS being in its nascent stage and its framework being setup recently, the 
functionality of this component is yet to be formulated in the decision making process of the 
Maryland SHA. Currently, the remediation response categorization is included as a 
recommendation sheet along with the final geotechnical report submitted following thorough 
analysis of the highway slope failure. The sheet provides information on the list of the categories 
considered while deliberating on the remediation response and will be used as a guide for the 
engineer to decide on the remediation and maintenance techniques to be implemented. The 
format of the remediation response categorization sheet is as shown in Figure B.1 in Appendix 
B.  
This section discusses the various categories considered to affect the remediation response 
towards highways slope failures and how they were shortlisted. The complete list of categories 
shortlisted and their definitions is provided in Table B.1.  
It was decided that a document containing a list of categories that might influence the SHA?s 
remediation response towards highway slope failures would be circulated among engineering 
staff at the OMT of Maryland SHA. The engineering staff would then provide their 
recommendations regarding the order or hierarchy of importance of the said categories.  
 
 19 
 
The engineers were asked to assign numbers between 1 and 16 (1 being most important and 
16 being the least important) on a survey sheet attached for each of the 16 categories based on 
their subjective evaluation of the system. These numbers are referred to as hierarchy numbers. 
This exercise proved to be very productive and brought to light newer categories that might 
influence the manner in which SHA or a district office may deal with a highway slope failure. 
The hierarchy numbers provided by the engineers were fed into a MS Excel sheet and the mean 
hierarchy number for each of the categories was calculated following which the standard 
deviation for each of the categories were calculated.  
Table B.2 provides the hierarchy numbers assigned by the different engineers along with the 
mean and standard deviation for each category. Categories suggested by engineers during the 
rating process are shaded in grey (Table B.2). In addition to completing the survey sheet, many 
engineers provided their recommendations and suggestions to further develop the rating system 
Some of the engineers had suggested that newer categories be included. The list of suggested 
additions to the category sheet is: (i) Distance to closest structure, (ii) Type of structure, (iii) 
Groundwater conditions, (iv) Vegetation conditions, (v) Utility impact, (vi) Rate of slope 
movement, (vii) Slope material properties, (viii) Subsurface conditions, (ix) Drainage and 
seepage conditions, (x) Availability of detour route, and (xi) Numbers of utilities affected. 
 
 
 20 
 
2.3.3 eGIS Slope Failure Content 
 
The Enterprise GIS (eGIS) Portal web application is an Intranet application utilized by the 
Maryland State Highway Administration to display, edit, and manage its valuable data. The 
portal provides broad access to geospatial information to foster collaboration between business 
units and to support critical business functions. The Office of Materials and Technology (OMT) 
utilizes eGIS to display, edit, and create slope failures along Maryland roads.  Furthermore, users 
can upload pictures of the slope failure site, hyperlink to as-built plans and geotechnical reports. 
2.3.3.1 System architecture 
 
eGIS utilizes a security architecture to facilitate and administrator defined user groups and roles. 
Figure 2.5 depicts the high-level system architecture for the eGIS application.  The eGIS Portal 
is dependent upon a number of external systems: (i) ArcGIS servers, (ii) eGIS web application 
and (iii) supplemental services and applications. 
The ArcGIS server stores all GIS data in the form of Geometry services or Map services. A 
map service enables publishing maps, features and data attributes on the web and creating user 
interfaces. Map services make the data stored in GIS layers available inside various applications 
accessible via internet or intranet thus catering to a wide range of users.  A Geometry service 
helps web applications to perform geospatial and geometric calculations such as buffering, 
simplifying, calculating areas and lengths, and projecting.   
The ArcGIS server system is setup such that it relies on cached and non-cached map services 
for most of its map data. Feature services are also usable for editing. A geometry service is used 
for specific cases throughout the portal. 
The configuration and content system works through the eGIS web application. This 
application enables the users to print maps and also generate MS Excel files from the web portal 
 
 21 
 
on to the desktop using tools available in the geometry service. The data is retrieved through the 
ArcGIS server Map services. 
Applications, web sites, and documents can be linked through a variety of means from the 
eGIS portal; external applications can link to the eGIS system, and if necessary, pass different 
parameters to focus the user to the necessary data of the application. Widgets, custom result 
grids, and other functions can rely on other services developed in a variety of ways to provide 
functionality and data to the specific workflows. The various components of the eGIS application 
are shown in Figure 2.6.  
The eGIS architecture leverages ESRI?s ?Widget Framework? allowing the application to be 
easily extended as new features and capabilities are required. The eGIS slope failure content has 
three widgets to maintain soil slope failures: Details, Edit, Create.  The next section will explain 
the workflow and functionality of the widgets. 
2.3.3.2 Workflow and widget functionality 
 
The eGIS slope failure content has three widgets to maintain soil slope failures: Details, Edit, 
Create.  The Details widget displays important and relevant slope failure information based on 
search parameters. The Edit widget enables engineers to edit slope failure information stored in 
the database. The Create widget enables users to report a slope failure by creating a new feature 
on the map content. Figure 2.7 shows the workflow for the slope failure content within the OMT.   
The Create widget enables users to create slope failures for OMT approval. The eGIS user 
determines if the slope failure is emergency or non-emergency, inputs the Failure Date on a 
preset Calendar and includes their Contact Information of the Create Slope Widget.  The Slope 
Failure location is entered by a Location coordinate or Route information and mile marker. Upon 
completion of the Create Slope, an email is generated and sent to the OMT Staff which includes 
 
 22 
 
the pertinent slope failure information as well as any pictures the eGIS user attaches.  The widget 
uses a security setup where groups and roles are defined for the OMT staff.  The email is sent to 
a pre-defined group using the security parameters. Figure 2.8 shows screenshots of the Create 
failure widget. 
eGIS users utilize the Slope Search Widget to find slope failures based on spatial reference or 
attribute information.  Users can query on slope failures within a SHA District, County, along a 
route type or a specific route.  In addition, users can look for specific attribute information with a 
spatial reference. Figure 2.9 shows screenshots of the slope search widget 
When a slope failure is selected in the eGIS Web Application, the user reviews attribute 
information for the record in the query results window located at the bottom of the eGIS 
Window.  This panel is configurable by the eGIS Technical Team along with the data owner.  
Together they determine which fields are presented in the query results grid.  By using the 
?Details? Function, the application invokes the Details Widget which provides further attribute 
information from the map service.  This is configurable in the widget and determined by the data 
owner which fields are presented. Figure 2.10 shows the list of data currently displayed by in the 
details tab. 
The OMT Slope Editor Group has permissions to edit slope attributes using the ?Slope Edit? 
widget.  The Slope Edit Widget updates an Oracle table that replaces the current OMT Access 
database.  The widget uses text fields, pull down menus, multi text boxes and comment fields to 
maintain the attribution of slope failures.   If an eGIS User does not have access to the widget, 
the widget will be grayed out.  Edits made to the slope attribute record are saved in real-time.  
The Slope Edit is invoked from the Results Grid under the Functions called ?Edit.? The Slope 
Edit Widget has 13 tabs.  Each updates a separate Oracle table. The following section lists 
 
 23 
 
specific information about the Oracle tables mentioned. Figure 2.11 shows the multiple tabs of 
the editor widget. 
2.3.3.3 Oracle SDE database 
 
All these widgets enable SHA users to manipulate the slope failure information stored in an 
Oracle 11g SDE database. The Oracle SDE database stores all data relevant to the slope failures 
that have occurred in the State of Maryland. It also consists of data stored in the MS Access 
database mentioned previously.  
The reason for migration from MS Access database file to an Oracle SDE database is that it 
allows for higher data volume and efficient processing. An Oracle SDE database also provides 
advanced spatial features and supports high-end GIS solutions.  
The MVA Center in Glen Burnie, MD is SHA?s central repository of Information 
Technology servers.  The GIS Services Team within the Highway Information Services Division 
(HISD) maintains the only spatial license/tables for Oracle SDE at the Maryland SHA.  The 
Slope Failure Access tables have been converted to Oracle 11g relational tables stored at the 
MVA Center.   There is one spatial Oracle SDE table and 13 related attribute tables. They are:  
(i) Project Description, (ii) Site Info, (iii) Failure Type, (iv) Failure Dimensions, (v) Impact 
Assessment, (vi) Adjacent Structures, (vii) Affected Structures, (viii) Materials, (ix) 
Characteristics, (x) Observed Remediation, (xi) Cause of Failure, (xii) Suggested Remediation, 
and (xiii) Remediation Information 
The spatial information is stored with PROJECTID as the primary key. Figure 2.12 shows 
the relationship tree between the multiple Oracle SDE tables stored in the database. 
 
 24 
 
2.4 CONCLUSIONS 
A comprehensive management and assessment system has been developed to allow the SHA to 
better record, evaluate, analyze and review the soil slope failure data and soil slope remediation 
data, provide recommendations and guidelines for design and maintain embankment and cut soil 
slopes. The system consists of three components, each aiding in three different phases of 
highway soil slope management. 
The first phase in monitoring and evaluating highway soil slopes is gathering and evaluation 
of historical data on soil slope failures in the State of Maryland and developing the necessary 
protocols to incorporate that information into a Geographic Information System (GIS) type 
database. The component aiding in this phase is the failure field sheet, which facilitates 
Maryland SHA engineers to collect useful data, by extracting all available soil slope failure and 
remediation data from the SHA project files and by visiting locations of existing soil slope 
failures. 
The collected data will include the type of slope (cut, fill, mechanically-stabilized, etc), types 
of failures, scale of failures, causes of failures, and mitigation methodologies. Information 
regarding these parameters is recognized as the most important data to evaluate failure potential 
as well as performance of slope stability. These records will be collected to analyze the influence 
of factors of slope stabilization and evaluate mitigation performance.  The failure field sheet has 
significantly optimized the daunting task of data collection and ensured consistency in collecting 
high quality information, based on feedback from Maryland SHA engineers. 
The second phase is storing and retrieval of the data collected in the field through a web 
based GIS package. The eGIS slope failure content enables SHA users to view, store, edit and 
create slope failure records through the SHA intranet. The GIS content developed by the HISD 
 
 25 
 
ensures quick and easy access to engineers for data manipulation and analysis. This application 
also enables data sharing and other cooperative ventures at a wide range.  
A database structure containing information relating to soil slope failures or distresses was 
developed using MS Access. This database structure was then organized into a web-based 
relational GIS type database with multiple tables for storing site location information, project 
description, slope characteristics and material information, remediation and maintenance 
information, type of failure, failure mechanisms and failure dimensions using Oracle SDE tables. 
All the information stored in the database and analyzed results can now be visualized by using 
GIS features. 
Currently, information relating to 49 highway slope failure sites is stored in the GIS database. 
Of the 49 highway slope failure cases, information for 18 failure sites were filled in retroactively 
from site photographs, as built plans, geotechnical reports, boring logs and firsthand accounts of 
SHA engineers. The information for the other 30 slope failure sites were recorded using failure 
field sheets on site visits by engineers. 
The third and final phase of the slope stability management is system is providing 
recommendations and guidelines for remediation design and maintenance. Taking into 
consideration the liability and legal issues that might arise due to the limited number of slope 
failure datasets and incomplete information regarding remediation information and cost of 
construction, it was considered premature to perform cost-to-benefit analysis for each type of 
slope failure and remediation methodology. Since the framework for such a system has just been 
developed and with further population of the database, coupled with routine inspection and 
maintenance information, there is definitely a scope for development of a decision based model 
to ascertain the most cost effective and efficient remediation methodology for a particular type of 
 
 26 
 
failure. To respond to this need at present, engineers make use of the remediation response 
categorization sheet which lists a set of parameters considered to influence Maryland SHA?s 
response to highway slope failures as discussed in previous sections. 
   
