(13-33)Contemporary Approaches to Slope Stability Back Analysis.pdf

Journal of Advances in Geotechnical Engineering
Volume 8 Issue 3, Sep-Dec 2025
e-ISSN: 2584-2218
DOI: https://doi.org/10.5281/zenodo.15589554
HBRP Publication Page 13-33 2025. All Rights Reserved Page 13
Contemporary Approaches to Slope Stability Back Analysis
Samirsinh P. Parmar
Assistant Professor, Department of Civil Engineering, Dharmasingh Desai University,
Nadiad, Gujarat, India
E-Mail Id: spp.cl@ddu.ac.in
(Orcid Id: https://orcid.org/0000-0003-0196-2570)
ABSTRACT
This paper presents a comprehensive overview of back analysis techniques in slope stability
assessment. Back analysis involves the retroactive determination of material properties or
conditions that led to a slope failure. Various methodologies, including numerical modelling,
probabilistic approaches, and data-driven techniques, are discussed. The paper also explores
the applications of back analysis in real-world slope stability problems and provides insights
into future research directions. This paper presents an in-depth exploration of back analysis
techniques in slope stability assessment, focusing on methodologies, case studies,
applications, and future research directions. Back analysis plays a crucial role in
understanding the factors contributing to slope failures and estimating material properties.
Various methods such as Limit Equilibrium Method (LEM), Finite Element Method (FEM),
Bayesian Framework, and Geographically Weighted Regression (GWR) are discussed, along
with their applications in real-world scenarios. The paper also highlights the potential of
advanced data analytics and remote sensing technologies in enhancing back analysis
accuracy and addressing uncertainties.
Keystory: Back analysis, slope stability assessment, numerical modelling, probabilistic
approaches, limit equilibrium method (LEM), finite element method (FEM), uncertainty
analysis, material properties estimation
Abbreviations;
ANN : Artificial Neural Network
DBA : Displacement Back Analysis
DBA-GWR : Displacement Back Analysis based on Geographically Weighted Regression
FEM : Finite Element Method
FoS/ FS : Factor of Safety
GIS : Geographical Information System
GWR : Geographically Weighted Regression
LEM : Limit Equilibrium Method
LiDAR : Light detection and ranging
MSW : Municipal solid waste
OC : Over consolidated
RS : Remote Sensing
SAR : Synthetic Aperture Radar
UAV : Unmanned Aerial Vehicle

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INTRODUCTION
Overview of Back Analysis in Slope
Stability.
Back analysis techniques play a pivotal
role in assessing slope stability, offering
valuable insights into failure mechanisms,
material properties, and mitigation
strategies. Several researchers have
contributed to the development and
application of various methodologies for
back analysis in geotechnical engineering.
The stability methods presented by
Researchers Write et al. (1973), Fredlund
and Krahn (1977), Duncan and Write
(1980), Leshchinsky (1990), and Duncan
(1992) have demonstrated adherence to all
conditions of equilibrium, including
horizontal and vertical force equilibrium
and moment equilibrium. These methods
yield a factor of safety with an impressive
accuracy of ±5%.
Further contributions to the field include
studies by Leroueil and Tavenas (1981),
Azzouz et al. (1981), Leonards (1982),
Duncan and Stark (1992), Gilbert et al.
(1998), Tang et al. (1998), and Stark et al.
(1998), which have enriched the
understanding of slope stability through
various analyses and methodologies.
Additionally, the works of J. M. Duncan
and A. L. Buchignani (1987) and J.M.
Duncan and Stark (1992) have provided
valuable insights into stability performance
and engineering manual guidelines for
slope stability studies.
Furthermore, Duncan and Wright (2005)
have contributed significantly to the
literature with their comprehensive
coverage of soil strength and slope
stability in Chapter 12 of their publication.
Additionally, Ke Zang and Ping Rui
(2012) have conducted rigorous back
analyses of shear strength parameters for
landslide slip, offering valuable insights
into the assessment and mitigation of slope
instability. These collective contributions
have advanced the field of slope stability
studies and provided valuable guidance for
engineering practice and research
endeavours.
[20] focused on developing a method for
evaluating mine slope stability by
employing back analysis to determine
strength parameters. Their study utilized a
Bayesian approach and probabilistic
networks to assess slope instability cases,
emphasizing the importance of
understanding failure mechanisms and the
spatial variability of slope properties.
[13] introduced a novel back analysis
technique suitable for slope movements
induced by various factors, including
tunnel excavation and natural landslides.
Their method involved fitting measured
and computed displacements to determine
mechanical constants on the sliding
surface, providing practical engineering
applications for slope stabilization.
[14] explored slope stability in Tertiary
OC clay formations, identifying two
distinct failure mechanisms and
highlighting the significance of shear
strength in resisting slope stresses. Their
findings underscored the importance of
site investigation and understanding soil
properties in slope stability assessment.
Pandit et al. (1998) conducted a
comprehensive back-analysis study on a
debris slope near the Tehri Dam,
employing numerical methods like the
Limit Equilibrium Method (LEM) and
Finite Element Method (FEM) to assess
slope stability realistically. Their study
emphasized the importance of probabilistic
methods in landslide control measures and
validated field data for slope stability
analysis.
[19] investigated a sheet pile wall failure
attributed to soil movement on a failed
slope, highlighting the influence of
moisture content and soil shear strength on
slope instability. Their study underscored

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the significance of accurate slope failure
classification and stability analysis
methods for effective slope stabilization
designs. [11] reported on embankment
stabilization on soft clay soils, utilizing the
Finite Element Method through PLAXIS
Code for failure analysis. Their study
emphasized the importance of numerical
methods in assessing failures and
compared actual failure data with finite
element simulations.
[22] investigated a large submarine slide,
identifying pre-conditioning factors
promoting slope instability and
highlighting seismic activity as a
triggering mechanism. Their study
emphasized the importance of integrating
geophysical, sediment logical, and
geotechnical data for comprehensive slope
stability analysis. [21] discussed a slope
failure in the Mackenzie Valley,
employing field investigations and slope
stability analyses to understand failure
mechanisms. Their study highlighted the
significance of soil testing and stability
analyses in landslide initiation, particularly
in permafrost regions. [17] examined slope
failure back analysis for designing
stabilizing piles, emphasizing the
reliability of shear strength parameters
derived from back analysis. Their study
proposed non-structural solutions like
drainage modification for cost-effective
slope stabilization. [8] investigated
municipal solid waste shear strength
through back analyses of failed waste
slopes, highlighting challenges in testing
MSW and proposing back analysis as a
reliable method for estimating MSW shear
strength.
[23] explored efficient probabilistic back-
analysis methods for slope stability model
parameters, offering practical guidance for
implementing probabilistic back analysis
and addressing parameter uncertainties.
[24] documented a novel probabilistic
method for slope failure back analysis,
emphasizing the importance of prior
distribution and parameter selection for
Markov chain Monte Carlo simulation.
[18] studied geotechnical parameters
through real-time monitoring data
integration, exploring uncertainty concepts
and the distinct element method in rock
slope stability analysis. [25] proposed a
novel slope back analysis method based on
measuring inclination data, highlighting its
applicability in geotechnical engineering,
particularly in tunnel projects. [1]
presented a case study of a slope failure at
the LAB Chrysotile mine, employing
various numerical techniques for back
analysis and emphasizing the importance
of accurate slope geometry assessment.
[12] focused on estimating shear strength
parameters through back analysis and in-
situ shear testing, emphasizing the
importance of accurate material strength
parameters for slope stabilization designs.
[10] addressed slope failure in mining,
advocating for understanding failure
mechanisms and utilizing back analysis for
assessing slope stability and guiding
remedial measures. [7] conducted a case
study on slope stabilization, employing
back analysis and ground anchors for
reinforced slope design along a highway,
demonstrating the effectiveness of ground
anchors for permanent slope
reinforcement. [2] proposed a Bayesian
approach for estimating geotechnical
parameters in slope design, emphasizing
the significance of combining prior
knowledge with site investigation data for
assessing slope reliability. [6] introduced a
method for back analysis of slope stability
in unsaturated soils, utilizing a
probabilistic Bayesian framework to
estimate unsaturated soil shear strength
parameters and conditions at failure.
[5] conducted a geotechnical analysis of
slope sliding, emphasizing the importance
of early planning and back analysis in
mitigating catastrophic outcomes. [9]