  
 
 27 
 
TABLES 
Table 2.1: List of landslide causal factors (Source: Popescu, 1994) 
1. Ground Conditions 
     (i) Composition 
         - Plastic material 
         - Collapsible material 
         - Weathered material 
         - Jointed and fissured material 
     (ii) Structure 
         - Mass discontinuities 
         - Structural discontinuities 
     (iii) Stratification 
         - Contrast in permeability and stiffness 
2. Geomorphological processes 
     (i) Erosion - Glacial, fluvial, wave, winds, freezing and thawing 
     (ii) Transitory - Earthquakes, tectonic uplift, Volcanic uplift 
     (iii) Deposition loading 
     (iv) Vegetation removal - erosion, forest fire, drought 
3. Physical processes 
     (i) Intense rainfall 
     (ii) Rapid melt of deep snow 
     (iii) prolonged precipitation 
     (iv) Freezing and thawing cycles 
     (v) Rapid drawdown - floods, high tides, breaching of dams 
4. Man-made processes 
     (i) Construction - Cuts and excavations, Blasting, Drilling, Heavy machinery 
     (ii) Removal of retaining walls or sheet piles 
     (iii) Drawdown (e.g. Lakes, reservoirs, lagoons) 
     (iv) Deforestation 
 
 
 
 
 28 
 
Table 2.2: List of existing SMS at different DOTs (Source: Lowell et al., 2002)  
Organization Number of sites analyzed 
Oregon DOT 3000+ 
Utah DOT 1099 
New York DOT 1700 
New Hampshire DOT 85 
Missouri DOT 300 
Idaho DOT 950 
North Carolina DOT 
1 (20 mile section of 
roadway) 
Washington State DOT 2500 
Kentucky DOT 1800 
Tennessee DOT 1943 
British Columbia Ministry of Transportation 
and Highways (MOTH) 
N/A 
Canadian Pacific Rail N/A 
Ontario MOTH N/A 
Italy 7 
Hong Kong Geotechnical Engineering Office 1400 
Scottish Office Industry Departmentl Roads 
Directorate 
N/A 
 
 
 29 
 
 
Table 2.3: List of Pros and Cons of other unstable slope management programs currently adopted in the USA 
SMS 
Program 
Pros Cons 
ODOT 
+ Strong rating system  - Lacks asset management 
+ Includes asset management  - Does not include soil slopes, fill failures or frozen ground 
OHDOT + Rates rock slope, soil slopes, and embankments  - Complex and lengthy review procedures 
NYSDOT + Includes risk assessment  - Does not include soil slopes, fill failures or frozen ground 
UDOT + Includes risk assessment with  adjustments for geology  - Does not include soil slopes, fill failures or frozen ground 
WSDOT + Good risk and asset manangement program  - Does not include soil slopes, fill failures or frozen ground 
TDOT + Balanced hazard and risk assessment 
 - Does not include soil slopes, fill failures or frozen ground 
 - Lacks asset management 
AKDOT 
+ Rates rock slope, soil slopes, and embankments  - Complex and lengthy data collection procedure 
+ Accounts for frozen soils   
+ Strong rating system   
 
 
 30 
 
FIGURES 
 
 
Figure 2.1: A collage of the different types of slope failures that have occurred in the State of 
Maryland 
 
 
 31 
 
 
Figure 2.2: Types of landslides (USGS Fact Sheet 2004-3072) 
 
 
 32 
 
 
Figure 2.3: Proposed framework for developing a Slope Management System 
 
 
 33 
 
 
Figure 2.4: The relationship tree for the MS Access database 
 
  
 
 34 
 
 
 
 
 
Figure 2.5: eGIS system architecture 
 
 
 35 
 
 
Figure 2.6: Various components of the eGIS content are highlighted 
 
  
 
 36 
 
 
 
 
 
 
 
 
Figure 2.7: Workflow for the slope failure content within the OMT 
 
 37 
 
 
Figure 2.8: A screenshot of the create slope widget. The area highlighted in red indicates the position of the creator widget in the 
screen.
 
 38 
 
 
Figure 2.9: A screenshot of the slope search widget. The area highlighted in red indicates the position of the search widget in the 
screen
 
 39 
 
 
 
 
Figure 2.10: A screenshot of the slope details widget. The area highlighted in red indicates the position of the details widget in the 
screen. 
 
 
 40 
 
 
Figure 2.11: A screenshot of the editor widget the multiple tabs for recording information. Each tab represents an Oracle SDE table.
 
 41 
 
 
Figure 2.12: The Oracle 11g database?s current relationships with PROJECTID as the 
primary key
  
 
42 
 
CHAPTER 3 
3.1 INTRODUCTION 
The surface of the earth is a complex and dynamic system constantly subject to modification 
through physical interactions and processes. Landslides, erosion flows and other soil movements 
along slopes are some of the processes that modify the landscape (Hansen, 1984). Slope 
processes such as these are referred to as mass movements. They involve outward or downward 
movement of soils along slopes under the influence of gravity (Glade et al., 2005).  
All slopes on the surface of the earth may be broadly classified into natural slope and 
engineered slopes (Abramson, et al., 2001) . In every slope, there are stresses that induce 
outward movement(shear stress) and stresses that resist the induce movement (shear strength). If 
these stresses are just balanced or when the shear stress exceeds the shear strength, the slope is 
said to be unstable and prone to failure(Selby, 1993).  
All slope movements are a manifestation of slope instability. It is a well documented fact that 
slope failures can result in extensive property damage and loss of life. In 2004, the National 
Research Council estimated that landslides in the U. S. cause more than $2 billion in property 
damange and claim 20-25 deaths annually. Given the increasing economic and societal cost of 
landslides, there has been an urgent need for improved protection against  landslides (He et al., 
2007). 
Investigation of slope instability and landslide hazard has sparked significant interest 
internationally and is the primary focus of various research initiatives around the world. 
Numerous publications have directed efforts towards discussion of the different scales of 
landlside investigation and slope instability analysis (Brundsen and Prior, 1984; Selby, 1993; 
Popescu, 1994; Cruden and Varnes, 1996; Dikau et al., 1996; Glade and Crozier, 2005).  
 
43 
 
The focus of research over the last decade has shifted from site investigations and stability 
assessments to predictive modelling and consequence analysis (Glade and Crozier, 2005). The 
main aim is to determine the location of future landslide and slope instability events in time and 
space based on spatial and temporal information relating to past events. 
Considerable amount of publications, reports and books discuss in detail the different aspects 
involved in developing a predictive model (Leroi, 1996; Aleotti and Chowdhury, 1999; Chung 
and Fabbri, 1999; Cruden and Fell, 2002; Dai & Lee, 2002; van Westen, 2004). With the current 
trend being towards developing early-warning systems, Geographic Information Systems (GIS) 
have become an important and powerful tool in landslide hazard assessment. 
GIS is at the forefront of all recent landslide hazard assessment research projects and is the 
most recommended platform for landslide and slope instability event prediction (Carrara, et al., 
1999; Sakellariou et al., 2001; Cavallo, et al., 2001; Bhattarai, et al., 2004; Huabin, et al., 2005). 
GIS facilitates application of quantitative techniques in mapping and is capable of performing 
complex statictical and spatial analysis, thus providing a versatile platform for developing 
powerful probabilistic or predictive models (Carrara et al., 1999; Huabin et al., 2005) 
Currently, the Maryland SHA does not have an assessment or predictive model to identify 
vulnerable highway slopes. Such a model, when used in tandem with the other components of 
the SMS, would be able to highlight those highway slopes that are more susceptible or 
vulnerable to movement or failure in comparison to the other slopes along highways. It is the 
intent  of this research project to lay the framework for setting up a robust model to facilitate the 
Maryland SHA to prioritize and optimize their response to slope failures well in advance. 
 
44 
 
3.2 BACKGROUND 
The application of GIS technology in slope instability mapping has a great potential to reduce the 
long term impacts of soil movements caused by surface and sub-surface phenomena (Hansen, 
1984). This loss-reduction is mainly possible because slope failures such as landslides are 
considered to be the most potentially predictable type of geological hazards (Alfors, et al., 1973; 
Leighton, 1976). 
To develop a robust predictive model, it is of paramount importance to understand causative 
parameters triggering slope instability and to establish classifications of failure modes by use of 
discriminatory factors. Many publications discuss initial and recently modified strategies for 
classification of slope movements based on a variety of causative factors (Terzaghi, 1950; 
Varnes, et al., 1978; Popescu, 1994; Dikau et al., 1996) 
Skempton (1950) developed one of the first measures to classify slope movements based on 
geomorphology by portraying correlations between geometrical properties of slopes and their 
mass movement features (Figure 3.1). Developments in field monitoring and site investigation 
methods gave rise to a new set of classifiable factors based on the morphology of the slope 
feature (Brundsen, et al., 1973). 
The most commonly adopted classification for slope movements are those of Varnes (1978) 
and Hutchinson (1988). Later publications produced slightly modified classifications compatible 
with the former publications (Popescu, 1994; Dikau et al., 1996; Cruden and Varnes, 1996). The 
International Geotechnical Societies' UNESCO Working Party on World Landslide Inventory 
reported that the Varnes? (1978) classification is the most widely used system (WP/WLI report, 
1990). Table 3.1 shows the abbreviated classification system proposed by Varnes (1978).  
 
45 
 
The focus of research over the last decade has shifted from site investigations, mechanism 
classifications and stability assessments to predictive modelling and consequence analysis (Glade 
and Crozier, 2005). The main aim is to map and locate future landslide and slope instability 
events in time and space based on spatial and temporal information relating to past events. 
Varnes (1984) was also one of the early advocates for this integrated approach in landslide 
research and engineering practice. 
Based on the literature reviewed on the principles, concepts, techniques and methodology for 
slope instability evaluation (Varnes, 1984; van Westen, 1993; Navarro and Garcia., 1996; 
Chung, et al., 1999; Carrara, et al., 1999; Guzzetti, et al., 1999; Cavallo, et al., 2001; Cruden, et 
al., 2002; Clerici et al., 2002; Cardinali, et al., 2002;  Huabin, et al., 2005; Glade, et al., 2005) all 
slope instability mapping techniques can be broadly classified into qualitative and quantitative 
analysis.  
Qualitative analysis involve techniques such as geomorphological mapping, Landslide 
inventory mapping, hueristic analysis and qualitative index overlay. Quantitative analysis can 
further be classified into statistical techniques and physical or geotechnical models. Figure 3.2 
shows the detailed classification tree of the various slope instability mapping techniques. 
Geomorphological mapping relies on information about the surface topography and relief 
features of the site in question. It is the easiest method for mapping instability and was widely 
used between 1970-80 (Fenti et al., 1979; Kienholz, 1978; Rupke et al., 1988). Landslide 
inventory mapping systems use information available relating to slope failure events that have 
occurred on the slope in the past to develop an inventory. However, they only emphasize on 
slope with failure histories (He and Beighley, 2007). Heuristic or index based analysis makes 
 
46 
 
uses a combination of expert opinion and past experience to analyze slopes (Anbalagan and 
Singh, 1996; Gupta and Anbalagan, 1997; Wachal and Hudak, 2000; Morton et al., 2003).  
Qualitative index overlay or factor mapping is commonly used in the initial stage of regional 
assesment (Crozier and Glade, 2004). It involves identifying spatial distribution of one or more 
causative factors or a combination of the causative factors and investigating their influence on 
slope stability. Weights are assigned to different factors based on the magnitude of influence on 
slope stability. Crozier (1989), Turner and Schuster (1996) and Guzzetti et al. (1999) studied the 
effect of a variety of parameters on slope instability. They provide a comprehensive list of 
causative factors influencing slope stability.  
Statistical analysis makes use of regression functions and distribution curves to predict slope 
failure based on data collected from site or from the lab. Correlation between different physical 
factors and previous slope failures are mapped using discriminant analysis. Then, quantitative or 
semi-quantitative estimates are made for those slopes without failure histories (Dai and Lee, 
2002). Statistical methods are more appropriate for slope instability mapping as they eliminate 
the subjective bias present in qualitative analysis (Fall et al., 2006).  
Physical or geotechnical models are based on 1D, 2D or 3D factor of safety analysis 
assuming infinite slopes. These models despite being precise in predicting vulnerable slopes 
(Wu, et al., 2000; Sakellariou, et al., 2001; Bhattarai, et al., 2004; Singh et al., 2008)  require the 
landforms to have uniform ground conditions. Also due to diversity in distribution of values over 
a particular region, data collection and sampling may not be logistically feasible for regional 
scale study. 
Limited studies exist (Carrara et al., 1992; 1995; van Westen, 1997; Chung, et al., 2004; 
Huabin, et al., 2005) that systematically compares these different techniques, in terms of 
 
47 
 
respective strengths and limitations. But it is evident that each technique has limitations with 
regard to scale of study and data availability (Aleotti and Chowdhury, 1999). It is vital to choose 
the appropriate scale of study for analysis different work scale affects the selection of the 
approach. Table 3.2 shows a list of advantages and disadvantages of the various mapping 
techniques and the recommended scale of study for each technique. 
The scale of assesment adopted for this study lies in the regional scale and the method of 
assessment used is a semi-qualitative index overlay. The primary reason for choosing a semi- 
qualitative technique for slope instability mapping is due to the insufficient data relating to 
historic slope failures. With the limited information regarding past events and causative factors, 
it is not feasible to develop a robust multivariate analysis model at a regional scale. Also, the 
qualitative index overlay as discussed before can be applied succesfully at all levels of study. 
 
3.3 DESCRIPTION OF STUDY AREA 
The State of Maryland is located in the Mid-Atlantic region of the United States. It is the 9th 
smallest state by area, but the 19th most populous and the 5th most densely populated of the 50 
United States (US Census bureau, 2011). The total study region covers an area of  27,076 sq. km. 
The mean elevation of the State of Maryland is 350 feet above sea level, ranging from 3,360 feet 
to mean sea level at the Atlantic ocean.  
The state is divided into 5 distinct physiographic provinces namely: Appalachian plateaus 
province, Ridge and valley province, Blue ridge province, Piedmont plateau province and the 
Atlantic coastal plains province.  
The Coastal Plain Province is underlain by a wedge of unconsolidated sediments including 
gravel, sand, silt, and clay, which overlaps the rocks of the eastern Piedmont along an irregular 
 
48 
 
line of contact known as the Fall Zone.  Eastward, this wedge of sediments thickens to more than 
8,000 feet at the Atlantic coastline.  Beyond this line is the Atlantic Continental Shelf Province, 
the submerged continuation of the Coastal Plain, which extends eastward for at least another 75 
miles where the sediments attain a maximum thickness of about 40,000 feet (Edwards Jr., 1981). 
The Piedmont Plateau Province is composed of hard, crystalline igneous and metamorphic 
rocks and extends from the inner edge of the Coastal Plain westward to Catoctin Mountain, the 
eastern boundary of the Blue Ridge Province.  Bedrock in the eastern part of the Piedmont 
consists of schist, gneiss, gabbro, and other highly metamorphosed sedimentary and igneous 
rocks of probable volcanic origin.  In several places these rocks have been intruded by granitic 
plutons and pegmatites. Deep drilling has revealed that similar metamorphic and igneous rocks 
underlie the sedimentary rocks of the Coastal Plain (Edwards Jr., 1981). 
Unlike the Coastal Plain and Piedmont Plateau Provinces, the Blue Ridge, Ridge and Valley, 
and Appalachian Plateaus Provinces are underlain mainly by folded and faulted sedimentary 
rocks.  The rocks of the Blue Ridge Province in western Frederick County are exposed in a large 
anticlinal fold whose limbs are represented by Catoctin Mountain and South Mountain.  These 
two ridges are formed by Lower Cambrian quartzite, a rock that is very resistant to the attack of 
weathering and erosion. A broad valley floored by Precambrian gneiss and volcanic rock lies in 
the core of the anticline between the two ridges. Figure 3.3 shows the generalized geological 
map for the state of Maryland (Edwards Jr., 1981). 
Despite its small size, Maryland exhibits considerable climatic diversity. Temperatures vary 
from an annual average of 48?F in the extreme western uplands to 59?F in the southeast, where 
the climate is moderated by the Chesapeake Bay and the Atlantic Ocean. Monthly average 
temperatures range from a high of 87.1?F to a low of 24.3?F. 
 
49 
 
The average annual precipitation values for the eastern half of the State of Maryland ranges 
from 42 inches to 52 inches per year. Precipitation averages about 49 inches annually in the 
southeast, but only 36 inches in the west. Higher values of average annual precipitation are 
observed in the western most tip of the study region.   
3.4 DATA SOURCES 
The study area was examined in detail using the ArcMap GIS software. The input map layers 
were imported into ArcMap in their original format for verifying data compatibility and integrity. 
A major issue with the study was procuring relevant data layers for the various physical 
parameters at appropriate resolutions. A wide array of physical parameters were considered as 
causative factors in this study based on literature(Turner and Schuster, 1996; Guzzetti et al., 
1999). Due to non-uniformity in the quality of data and the level of resolution only a handful of 
parameters were shortlisted. 
A variety of factors that influence slope stability directly or indirectly were considered, and 
based on data availability and literature (Turner and Schuster, 1996; Guzzetti et al., 1999; Chau 
et al., 2004; He and Beighley, 2007; Singh et al., 2008;  Bhattarai et al., 2004) the following 
factors were considered in the study: (i) Elevation, (ii) Slope angle, (iii) Land cover, (iv) Storm 
event precipitation, (v) Slope history or failure inventory, and (vi) Surface geology. Table 3.3 
provides details regarding the source of the data layers used in this study. 
The elevation dataset is obtained from the National Elevation Dataset(NED) 1/3 Arc-Second 
coverage in raster format. The dataset has a resolution of 10 x 10 m and was downloaded from 
the USGS website. The slope angle dataset was derived from this layer using spatial analysis 
tools available in the ArcMap software. The derived slope angle data layer was also resampled to 
a resolution of 10 x 10 m. 
 
50 
 
The land cover datalayer was obtained from the National Land Cover Database (NLCD) 
2006 edition at 30 m resolution. The NLCD dataset was reclassified into six different values: 
grass, shrubs, woodland, cultivated land, developed land and other. This was performed to make 
the datalayer compatible with the land cover classification adopted by the GIS database 
discussed in the previous chapter. 
 The precipitation data was obtained from the The NOAA Atlas 14, Volume 2 (Ohio 
River Basin and Surrounding States) dataset. The NOAA Atlas 14 precipitation provides 
frequency estimates, with upper and lower bounds of the 90% confidence interval, in grid format 
and are resampled at 30 m resolution at the time of data extent specification.  Data are available 
for the 1, 2, 5, 10, 25, 50, 100, 200, and 500 year storm events and for 6, 12, 24, and 48 hour 
durations. For this study, the estimates for a 2 year 24 hour duration storm event and a 100 year 
24 hour duration storm event were chosen. 
The slope history or failure inventory data was derived from the GIS database discussed in 
the previous chapters. The failure location information table of the GIS database was exported to 
a .xls file and then imported into the ArcMap software. Since the tables were populated with 
GPS coordinates of the failure sites, it was easy to project and create the slope failure inventory 
layer as a shapefile. 
The surface geology dataset consists of 2 layers. The first layer depicts the extents of the 
different physiographic provinces in the State of Maryland. This shapefile was obtained from the 
Maryland Geological Survey. The second layer is the geological map of the State of Maryland 
which is obtained from the USGS mineral resources spatial database. This layer provides details 
regarding the superficial and bedrock geology of the State of Maryland. Both datasets are in 
vector format in 1 : 250,000 scale. Figures C.1 to C.6 shows all the data layers used in the study. 
 