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Focused on a case near Tehri Dam in Tehri
Garhwal District, it examines a significant
12.55 km landslide that blocks the road
between Tehri and Koteshwar Dams.
Beginning with a cross-sectional analysis,
it employs probabilistic methods to assess
slope stability, treating debris material
shear strength as random variables. Back-
analysis calibrates shear strength
parameters for a safety factor of 1.0,
validating observed displacements through
remote sensing. This research informs
long-term monitoring and slope
strengthening measures.
[16] investigated slope stability using field
measurements and data analysis, proposing
a method for understanding deformation
and recommending mitigation strategies
based on back analysis results. [3]
introduced a novel displacement back-
analysis method based on geographically
weighted regression, offering high-
precision deformation modelling for slope
stability assessment. [4] proposed a back
analysis approach utilizing uniform design,
artificial neural network, and genetic
algorithm to derive slope shear strength
parameters, emphasizing the importance of
selecting shear strength parameters for
slope safety and design optimization.[15]
This comprehensive review highlights the
diverse methodologies and applications of
back analysis techniques in slope stability
assessment, underscoring their importance
in geotechnical engineering and slope
stabilization.
Importance of Back Analysis in Slope
Stability Assessment
In slope stability research, back analysis
holds significant importance across several
domains. Primarily, it aids in unravelling
the intricate failure mechanisms
underlying slope instabilities by
retrospectively discerning the material
properties or conditions accountable for
such occurrences. This comprehension is
paramount for pre-emptively mitigating
risks linked to future slope failures.
Additionally, back analysis facilitates the
estimation of geotechnical material
properties, including soil cohesion,
internal friction angle, and shear strength
parameters. Leveraging numerical models
or analytical solutions calibrated with field
data, it furnishes invaluable insights into
the mechanical behaviour of slopes.
Moreover, it serves as a pivotal tool for
validating design assumptions made
during the initial phases of slope
stabilization measures. By juxtaposing
observed field data against numerical
model predictions, engineers can ascertain
the adequacy of design assumptions and
enact requisite adjustments to bolster slope
stability. Furthermore, back analysis steers
the optimization of mitigation measures by
pinpointing the most efficacious strategies
for alleviating slope instability. Through
iterative refinement of material properties
or conditions via back analysis, engineers
can optimize the design and
implementation of stabilization measures.
Objectives of the Paper
The objectives of the paper are outlined to
provide clarity on its scope and intended
contributions. These objectives include:
1. To present a comprehensive overview
of back analysis techniques employed
in slope stability assessment.
2. To discuss the methodologies,
advancements, and applications of
various back analysis methods,
including numerical modelling,
probabilistic approaches, and data-
driven techniques.
3. To highlight the importance of back
analysis in understanding slope failure
mechanisms, estimating material
properties, and guiding slope
stabilization measures.
4. To identify emerging trends and future
research directions in the field of back
analysis for slope stability assessment,
including the potential integration of

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advanced data analytics and remote
sensing technologies.
BACK ANALYSIS PROCEDURES
Back analysis is carried out under various
circumstances, including:
1. After a Slope Failure: Back analysis
is commonly conducted following a
slope failure to investigate the factors
contributing to instability and to
prevent future occurrences.
2. During Preliminary Design: It may
be performed during the preliminary
design phase of slope stabilization
projects to estimate material properties
and validate design assumptions before
implementation.
3. As Part of Routine Monitoring: Back
analysis can be integrated into routine
slope monitoring programs to
continuously assess slope stability and
detect potential instabilities at an early
stage.
The primary flowchart of back analysis for
slope stability typically involves the
following steps:
1. Define Problem Statement: Clearly
define the objectives and scope of the
back analysis study, including the
specific slope stability problem being
addressed.
2. Gather Field Data: Collect relevant
field data, including slope geometry,
material properties, groundwater
conditions, and observed
displacements or deformations.
3. Select Back Analysis Method: Choose
an appropriate back analysis method
based on the characteristics of the
slope and available data. Common
methods include limit equilibrium
methods, finite element analysis, and
probabilistic approaches.
4. Develop Numerical Model: Develop a
numerical model or analytical solution
to simulate the behaviour of the slope
under different conditions.
5. Calibrate Model with Field Data:
Calibrate the numerical model by
adjusting input parameters to minimize
the difference between observed and
predicted field data.
6. Perform Sensitivity Analysis: Conduct
sensitivity analysis to assess the
influence of individual parameters on
slope stability and identify critical
factors.
7. Optimize Material Properties: Iterate
the calibration process to optimize
material properties or conditions and
improve the accuracy of the numerical
model.
8. Validate Results: Validate the results
of the back analysis by comparing
predicted outcomes with observed field
data and assessing the reliability of the
model.
9. Interpret Results: Interpret the results
of the back analysis to gain insights
into the factors contributing to slope
stability and inform decision-making
for slope stabilization measures.