51 
 
3.5 PHYSICAL PARAMETERS 
Different physical parameters or factors that influence slope stability have been used in different 
methods of analysis while mapping slope instability (Sakellariou, et al., 2001; Cavallo, et al., 
2001; Chau, et al., 2004; Bhattarai, et al., 2004; Saboya Jr., et al., 2006; He, et al., 2007; Singh, 
et al., 2008).  
These factors can be broadly classified into intrinsic factors and extrinsic factors (Huabin et 
al., 2005). Intrinsic factors include geology, topography, lithology, surface characteristics and 
slope structure and characteristics (slope angle, soil type, vegetation etc). Extrinsic factors 
include seismic events, storms and human activities like mining, blasting, drilling and other 
construction activities. 
During the initial stages of this study, the following factors were considered for correlation 
and feasibility studies: Elevation, slope angle, slope structure: convex or concave, precipitation, 
storm event, seismic vibrations, human activities, geological formations, fault lines, land cover, 
land usage, proximity to water bodies/drainage lines, slope history/ landslide history and type of 
drainage facilities. 
Due to issues such as lack of availability of data at required scale, diversity in factor values 
over large regions, logistical hindrance in data collection through site investigation (regional 
scale) and quality of data many of these factors had to be disregarded for this study. 
After performing feasibility studies based on expert opinions and recommendations from the 
engineers at the OMT, Maryland SHA, the following physical parameters were shortlisted: (i) 
Elevation, (ii) Slope angle, (iii) Land cover, (iv) Storm event precipitation, (v) Slope history or 
failure inventory, and (vi) Physiographic provinces. 
 
52 
 
The following section will discuss in detail the correlation between each of these factors with 
slope instability. 
3.6 DATA ANALYSIS 
Using the SMS tools- the failure field sheet and the GIS database, a total of 48 slope failure cases 
occurring between 2008 and 2012 were recorded actively and retroactively by engineers at the 
Maryland SHA. Based on the comprehensive information for the 48 slope failures and using 
spatial analysis tools available in ArcMap ver. 10 software, certain trends in failure distribution 
in relation to the selected parameters were established. The trends and data analysis is presented 
in this section. 
3.6.1 Elevation and slope angle 
Elevation and slope angle are the two most widely chosen parameters considered to influence 
slope stability while mapping regions vulnerable to failure (Chau, et al., 2004; He, et al., 2007; 
Saboya Jr., et al., 2006; Sakellariou, et al., 2001; Singh, et al., 2008). Skempton (1953) and 
Brundsen (1973) developed and modified, respectively, the relationship between slope angle and 
slope height in terms of potential failure mechanisms.  
In this study, elevation as a separate parameter does not exhibit a strong correlation with 
slope instability. As shown in Figure 3.4a, 56% of the total number of slope failures has occurred 
on slopes between 30 m and 90 m in height and nearly a fourth of the total number of failures 
have occurred on slope with heights between 10m and 30 m. No clear trend or correlation could 
be observed at present between slope height and soil slope failures in the State of Maryland. 
Figure 3.4b shows the failure distribution for the slope angle sub categories. It is clearly 
evident that a majority (more than 50%) of failures has occurred on slopes with slope angles 
 
53 
 
between 20? and 30? as these failures have occurred along highway slopes. For all engineering 
and analyses purposes, the Maryland SHA assumes that all or most highway slopes have a 2H: 
1V slope unless explicitly mentioned.  
The failure distribution pattern for elevation and slope angle correlates with the field 
conditions observed by engineers, because the Maryland SHA records only those slope failures 
that are within their right of way. Since a distinct pattern or correlation with slope instability is 
yet to be drawn with respect to these parameters, it can be concluded that these numbers when 
combined with failure distribution patterns for other parameters will yield a more conclusive 
result. 
3.6.2 Land cover 
Land cover also influences slope behavior (Varnes and IAEG, 1984). A study by Lee and Choi 
(2004) in southern California found the probability of landslide occurrence to be highest for 
grass lands and certain forest types. It must be noted that their findings may be a result of co-
 existing landscape characteristics. For example, they show a high probability of landslide 
occurrence for vegetation types found in steep, mountainous areas. 
In this study, 53% of the total number of failures has occurred on slopes predominantly 
covered with grass (Figure 3.5). Cross referencing this information with the vegetation density 
information recorded on site, it is evident that many failures have occurred on slopes with 
medium to low density of grass vegetation. This trend highlights the importance of type of 
vegetation cover on highway slopes as an important factor of influence in slope vulnerability 
studies in the State of Maryland. 
52% of the remaining slope failures have occurred on developed land or urbanized regions. 
This trend presents an interesting insight into the effect of urbanization and land use pattern on 
 
54 
 
slope instability. This relatively large percentage occurrence of failures on developed land can be 
attributed to the increased amount of human activity such as blasting, drilling, traffic volume and 
other construction activities. 
3.6.3 Storm event precipitation 
Precipitation is a fundamental slope instability factor. Hong Kong?s densely populated urban 
areas suffered 185 slips as a result of heavy rains in 1972 (Chau, et al., 2004). Countries like 
Japan, Malaysia and Nepal are also prone to slope movements triggered by heavy rains or storm 
events (Schuster, 1995; Singh et al., 2008; Bhattarai, et al., 2004).  
In the USA heavy rains during winter have caused significant amounts of social and 
economic losses (Beighley et al., 2003; NOAA, 2001). Generally, areas receiving higher rainfall 
relative to the region have a higher probability of landslide occurrence (He and Beighley, 2007). 
It is evident from past events across the globe, that failure is more likely to occur in areas 
with high estimation of precipitation values. In this study, the estimates for a 2 year 24 hour 
duration storm event and a 100 year 24 hour duration storm event were chosen. 
Figure 3.6a shows the failure distribution pattern for the 2 year 24 hour storm event. 87% of 
the total number of slope failures have occurred in regions estimated to have 50 mm to 60 mm of 
precipitation. Figure 3.6b shows the failure distribution pattern for the 100 year 24 hour storm 
event estimates. A similar trend is observed here again, more than 80% of the total slope failures 
have occurred in regions expected to have heavy amount of rainfall during a storm event. 
3.6.4 Slope failure inventory 
Slope instability classification systems are usually based on a combination of material and 
movement mechanism (Dai and Lee, 2002). For this study, the classification system proposed by 
 
55 
 
Cruden and Varnes (1996) was slightly modified to reflect the failure conditions prevalent 
locally in the State of Maryland (Figure 3.7).  
Figure 3.8 shows the distribution pattern for the different types of failure as per the 
classification shown in Figure 3.7. 90% of the total numbers of slope failures are surficial 
erosion failures. Cross referencing with the GIS database, 80% of the total number of slope 
failures have occurred during or after rainfall. Figure 3.9a shows the distribution pattern for the 
different types of slopes in the State of Maryland. This trend when compared with the failure 
distribution pattern for the type of drainage section at failure site (Figure 3.9b) shows the 
influence of precipitation and drainage conditions on slope instability. 
3.6.5 Physiographic provinces and lithology 
It may be reasonably expected that the properties of the slope-forming materials, such as strength 
and permeability that are involved in the failure, are related to the lithology, which therefore 
should affect the likelihood of failure (Dai and Lee, 2002). The statistic that 87% of the total 
number of slope failures recorded has occurred in the Atlantic coastal plains province highlights 
the effect of lithology or soil type of the highway slopes (Figure 3.10a).  
The Atlantic coastal plains province predominantly consists of slopes with silty or clayey 
sand, gravelly sand, coarse sand and gravel type soils. 50% of the total number of slope failures 
lay on slopes with sand formations and 39% of these slope failures occur on slopes with gravel 
formations (Figure 3.10b). 
 
 
56 
 
3.7 SLOPE INSTABILITY MAPPING 
3.7.1 Logistic regression 
Logistic multiple regression is a technique that considers several physical parameters that may 
affect probability. The advantage of logistic multiple regression modeling over other multivariate 
statistical techniques including multiple regression analysis and discriminant analysis is that the 
dependent variable can have only two values?an event occurring or not occurring, and that 
predicted values can be interpreted as probability since they are constrained to fall in the interval 
between 0 and 1 (Dai and Lee, 2002). 
The technique of logistic multiple regression yields coefficients for each variable based 
on data derived from samples taken across a study area. These coefficients serve as weights in an 
algorithm which can be used in the GIS database to produce a map depicting the probability of 
landslide occurrence (Dai and Lee, 2002). 
The relationship between the probability of occurrence of an event P, and its dependency 
on variables influencing the event can be represented by Equation 3.1. 
? =  
1
 (1 + ???)
  
                                                                                                                                                     
(Eq 3.1) 
 
P is the estimated probability of landslide occurrence. As Z varies from -1 to +1, the probability 
varies from 0 to 1 on an S-shaped curve. Z is the linear regression equation as represented in 
Equation 3.2.  
? = ?0 + ?1?1 +?+ ????   
                                                                                                                                              
(Eq 3.2) 
 
57 
 
 
where Wi ( i = 1,2,?,N) are the coefficients estimated through regression and Xi ( i = 1,2,?,N) 
are the independent variables. 
Dai and Lee (2002) used this technique to predict slope instability in the Lantau Island, Hong 
Kong. They also studied the runoff potential and behavior of landslide masses in this study. 
Mark and Ellen (1995) described the use of logistic regression on a database of thousands of 
debris flows and had shortlisted five physical attributes. They used the distribution and frequency 
of Shallow landslides to model future initiation sites, estimate runoff volumes and run out 
distances and compared these results with existing landslides. More recently Gorsevski, et al., 
(2000) applied logistic regression for spatial prediction of landslide hazard in Alberta, Canada. 
While this method of analysis is highly recommended for this scale of study and is most 
compatible with the format in which data is recorded, due to inadequate sample size of slope 
failures, the application of this method for this study will not be feasible. For this model to be 
used the sample size of the number of failure cases needs to be exponentially larger. The time 
required for the GIS database to acquire the appropriate volume of data would render the 
application of such a statistical model, out of the scope of the present study. 
3.7.2 Qualitative index overlay 
The qualitative index overlay method may be successfully used at all scales of study. Qualitative 
index overlay or factor mapping is commonly used in the initial stage of regional assessment 
(Glade and Crozier, 2005). Since the primary objective of this study is to the lay the framework 
for developing a robust slope instability model for the State of Maryland, qualitative index 
overlay would be the most suitable method of analysis for the volume of data collected for this 
study. 
 
58 
 
The general concept behind such an analysis is to characterize both spatial and temporal 
conditions that have determined the occurrence of past instability events and to use these 
characteristics to highlight those slopes with similar conditions that are vulnerable to failure.  
Chau et al. (2004) discuss the principle behind a weighted overlay of index or thematic maps 
using ArcGIS software: A denotes the whole study area of the instability map and there are m 
layers of thematic spatial data (elevation, slope angle, lithology, and precipitation etc.) 
containing causal factors- ci. A pixel p in A would have m pixel values, c1?cm. The model can be 
programmed to calculate the occurrence of failure in p in terms of conditional probabilities 
(Clerici et al., 2002) based on pixel values of the causal factors. Figure 3.11 shows schematic of 
the principle in discussion.  
However, in a strict sense, the final pixel value of the instability map produced in this study 
does not represent probability since the dynamic variables triggering landslides, such as rainfall, 
are not accounted for. Hence, it may be more appropriate to refer to these values as failure 
density. 
The values of all the physical parameters are classified in to sub classes or categories as 
shown in Table 3.4. A failure density index is assigned for each sub- class. The purpose of 
assigning such an index to each sub class is to identify the unstable slopes along regions with no 
previous slope failure occurrence. The class intervals are decided using statistical tools available 
in the ArcMap software. 
Equation 3.3 outlines the methodology used to calculate the failure density index for each 
subcategory as shown in Table 3.4. A normalized density index is calculated for a more 
conservative approach. For a particular factor, the density index for each subcategory is 
normalized by the maximum density index value for that factor. Figure 3.12 shows the variation 
 
59 
 
of both the failure density index and the normalized failure density index for the different 
subclasses of parameters. This figure shows how the conservative index provides for more 
striking variation in failure density values for the same sample set of data when compared to the 
failure density index.  
   Figure 2.13 illustrates the variation of failure density indices of parameter subclasses over the 
area of the study region. The low sample size of slope failures for this study gives rise to 
insignificant failure density values for some parameter subclasses as shown in Figure 2.13. A 
conservative index allows for a more well distributed model by rating up slopes that have low 
failure density values due lack of field data, but might have potential to fail based on spatial and 
temporal conditions. Table 3.4 shows the density index values and the normalized density index 
values for each subcategory. 
 
      ?  ?         ?  ( ) =  (
     ?         ?       ?              
          ?         ?       ? 
) 
                                                                                                                                              (Eq 3.3) 
   A weighted mean of the normalized failure density index of the various factors gives the failure 
density value at any particular pixel (refer Equation 3.4). The weights were assigned based on 
expert opinion and the trends observed between the failure density index and the causative 
parameter. A weightage of 3 is applied to parameters exhibiting a clear trend between parameter 
data and the failure density index, while the weightage of 2 or 1 is provided to other parameters 
based on expert opinion. 4 trials were conducted and 4 failure density maps were generated. 
Table 3.5 gives the different weights assigned to the different factors.  
 
      ?  ?     =  (
 ? ?? .  ?
 ?
 ?=1
 ? ??
 ?
 ?=1
 ) 
                                                                                                                                        (Eq 3.4) 
 
60 
 
Figure 3.14 shows the results of the 4 weighted overlay maps using the raster calculator function 
in ArcMap software. 
3.8 CONCLUSIONS 
A framework for analyzing slope instability is proposed and developed for the State of Maryland. 
A total of 48 slope failures recorded by Maryland SHA engineers using the GIS database were 
analyzed for emerging trends and patterns correlating physical parameters with slope instability.  
In this study, six factors were considered to affect highway soil slope stability: event 
precipitation, geological formation, land cover, slope history, ground slope and elevation. 
Overlaying statewide GIS data for these factors brings some interesting trends to light: 
precipitation and poor surface or sub-surface drainage conditions are principal factors causing 
slope failures. 96% of the failed slopes lie along roads with open drainage section. Majority of 
the failed slopes lie in regions with relatively high event precipitation values. 90% of the existing 
failures are surficial erosion type failures, only 4% of the slope failures are deep rotational type 
failures. Cross referencing this with the GIS database, it is found that 80% of the total number of 
slope failures has occurred during or after rainfall. 58% of the existing slope failures have 
occurred in regions having low density land cover. 50% of the failures have occurred in sand and 
39% have occurred in gravel formations. 
Distinct trends and patterns were recognized for some physical features such as Lithology, 
Physiographic provinces, precipitation and land cover. These physical parameters presently 
influence highway slope stability to a greater extent in relation to physical parameters such as 
elevation and slope angle. The influx of more data relating to failed slopes should give rise to 
more trends, and thus this system will aid the State Highway Administration (SHA) of Maryland 
in prudential budget allocation and prioritizing different remediation projects. 
 
61 
 
It is the intent of this study to lay the groundwork for a robust quantitative mapping system. 
In the initial stage, the mapping technique used is a weighted overlay of thematic maps. An ideal 
and suitable multivariate statistical approach was reviewed and presented.  
 
62 
 
TABLES 
Table 3.1: Classification of soil movements by Varnes (1978) 
TYPE OF MOVEMENT 
TYPE OF MATERIAL 
BEDROCK 
ENGINEERING SOILS 
Predominantly coarse Predominantly fine 
FALLS Rock fall Debris fall Earth fall 
TOPPLES Rock topple Debris topple Earth topple 
SLIDES 
ROTATIONAL 
Rock slide Debris slide Earth slide 
TRANSLATIONAL 
LATERAL SPREADS Rock spread Debris spread Earth spread 
FLOWS 
Rock flow 
(Deep creep) 
Debris flow Earth flow 
(Soil creep) 
COMPLEX Combination of two or more principal types of movements 
 
 
63 
 
Table 3.2: Various mapping techniques- their scale of use, advantages and disadvantages  
Classification of 
Mapping technique 
Mapping 
Technique 
Scale of use recommended 
Advantages Disadvantages 
Regional Medium 
Large to 
small 
Qualitative- Heuristic 
analysis 
Qualitative map 
combination 
Yes Yes No 
-Can be used for evaluating 
large areas of land.  
-Subjectivity involved in 
assigning weights to various 
layers 
Statistical Analysis 
Multivariate 
statistical analysis 
Yes Yes 
Restricted 
use 
-Eliminates subjectivity 
involved in assigning weights 
to factors. 
   
-Correlates influence of 
parameters with slope 
instability 
-Large efforts to collect and 
validate data.  
Artificial Neural 
Netorks 
No Yes Yes 
-Can deal with qualitative and 
quantitative input.  
 
-Adaptive and can deal with 
incomplete data 
-Initial weights are random.  
 
-Subjectivity involved in 
selection of factors 
Physical or Mechanistic 
Analysis 
Factor of Safety 
Analysis 
No No Yes 
-Deals with real time data. 
  
-Accounts for intrinsic and 
extrinsic stresses in a direct 
manner 
-Laborious data collection 
process.  
 
-Impossible to have accurate 
data due to spatial variablity of 
parameter values 
 
 
 
64 
 
 
Table 3.3: List of parameters considered and their data sources 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Parameters considered Data source 
Elevation 
National Elevation Dataset (NED) 1/3 Arc Second (~10m 
resolution). Primary elevation data product of the USGS. 
(http://seamless.usgs.gov/) 
Slope angle 
~10 m resolution. Derived from the NED 1/3 Arc Second 
datalayer using spatial analyst tools in ArcMap ver. 10 
Land cover 
National Land Cover Database (NLCD) 2006 edition 
from the USGS seamless data warehouse. (~30 m 
resolution) 
Storm event 
precipitation 
Data for 2 year and 100 year recurrence intervals for a 24 
hour storm duration obtained from the NOAA Atlas 14, 
Volume 2 
(http://hdsc.nws.noaa.gov/hdsc/pfds/pfds_gis.html) 
Physiographic 
provinces 
Shapefile obtained from the Maryland Geological Survey 
website (www.mgs.md.gov/coastal/maps/g1.html) 
Failure inventory 
Based on comprehensive data collected using the failure 
field sheet and stored in the MS Access database 
 
65 
 
 
 
Table 3.4: Physical parameters classified into sub-categories along with the density and normalized indices for each sub categories 
 
  
Factor  Class  Area ratio  Failure density index  Normalized index  
Slope angle (degrees)  < 10  89.4 0.2083 0.4545 
10 - 20  8.7 0.3125 0.6818 
20 - 30  1.6 0.4583 1.0000 
30 - 40  0.3 0.0208 0.0455 
> 40  0.0 0.0000 0.0000 
Land cover (based on NLCD classification)  Grass  13.9 0.5625 1.0000 
Shrubs  1.6 0.0417 0.0741 
Woodland  31.8 0.0625 0.1111 
Developed Land  2.5 0.2292 0.4074 
Cultivated Land  30.4 0.0208 0.0370 
Other: Wetlands, Barren  19.7 0.0833 0.1481 
Elevation (meters)  < 10  27.7 0.1250 0.2222 
10 - 30  18.9 0.2292 0.4074 
30 - 90  14.2 0.5625 1.0000 
90 - 270  28.2 0.0833 0.1481 
> 270 9.4 0.0000 0.0000 
Physiographic province (Maryland Geological Survey)  Appalachian Plateaus Province  7.4 0.0000 0.0000 
Ridge and Valley Province  6.7 0.0000 0.0000 
Piedmont Plateau Province  26.3 0.1667 0.2000 
Blue Ridge Province  2.9 0.0000 0.0000 
Atlantic Coastal Plain Province  56.6 0.8333 1.0000 
Storm event precipitation - 2 year recurrence, 6 hrs. duration (mm)  < 56  26  0.0000 0.0000 
56 - 58  17  0.6250 1.0000 
58 - 60  27  0.2500 0.4000 
60 - 62  17  0.0000 0.0000 
> 62  13  0.1250 0.2000 
Storm event precipitation - 100 year recurrence, 6 hrs. duration (mm)  < 135  30  0.0000 0.0000 
135 - 140  34  0.7292 1.0000 
140 - 145  13  0.1458 0.2000 
145 - 150  19  0.1250 0.1714 
> 150  5  0.0000 0.0000 
 