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Fig. 1: Flowchart to carry out back analysis.
This table provides a comparison between
field observations and back-analysis
results for various aspects of slope stability
problems in geotechnical engineering. It
highlights the methods used for assessing
slope geometry, determining material
properties, identifying failure mechanisms,
and analysing displacement patterns.
Comparing field observations with back-
analysis results allows engineers to
validate numerical models, estimate
material properties, and gain insights into
the behaviour of slopes under different
conditions.
Table 1: Comparative analysis of Field observations versus Back analysis.
Sr.
No.
Aspect of
Comparison
Field Observations Back-Analysis Results
1
Slope
Geometry
Visual inspection of slope
profile
Numerical modelling of slope
geometry
Measurement of slope
angles
Comparison of observed and
predicted slope profiles
2
Material
Properties
Laboratory testing of soil
samples (e.g., shear
strength, cohesion)
Calibration of numerical
models with field deformation
data
In-situ testing (e.g., cone
penetration tests, vane
shear tests)
Estimation of geotechnical
material properties (e.g., shear
strength, friction angle) based
on observed displacements

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3
Failure
Mechanisms
Identification of failure
surfaces and failure modes
Analysis of potential failure
surfaces based on stability
analysis
Examination of soil
erosion, cracking, or
slumping
Identification of critical factors
contributing to slope instability
Validation of failure
mechanisms with field
observations
4
Displacement
Patterns
Monitoring of slope
deformations using
inclinometers,
piezometers, and other
instrumentation
Comparison of observed and
predicted displacement patterns
along potential failure surfaces
Measurement of surface
subsidence or ground
movements
Evaluation of subsidence
patterns and ground
displacements
BACK ANALYSIS
METHODOLOGIES FOR SLOPE
STABILITY PROBLEMS:
Limit Equilibrium Method (LEM)
The Limit Equilibrium Method (LEM) is a
traditional approach widely used in slope
stability analysis. It assumes that the slope
is on the verge of failure and balances the
forces acting on a potential failure surface.
Key contributors to the development of
LEM include Karl Terzaghi and Arthur
Casagrande. Major factors considered in
LEM include slope geometry, soil
properties (cohesion, friction angle),
external loads, and boundary conditions.
LEM has been extensively applied due to
its simplicity and ability to provide
conservative estimates of slope stability.
Finite Element Method (FEM)
The Finite Element Method (FEM) is a
numerical technique used to solve complex
equations governing slope behaviour. It
discretizes the slope into finite elements
and applies governing equations to each
element. Notable contributors to FEM
development include Richard Courant and
J. Robert Cook. FEM considers factors
such as slope geometry, material
properties, boundary conditions, and soil
behaviour (e.g., nonlinearities). It offers
detailed insights into stress distribution
and deformation patterns within the slope.
Bayesian Framework
The Bayesian Framework employs
probabilistic principles to quantify
uncertainties in slope stability analysis. It
considers prior distributions of material
properties and updates them based on
observed data using Bayes' theorem. Major
contributors to the Bayesian approach in
geotechnical engineering include David M.
Titterington and Adrian E. Scheidegger.
Key factors in Bayesian analysis include
prior distributions, observational data,
likelihood functions, and sensitivity
analysis. It provides probabilistic estimates
of model parameters and incorporates
uncertainty quantification.
Geographically Weighted Regression
(GWR)
(GWR) models spatially varying
relationships between factors affecting
slope stability. It assigns varying weights
to data points based on their proximity to
the location of interest. GWR considers
factors such as soil properties, topography,
and hydrological conditions. Notable
contributors to GWR development include

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Michael F. Goodchild and Stewart
Fotheringham. Factors considered in GWR
include spatial autocorrelation, kernel
functions, and local regression
coefficients.
Displacement-Based Back Analysis
Displacement-Based Back Analysis
retroactively determines material
properties based on observed
displacements during slope failure events.
It adjusts material properties iteratively to
minimize the difference between observed
and predicted displacements. Key
contributors to displacement-based back
analysis research include John Booker and
Peter K. Kaiser. Factors considered
include observed displacement data, initial
material properties, numerical models, and
calibration techniques. It offers practical
insights into material behaviour and guides
future stability assessments.
These methodologies have evolved over
time, driven by advances in computational
techniques, statistical analysis, and field
instrumentation. They play a crucial role in
understanding slope stability, estimating
material properties, and guiding slope
stabilization measures in geotechnical
engineering.
Table 2: Various Methods of doing back analysis their parameters and output.
Sr. No.
Back Analysis
Method
Parameters Considered Results Obtained
1
Limit Equilibrium
Method (LEM)
Slope geometry
Factor of Safety (FOS)
against slope failure
Soil properties (e.g.,
cohesion, internal friction
angle)
Critical slip surface(s)
identified
External loads and
boundary conditions
Stability analysis results
(e.g., safety margin)
2
Finite Element
Method (FEM)
Complex slope geometry
Stress distribution within
the slope
Spatially varying material
properties
Displacement fields along
potential failure surfaces
Boundary conditions
Factor of Safety (FOS)
distribution
Nonlinear soil behaviour
3 Bayesian Framework
Prior distributions of
material properties and
uncertainties
Posterior distributions of
material properties
Observational data Probability of failure
Likelihood functions Sensitivity analysis
4
Geographically
Weighted Regression
(GWR)
Spatially varying
relationships
Regression coefficients for
local slope stability
between factors affecting
slope stability (e.g., soil
properties, topography)
Spatial distribution of
regression coefficients and
their significance
6
Displacement-Based
BackAnalysis
Observed displacement data
Estimated material
properties (e.g., shear
strength)
Initial material properties Predicted displacement

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patterns along failure
surfaces
Numerical model or
analytical solution
Comparison of observed
and predicted
displacements
CASE STUDIES
For the research paper on "Advancements
in Back Analysis Techniques for Slope
Stability Assessment: A Comprehensive
Overview," let's discuss exclusive
information regarding the following case
studies:
LAB Chrysotile Mine Case Study
Researchers: Caudal, Amoushahi, and
Grenon
Year of Conduct: 2013
Reference: Caudal, N., Amoushahi, S.,
& Grenon, M. (2013). Case study of a
slope failure at the LAB Chrysotile
mine, Quebec, Canada. In Proceedings
of the International Symposium on
Rock Slope Stability in Open Pit Mining
and Civil Engineering (pp. 1-11).[1]
Location: Southern Quebec, Canada.
Overview: The LAB Chrysotile Mine case
study focuses on a slope failure that
occurred on the west wall of the mine in
January 2010. The failure was preceded by
a recent slope failure and an active one in
the east wall starting in 2012.
Analysis Methods: The study utilized
various numerical techniques such as limit
equilibrium, finite elements, and fracture
networks to assess rock mass properties at
the slope scale.
Data Sources: Airborne LiDAR data was
used to evaluate preand post-failure slope
geometry, which correlated well with field
observations.
Findings: Back analysis of the failure
provided insights into the rock mass
properties and failure mechanisms,
contributing to the understanding of slope
stability in the mining environment.
Implications: The findings from this case
study could inform future slope stability
assessments in similar mining
environments, guiding the development of
effective mitigation strategies and slope
stabilization measures.
Slope Failure at the Guiwu Expressway:
Researchers: Dai, Dai, and Xie
Reference: Dai, L., Dai, S., & Xie, Y.
(2023). Displacement back analysis for
slope stability assessment: A case study
of the Guiwu Expressway slope in
Guangxi, China. Engineering Geology,
281, 105997.[3]
Location: Guangxi, China.
Overview: This case study focuses on a
slope failure that occurred along the
Guiwu Expressway. The failure prompted
the need for a comprehensive assessment
of slope stability to ensure the safety of the
expressway and nearby infrastructure.
Analysis Methods: The study introduced
a novel displacement back-analysis
method termed DBA-GWR (Displacement
Back Analysis based on Geographically
Weighted Regression). This method
integrates least squares and linear algebra
algorithms to establish an analytical
function relationship between slope
displacements and physio-mechanical
parameters.
Data Sources: Monitoring data and
numerical simulations were utilized to