66 
 
 
 
 
Table 3.5: The weightage scheme assumed for the different test maps 
Factor Map 1 Map  2 Map  3 Map  4 
Slope angle 1 3 1 3 
Land cover 1 3 1 3 
Elevation 1 1 1 1 
Physiographic provinces 1 2 1 2 
Storm event precipitation - 2yr recurrence 24 hr duration 1 3 0 0 
Storm event precipitation - 100yr recurrence 24 hr duration 0 0 1 3 
Slope failure history 1 2 1 2 
 
67 
 
FIGURES 
 
 
Figure 3.1: Schematic showing the relationship between slope angle and 
slope height (Skempton, 1953) modified by Brundsen (1973) 
 
68 
 
 
Figure 3.2: A broad classification of all the slope instability mapping techniques developed from Huabin et al., 2005
 
69 
 
 
Figure 3.3: A generalized geological map of the State of Maryland (Source: Maryland Geological Survey, www.mgs.md.gov/)
 
70 
 
 
 
 
 
 
 
 
Figure 3.4: Distribution of failures in the different sub categories for (a) Elevation and (b) Slope angle
 6 
11 
27 
4 
0 
0
 5
 10
 15
 20
 25
 30
 < 10 10 - 30 30 - 90 90 - 270 > 270
 N
 o
 . 
o
 f 
sl
 o
 p
 e 
fa
 il
 u
 re
 s 
Elevation (meters) 
Slope failure distribution - Elevation 
10 
15 
22 
1 
0 
0
 5
 10
 15
 20
 25
 < 10 10 - 20 20 - 30 30 - 40 > 40
 N
 o
 . 
o
 f 
sl
 o
 p
 e 
fa
 il
 u
 re
 s 
Slope angle (degrees) 
Slope failure distribution - Ground slope angle 
(a) 
(b) 
 
71 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.5: The distribution of slope failures for the different classes of land covers in the State of Maryland
 56% 
4% 
6% 
23% 
2% 
9% 
Slope failure Distribution - Land cover (NLCD classification modified)  
Grass
 Shrubs
 Woodland
 Developed Land
 Cultivated Land
 Other: Water, Wetlands,
 Barren
 
72 
 
 
 
 
 
 
 
 
Figure 3.6: The failure distribution pattern for the different sub categories of (a) 2 year 24 hour duration 
storm event and (b) 100 year 24 hour duration storm event 
0 0 0 0 0 
42 
6 
0
 10
 20
 30
 40
 50
 0 - 60 60 - 65 65 - 70 70 - 75 75 - 80 80 - 85 85 - 90
 N
 o
 . 
o
 f 
sl
 o
 p
 e 
fa
 il
 u
 re
 s 
Precipitation estimates (mm) 
Failure distribution - Storm event precipitation 
(2 year 24 hour duration) 
0 0 0 0 0 
32 
11 
5 
0
 5
 10
 15
 20
 25
 30
 35
 < 145 145 -
 160
 160 -
 175
 175 -
 190
 190 -
 205
 205 -
 220
 220 -
 235
 > 235
 N
 o
 . 
o
 f 
sl
 o
 p
 e 
fa
 il
 u
 re
 s 
Precipitation estimates (mm) 
Failure distribution - Storm event precipitation  
(100 year 24 hour duration) 
(a) 
(b) 
 
73 
 
 
 
 
 
 
 
 
 
 
 
 
 
Failure type Failure type sub - classification 
  Erosion  Erosion Area  Head                             Toe Flank Body 
  Rotational failure 
Circular  Deep Shallow 
Non-circular  Deep Shallow 
  Translational failure   Block   Slide 
  Others Landslide   Flow Spread 
  Compound / Complex (provide sketch below) 
 
Figure 3.7: Proposed failure type classification used by Maryland SHA based on Cruden and Varnes (1996)
 
74 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.8: Failure distribution pattern for the different types of slope failures as per the modified Cruden 
and Varnes (1996) classification
 79% 
9% 
2% 
4% 
4% 2% 
Slope Failure Distribution- Failure type 
Erosion- Body
 Erosion- Head
 Erosion- Body & Head
 Translational- Slide
 Deep Circular Rotational
 Compound/Complex
 
75 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.9: Slope failure distribution patterns for different (a) Slope types and (b) Slope drainage section 
types 
 
4% 
48% 38% 
10% 
Slope Failure Distribution - Slope type 
Cut
 Cut and Fill
 Fill
 Natural
 96% 
4% 
Slope Failure Distribution- Drainage Section type 
Open Section
 Closed Section
 (a) 
(b) 
 
76 
 
 
 
 
 
 
 
 
 
 
Figure 3.10: The slope failure distribution pattern for (a) the different physiographic provinces and (b) for 
the different lithology or soil type.
 17% 
83% 
Slope failure distribution - Physiographic provinces 
Appalachian Plateaus Province
 Ridge and Valley Province
 Piedmont Plateau Province
 Blue Ridge Province
 Atlantic Coastal Plain Province
 44% 
11% 4% 
35% 
4% 
2% 
Failure distribution - Lithology and soil type 
Gravel- sand
 Sand- gravel
 Gneiss- schist
 Sand- clay or mud
 Quartz feldspar schist-
 granulite
 Gabbro- norite
 (a) 
(b) 
 
77 
 
 
 
 
 
 
Figure 3.11: Schematic explaining the proposed mapping system. Currently the system uses the qualitative overlay model with a raster output.  
 
78 
 
 
 
 
Figure 3.12: Variation of the failure density index and normalized failure density for the subclasses of 
parameters (a) Slope angle and (b) Storm event precipitation (100 yr, 24 hr)
 0
 0.2
 0.4
 0.6
 0.8
 1
 1.2
 0 10 20 30 40 50 60 70
 F
 a
 il
 u
 re
  d
 en
 si
 ty
  
Slope angle (degrees) 
Failure density index
 normalized failure density
 index
 0
 0.2
 0.4
 0.6
 0.8
 1
 1.2
 135 155 175 195 215 235
 F
 a
 il
 u
 re
  d
 en
 si
 ty
  
Precipitation estimate (mm) 
Failure density index
 Normalized failure density
 
79 
 
 
 
Figure 3.13: Variation of failure density indices for the different parameter subclasses over area of the study region
 0
 0.1
 0.2
 0.3
 0.4
 0.5
 0.6
 0.7
 0.8
 0.9
 1
 0 4000 8000 12000 16000 20000 24000 28000
 F
 a
 il
 u
 re
  D
 en
 si
 ty
  
Area (sq. km.) 
ELEVATION
 SLOPE ANGLE
 LAND COVER
 PHYSIOGRAPHIC PROVINCES
 PRECIPITATION(MM)- 2 YEAR
 EVENT 24 HOUR DURATION
 PRECIPITATION(MM)- 100 YEAR
 EVENT 24 HOUR DURATION
 Very high density 
High density 
Moderate density 
Low density 
Very low density 
 
80 
 
 
 
 
Figure 3.14: Failure density maps generated by layers and weights provided in Table 3.5. (a) Follows weighing scheme ? Map 1 (b) follows weighing 
scheme ? Map 2 (c) follows weighing scheme ? Map 3 (d) follows weighing scheme ? Map 4 
 
(a) (b) 
(c) (d) 
 
 
81 
CHAPTER 4 
4.1 SUMMARY 
The purpose of this study were (i) gathering and evaluation of historical data on soil 
slope failures in the State of Maryland and developing the necessary protocols to 
incorporate that information into a GIS database, (ii) developing a database structure 
containing information relating to soil slope failures (iii) laying the framework for the 
implementation of a quantitative model for predicting the vulnerable highway slopes 
in the State of Maryland. 
A majority of the SMS reviewed in chapter 2 have an inherent factor of 
subjectivity linked with their study. While the issue of subjectivity while assessing 
failed slopes wasn?t completely eliminated, this study presents and reviews 
procedures that can quantitatively analyze and rate slopes thus reducing subjective or 
qualitative analysis to a minimum. 
The important conclusions, results and findings are discussed as follows: 
a) A comprehensive management and assessment system has been developed to 
allow the SHA to better record, evaluate, analyze and review the soil slope failure 
data and soil slope remediation data 
b) A database structure containing information relating to soil slope failures or 
distresses was developed using MS Access. This database structure was then 
organized into a web-based relational GIS type database with multiple Oracle 
SDE tables. All the information stored in the database and analyzed results can 
now be visualized by using GIS features. 
c) A comprehensive survey sheet for recording information relevant and specific to 
 
 
82 
highway slope failures in the State of Maryland. This failure field sheet has 
significantly reduced the time and optimized the data collection process for the 
engineers in the field. It has also enforced a structured approach towards data 
collection, entry and storage. The information collected in the field can be keyed 
in to the GIS database. 
d) The Maryland SHA?s remediation response towards slope failures was categorized 
into list of factors based on the consequence of failure. Thus, the initial stage in 
consequence and risk study is laid out. Taking into consideration the liability and 
legal issues that might arise due to the limited number of slope failure datasets 
and incomplete information regarding remediation information and cost of 
construction, at present engineers simply use this categorization as a guideline for 
budget allocation and prioritizing remediation projects. 
e) A framework for analyzing slope instability is proposed and developed for the 
State of Maryland. A total of 48 slope failures recorded by Maryland SHA 
engineers using the GIS database were analyzed for emerging trends and patterns 
correlating physical parameters with slope instability.  
Using the SMS tools- the failure field sheet and the GIS database, a total of 48 
slope failure cases occurring between 2008 and 2012 were recorded actively and 
retroactively by engineers at the Maryland SHA. Based on the comprehensive 
information for the 48 slope failures and using spatial analysis tools certain trends in 
failure distribution in relation to the selected parameters were established. The 
significant and conclusive trends are listed as follows. 
 
 
83 
a. 56% of the total number of slope failures has occurred on slopes between 30 m 
and 90 m in height and nearly a fourth of the total number of failures have 
occurred on slope with heights between 10m and 30 m. No clear trend or 
correlation could be observed at present between slope height and soil slope 
failures in the State of Maryland 
b. It is clearly evident that a majority (more than 50%) of failures has occurred on 
slopes with slope angles between 20? and 30? as these failures have occurred along 
highway slopes. For all engineering and analyses purposes, the Maryland SHA 
assumes that all or most highway slopes have a 2H: 1V slope unless explicitly 
mentioned. Thus, the analysis is congruent with field conditions 
c. The failure distribution pattern for elevation and slope angle correlates with the 
field conditions observed by engineers, because the Maryland SHA records only 
those slope failures that are within their right of way. Since a distinct pattern or 
correlation with slope instability is yet to be drawn with respect to these 
parameters, it can be concluded that these numbers when combined with failure 
distribution patterns for other parameters will yield a more conclusive result. 
d. 52% of the remaining slope failures have occurred on developed land or 
urbanized regions. This trend presents an interesting insight into the effect of 
urbanization and land use pattern on slope instability.  
e. 58% of the existing slope failures have occurred in regions having low density 
land cover. This relatively large percentage occurrence of failures on developed 
land can be attributed to the increased amount of human activity such as blasting, 
drilling, traffic volume and other construction activities. 
 
 
84 
f. 90% of the total numbers of slope failures are surficial erosion failures. Only 4% 
of the slope failures are deep rotational type failures. Cross referencing with the 
GIS database, 80% of the total number of slope failures have occurred during or 
after rainfall.  
g. More than 80% of the total slope failures have occurred in regions expected to 
have heavy amount of rainfall during a storm event. 96% of the slope failures 
have occurred along highway slopes with open drainage sections.  
h. These trends when correlated with factors such as precipitation, the type of 
drainage section at failure site shows the influence of precipitation and drainage 
conditions on slope instability.  
i. The statistic that 87% of the total number of slope failures recorded has occurred 
in the Atlantic coastal plains province highlights the effect of lithology or soil 
type of the highway slopes. The Atlantic coastal plains province predominantly 
consists of slopes with silty or clayey sand, gravelly sand, coarse sand and gravel 
type soils.  
j. 50% of the total number of slope failures lay on slopes with sand formations and 
39% of these slope failures occur on slopes with gravel formations. 
k. The framework and guidelines for developing robust quantitative mapping system 
have been prepared. An ideal and suitable multivariate statistical approach was 
reviewed and is presented in this study. 
 
 
85 
4.2 RECOMMENDATIONS FOR FUTURE WORK 
The SMS developed and reviewed in this study is still in a nascent stage. As 
frequently mentioned in various sections of this document, the full potential of the 
system will be realized with the inclusion of more slope failure cases.  
The SMS developed has recorded only information relating slope failures that 
have occurred between 2008 and 2012. With the passage of time and continual 
process of further population of the database, many more improvements can be made 
to the system to support the influx of new information and analyze and establish more 
conclusive trends with regard to highway slope failures.  
While the skeletal structure or framework of the system has been established, further 
improvements that were discussed in this study, but are presently out of the scope of 
this study, can be implemented to realize the full potential of such a tool. This section 
discusses the recommended improvements to be made to the GIS components in the 
near future  
For the eGIS web map service, there are two important implementations that are 
required to improve the functionality and are discussed in the following paragraphs:  
The first implementation is a photo gallery that can be viewed through the eGIS 
content. The Photo Viewer Widget allows users to peruse through thumbnail pictures, 
provide file names and provides save-to-desktop capabilities. This component of the 
eGIS content allows users to upload and view photographs of the failure site taken 
after failure, during remediation and after remediation.  
Thus, the OMT is able to review and track (i) the performance of the highway 
slope after remediation projects (ii) the efficiency of a particular type of remediation 
 
 
86 
methodology for a particular type of failure. The eGIS Technical Team recommends 
an approach similar to that adopted in developing the previous widgets for the same 
content. One of the many benefits of the eGIS application is the ability to reuse the 
technology and code for other projects. OMT wants the ability to upload pictures 
stored on their fileshare with naming conventions and sub-folder structure.  
The second implementation is the incorporation of a robust quantitative mapping 
system based on the mathematical model discussed in the previous chapter. Such a 
model requires large sample datasets to accurately predict the probability of failure of 
any given highway slope. While this implementation cannot be adopted immediately, 
it is definitely imperative, because at the regional scale, only a quantitative mapping 
system would be ideal and accurate for use. 
When sufficient data is available regarding dimensions of initiation sites and 
volume of debris in the future, it is recommended that the scale of study is leveled 
down to medium to small scale (analysis is performed at the district level or county 
level). This increases the accuracy of prediction and presents a multitude of mapping 
techniques to be chosen from. Also the ratio between the total area of failure sites and 
the total study area becomes much more significant at this scale of study, thereby 
presenting conditions for susceptibility analysis or conditional probability analysis.  
As and when the GIS database is populated with remediation details and 
maintenance information, it is recommended that this data is analyzed to ascertain the 
most cost effective and efficient remediation methodology for a particular type of 
failure. The results from the data analysis could be used as input for developing an 
automated remediation response model, which provides the most viable remediation 
 
 
87 
option based on the set of parameters previously discussed. This model may also be 
able to perform benefit to cost ratio analysis, thereby providing district offices with 
significant results to allocate budgets and resources accordingly. 
  
 
 
88 
 
 
 
 
 
 
 
 
 
APPENDIX A 
FAILURE SITE FIELD SHEET
Failure Site Field Sheet                                       Site Evaluation Date: MM/DD/YYYY  
 
89 
Project Description: ______________________________________________________________________ 
Contract #:          FMIS#: 
              
1. Site Location 
District:_____________________  County: __________________ Date of Failure Reported : ______________ 
Route #:_____ Route Direction: _____ # of Lanes: ________ 
Route Name (if any):___________________________________  
ADT:______________________ Route Type: ______________ 
BMP    : _____ EMP    : _____ Northing (ft)  : ______________ Lat. (Deg)   : _________________ 
Easting   (ft) : ______________ Long. (Deg): _________________ 
Failure Location With Respect to Roadway:  Above Roadway   Below Roadway  
Weather Conditions during Failure:  Rain  Snow Flooding Other: _____________ 
Comments: ________________________________________________________________________________ 
2. Slope Failure Type 
Type of failure: 
  Erosion  Erosion Area 
 
Head                             
 Toe  Flank             Body             
  Rotational (provide sketch below) 
Circular  Deep  Shallow 
Non-circular  Deep  Shallow 
  Translation (provide sketch below)   Block   Slide 
  Compound / Complex (provide sketch below) 
  Others(provide sketch below)   Landslide   Flow   Spread 
Sketch Box 
Comments: 
Multiple Failures:  __ Yes  __ No 
 
Location information   : Area #___out of ___ 
Failure Site Field Sheet                                       Site Evaluation Date: MM/DD/YYYY  
 
90 
Project Description: ______________________________________________________________________ 
3. Detailed Dimensions of Failure 
Comments:______________________________________________________________________________________________
 _______________________________________________________________________________________________________
 _______________________________________________________________________________________________________ 
 
 
 
 
 A 1. Length of failure section along roadway, 
L:_______________Feet 
2. Average slope angle,  ?:_________degrees 
3. Height of slope, H:____________Feet 
4. Width of failure along slope incline, 
W:______________ Feet 
5. Distance from crest of slope to failure section, 
D1: _____________ Feet 
6. Distance from toe of slope to failure section, 
D2: _____________ Feet  
7. Maximum depth of failed section, 
D3:______________Feet 
 B 
1. L:_______________Feet  
2. ?:________________degrees 
3. H:________________Feet 
4. W:________________Feet 
5. D1: _______________ Feet 
6. D2: _______________ Feet 
7. D3:________________Feet 
 C 
1. L:_______________Feet 
2. ?:________________degrees 
3. H:________________Feet 
4. W:________________Feet 
5. D1: _______________ Feet 
6. D2: _______________ Feet 
7. D3:________________Feet 
 D 
1. L:_______________Feet 
2. ?:________________degrees 
3. H:________________Feet 
4. W:________________Feet 
5. D1: _______________ Feet 
6. D2: _______________ Feet 
7. D3:________________Feet 
? 
D2 
D1 
W 
D3 H 
D2 
D1 
W 
H 
? 
D3 
H 
D2 
D1 
W 
? 
D3 
D3 H 
D2 
D1 
W 
? 
Failure Site Field Sheet                                       Site Evaluation Date: MM/DD/YYYY  
 
91 
Project Description: ______________________________________________________________________ 
4. Impact Assessment on Roadway and Beyond Right of Way 
Current and Potential Impact of Slope Failure on Roadway 
  On slope with a low potential to affect shoulder 
  On slope with a low potential to affect roadway  
  On shoulder or on slope with moderate potential to affect roadway 
  On roadway or on slope with high potential to affect roadway or structure 
Current and Potential Impact of Slope Failure on Area Beyond Right of Way 
  On slope with a low potential to impact area beyond right of way 
  On slope with a moderate potential to impact area beyond right of way 
  On slope with a high potential to impact area beyond right of way 
  On slope with a high potential to impact building or structure beyond right of way 
Natural Activities 
Dip  Yes  No 
 
Maximum displacement of dip  
Vertical displacement (VD) (inch)    :______________________ 
Horizontal displacement (HD) (inch):______________________ 
Crack  Yes  No 
 
Maximum displacement of crack 
Vertical displacement (VD) (inch)    :______________________ 
Horizontal displacement (HD) (inch):______________________ 
Earth Debris on 
Roadway 
 Yes  No Estimated volume(Yd
 3):_______________________ 
Comments  
 
5. Adjacent Structures and Area 
Adjacent Structures 
Roads  Railroads  Residential Buildings Bridge Utilities Culverts Other(specify):____________ 
Surrounding Area 
Forest Agriculture Rural  Urban  Housing development  Others(specify):_______________________ 
Comments  
 
Failure Site Field Sheet                                       Site Evaluation Date: MM/DD/YYYY  
 
92 
Project Description: ______________________________________________________________________ 
6. Existing Utilities or Structures Affected 
Utilities/ Structures Affected 
Ditch line Bridge  Sewer line Electric- overhead    
 Drainage pipe  Travel lane pavement      Gas line Electric- underground 
 Culvert   Shoulder  Water line  Telephone- overhead 
Guard rail Headwall   Cable TV Telephone- underground 
Sign structure Others(specify):________________________________________________ 
Comments  
 
7. Slope Characteristics 
Slope Type Natural  Cut  Fill Cut and Fill Reinforced Rip-rap Rock  
Original Slope Ratio (H:V)  
Slope Surface Appearance Straight Concave Convex Hummocky Terraced Complex 
Vegetation 
Cover 
 Grass ___%  Land covered Comments: 
 Shrub ___%  Land covered 
 Cultivated land ___%  Land covered 
 Reforestation ___%  Land covered 
 Woodland ___%  Land covered 
 Other ___%  Land covered 
Vegetation Density  Sparse                                  Moderate                                Dense 
Hydrogeology 
Surface Water 
Types of Sources 
  None    
  Reservoir    
  Lake 
  Pond 
  Creek 
  Surface drainage 
  River  
  Other : _____________ 
Location of Sources with Respect to Highway 
  Above                                   Below                                      Both   
Surface Drainage Type 
  Closed section   Open section 
Surface Drainage Flow Direction 
  Towards slope   Away from slope 
Ground Water 
Groundwater Flow 
Into failure area Off failure area Both Unknown None 
Groundwater Condition 
Failure Site Field Sheet                                       Site Evaluation Date: MM/DD/YYYY  
 
93 
Project Description: ______________________________________________________________________ 
Spring Seep Both Unknown None 
Location of Groundwater 
 Above  Below  Middle  None 
Presence of Monitoring or Water well 
 Artesian  Flowing artesian  Pooled  None 
Comments  
 
8. Slope Materials Information 
Soil Origin 
Unweathered rock   Weather rock Residual soil 
Colluvium   Alluvium Till   Fill 
Combination  Other(specify):___________________________________ 
Soil Type 
Boulders/cobbles Stone fragments Gravel Sand 
Fine sand Silty gravel Clayey gravel                Silty sand 
Clayey sand Silty soil Clayey soil Organic 
Combination Others(specify):__________________________________________ 
Physiographic 
Province 
Appalachian Plateaus Blue Ridge  Ridge and Valley 
Piedmont Plateau ? Lowland Coastal Plain - Western Shore Upland 
Piedmont Plateau ? Upland Coastal Plain - Delmarva Peninsula 
Comments    
 
9. Observed Remediation 
Existing 
Remedial 
Activities 
Drainage Bio-stabilization Slope Geometry Correction   Retaining Structures 
Internal Slope Reinforcement Erosion Control                      Chemical Stabilization 
Rip-rap Other(specify):_________________________________________________ 
Comments  
Failure Site Field Sheet                                       Site Evaluation Date: MM/DD/YYYY  
 
94 
Project Description: ______________________________________________________________________ 
10. Preliminary Determination of Cause of Failure 
Human 
Activities 
Excavation/under cutting Groundwater pumping  Loading  
Deforestation   Defective maintenance  Failure of drainage 
Water leakage from pipes Artificial vibrations  Poor vegetation 
Loose waste dumping  Construction related  Other(specify):_____________ 
Natural 
Activities 
Rainfall   Snowmelt  Earthquake 
Ground water   Toe erosion  Inadequate long term strength 
Rapid drawdown/ Surface water level change  Erosion from concentrated surface flow 
Degradation of construction material   Other(specify):____________ 
Comments  
 
11. Suggested Remediation Measures 
 Drainage Improvement  Remarks: ______________________________________________________ 
 Scour Counter Measures Remarks: ______________________________________________________ 
 Remove & Replace Remarks: ______________________________________________________ 
 Rip-rap   Remarks: ______________________________________________________ 
 Light Weight Fills  Remarks: ______________________________________________________ 
 Chemical Treatment  Remarks: ______________________________________________________ 
 Bio-engineering  Remarks: ______________________________________________________ 
 Geosynthetic Reinforcement  Remarks: ______________________________________________________ 
 Regrading or Flattening Slope  Remarks: ______________________________________________________ 
 Benching and Regrading  Remarks: ______________________________________________________ 
 Counter Berm and Regrading  Remarks: ______________________________________________________ 
 Shear Key  Remarks: ______________________________________________________ 
 Soil Nailing  Remarks: ______________________________________________________ 
 Concrete Retaining Wall  Remarks: ______________________________________________________ 
 Sheet Pile  Remarks: ______________________________________________________ 
 H-Pile  Remarks: ______________________________________________________ 
 Drilled Shaft  Remarks: ______________________________________________________ 
 Solder Pile Lagging Wall  Remarks: ______________________________________________________ 
 Relocation  Remarks: ______________________________________________________ 
 Other (specify): 
_____________________________________________________________________________________________ 
_____________________________________________________________________________________________ 
  
Failure Site Field Sheet                                       Site Evaluation Date: MM/DD/YYYY  
 
95 
Project Description: ______________________________________________________________________ 
12. Remediation Information 
Repair Status : 
Recommended Repair : 
Recommendation Date : ___/___/______ 
Remediation FMIS #  : 
Remediation Contract #  : 
Remediation Method Used  :  
Estimated Repair Cost :______________________$ 
Estimated Time Required for Remediation (days):______________________days 
As-built Plans :______________________    
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
Evaluator name   : 
Evaluator signature  : 
 
 
96 
 
 
 
 
 
 
 
 
 
 
APPENDIX B 
REMEDIATION RESPONSE CATEGORIZATION
 
97 
 
 
Category Considered 
Not 
Considered 
Comments 
Accident History/Potential     
Relative Emergency 
(FHWA rating)  
   
Impact on Traffic     
Roadway Impedance     
Pavement Damage     
Utility Impact     
Impact of Failure along 
Length of roadway  
   
Material Incursion on 
Roadway  
   
Maintenance Frequency     
Maintenance Cost     
Annual Average Daily 
Traffic (AADT)  
   
Groundwater Conditions     
Vegetation Conditions     
Figure B.1 :The remediation response categorization sheet currently used by OMT 
engineers while filing geotechnical reports. 
  
 
98 
 
Table B.1: The various categories shortlisted and their definitions 
Impact on Traffic Categorizes the impact of failure based on the functioning capability of the 
highway after failure has occurred. It provides information on whether the traffic 
flow is normal, or the roadway is partially or completely shut down due to slope 
failure along the roadway 
Annual Average Daily 
Traffic (AADT) 
The total volume of vehicle traffic of a highway or road for a year divided by 
365 days. 
Maintenance Frequency Is used to reflect the intensity/frequency of the past maintenance activity of a 
landslide site. 
Maintenance Cost Is used to reflect the cost involved with remediation of the slope each time it 
fails 
Material Incursion on 
Roadway: Frequency 
Frequency per year at which the slope material falls on the roadway whenever 
the slope tends to fail 
Accident 
History/Potential 
Is used to categorize the accidents/damage caused to the public/property by the 
failed slope, or the potential of a slope to cause accidents when it fails. 
Pavement Damage Is used to reflect the magnitude of damage inflicted on the pavement as a result 
of the slope failure along the roadway 
Impact of Failure along 
Length of roadway 
Is the length of the failure section in feet measured along the roadway  
Roadway Impedance Is the extent of slope material incursion along the width of the roadway, caused 
due to slope failure 
Relative Emergency 
(FHWA rating) 
Is the failure rating criteria suggested by the FHWA based on the remediation 
response required for the failed slope. (ref. FHWA Slope stability and 
maintenance manual)  
Utility Impact Is the category used to reflect the intensity of the failure based on the number of 
utilities affected at the failure site 
Groundwater Conditions Is used to indicate the nature of groundwater conditions at the failure site 
Vegetation Conditions Is used to indicate the nature of vegetation conditions and the density of 
vegetation at the failure site. 
 
  
 
99 
 
Table B.2: Hierarchy numbers for categories listed engineer-wise. Cells highlighted in 
grey are suggested additions for which ratings were provided. 
Category Eng I Eng II Eng III Eng IV Eng V Eng VI Average Std. Deviation 
Impact on Traffic 1 3 13 3 3 5 4.7 4.27 
Roadway Impedance 2 2 5 6 13 1 4.8 4.45 
Pavement Damage 3 16 3 4 6 1 5.5 5.39 
Annual Average Daily 
Traffic (AADT) 4 15 15 11 4 14 10.5 5.24 
Average Vehicle Risk 
(AVR) 5 14 14 2 2 12 8.2 5.81 
Failure Depth: 
Embankment Height 6 9 9 8 14 1 7.8 4.26 
Material Incursion on 
Roadway: Frequency 7 10 10 5 12 1 7.5 4.04 
% Decision Sight 
Distance (DSD)  8 18 8 9 8 15 11.0 4.38 
Maintenance 
Frequency 9 5 11 12 10 5 8.7 3.01 
Maintenance Cost 10 7 12 15 11 6 10.2 3.31 
Accident 
History/Potential 11 4 2 1 5 2 4.2 3.66 
Relative Emergency 
(FHWA rating) 12 1 1 10 1 1 4.3 5.20 
Impact of Failure 
along Length of 
roadway 
13 8 4 7 7 1 6.7 4.03 
Traffic Speed 14 11 6 13 9 10 10.5 2.88 
Highway Classification 15 19 7 14 15 11 13.5 4.09 
% of Trucks 16 20 16 16 16 5 14.8 5.08 
Utility Impact  N/a 6 N/a N/a N/a N/a 6.0 N/a 
Rate of Slope 
Movement  
N/a 12 N/a N/a N/a N/a 12.0 N/a 
Groundwater 
Conditions 
N/a 13 N/a N/a N/a N/a 13.0 N/a 
Vegetation Conditions N/a 17 N/a N/a N/a N/a 17.0 N/a 
 
 
 
 
100 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
APPENDIX C 
BASE MAPS
 
101 
 
 
Figure C.1: Elevation data layer in meters along with the spatial distribution of slope failures
 
102 
 
 
Figure C.2: Slope angle data layer in meters along with the spatial distribution of slope failures
 
103 
 
 
Figure C.3: Land cover data layer in meters along with the spatial distribution of slope failures
 
104 
 
 
Figure C.4: Storm event precipitation (2 year, 24 hour) data layer in meters along with the spatial distribution of slope failures
 
105 
 
 
Figure C.5: Storm event precipitation (100 year, 24 hour) data layer in meters along with the spatial distribution of slope failures
 
106 
 
 
Figure C.6: Physiographic provinces data layer in meters along with the spatial distribution of slope failures
 
107 
 
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