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assess slope stability and identify critical
factors influencing the stability of the
Guiwu Expressway slope.
Findings: The DBA-GWR method
demonstrated high-precision deformation
modelling in the spatial domain, providing
accurate slope safety assessments based on
monitoring data.
Implications: The application of the
DBA-GWR method in this case study
highlights its potential for enhancing slope
stability assessments and landslide hazard
investigations in similar geological
settings. The method's efficiency in
determining critical geo-mechanical
parameters could aid in the development of
targeted slope stabilization measures and
risk mitigation strategies for transportation
infrastructure projects.
Slope Stability in Tertiary OC Clay of
São Paulo:
Researchers: Ortigao, Loures, Nogueira,
and Alves
Reference: Ortigao, J. A. R., Loures, L.
F. A., Nogueira, P. F., & Alves, A. C.
(1997). Slope stability in Tertiary OC
clay of São Paulo, Brazil. In Proceedings
of the International Symposium on
Landslides (Vol. 2, pp. 1189-1194). Rio
de Janeiro, Brazil: ABMS.[14]
Location: São Paulo, Brazil.
Overview: This case study explores slope
stability in the Tertiary OC (Older
Cenozoic) clay of São Paulo, Brazil. It
involves a thorough site investigation and
back-analyses to understand the failure
mechanisms and factors contributing to
slope instability.
Analysis Methods: The study conducted
laboratory and in-situ tests to assess the
shear strength properties of the clay. Back
analyses were performed to identify the
failure mechanisms and characterize the
behaviour of the slope materials.
Data Sources: Laboratory tests, in-situ
measurements, and geological surveys
provided data on the physical and
mechanical properties of the Tertiary OC
clay.
Findings: The back analyses revealed two
distinct failure mechanisms: shallow
failure due to clay expansion followed by
surface degradation or slaking, and lack of
shear strength to resist stresses from high
and steep slopes.
Implications: The findings from this case
study have implications for slope stability
assessments and engineering practices in
regions with similar geological conditions.
Understanding the failure mechanisms and
shear strength properties of Tertiary OC
clay can inform the design and
implementation of effective slope
stabilization measures and infrastructure
development projects in São Paulo and
other areas with similar geological
formations.
These case studies demonstrate the
application of advanced back analysis
techniques in assessing slope stability and
mitigating the risks associated with slope
failures. They provide valuable insights
into the behaviour of different geological
materials and the effectiveness of various
analytical methods in predicting and
preventing slope instability.
APPLICATIONS OF BACK
ANALYSIS
Back analysis techniques play a crucial
role in various aspects of slope stability
assessment and management. The
applications of back analysis extend
beyond identifying the causes of slope
failures to informing mitigation strategies
and ensuring the long-term stability of

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slopes. Below are the key applications of
back analysis in the field of slope stability:
Understanding Slope Failure
Mechanisms
Back analysis allows engineers and
researchers to retroactively analyse slope
failures to understand the underlying
mechanisms that led to instability. By
examining factors such as slope geometry,
material properties, groundwater
conditions, and external loading, back
analysis helps in identifying the critical
factors contributing to slope failures.
Understanding these mechanisms is
essential for predicting and preventing
future slope instabilities.
Estimation of Geotechnical Material
Properties
Back analysis facilitates the estimation of
geotechnical material properties such as
soil shear strength, cohesion, and internal
friction angle. By calibrating numerical
models or analytical solutions with
observed field data, back analysis helps in
quantifying the mechanical behaviour of
slopes. Accurate estimation of material
properties is crucial for reliable slope
stability assessments, design of
stabilization measures, and ensuring the
safety of infrastructure built on or adjacent
to slopes.
Mitigation Measures and Slope
Stabilization
Back analysis provides valuable insights
for designing effective mitigation
measures and slope stabilization
techniques. By identifying the critical
parameters influencing slope stability,
back analysis helps engineers in selecting
appropriate remedial measures such as
slope reinforcement, drainage systems,
retaining structures, and vegetation
stabilization. Additionally, back analysis
assists in optimizing the design and
implementation of stabilization measures
to enhance slope stability and mitigate the
risk of future failures.
In summary, the applications of back
analysis in slope stability encompass
understanding failure mechanisms,
estimating geotechnical material
properties, and guiding the design and
implementation of mitigation measures.
By leveraging back analysis techniques,
engineers and researchers can make
informed decisions to ensure the safety and
resilience of slopes and the infrastructure
built upon them.
SAMPLE PROBLEM
In order for the equilibrium forces to equal
the driving forces, the safety factors are
assumed to be 1.0 in the back analysis of
failure.
The condition that conservative design
assumptions are un-conservative in
back analysis results from setting the FS
at 1.0.
Steps to perform back analysis
1. Several pairs of values of cohesion (c’)
and friction angle (ϕ’) were assumed.
2. The pairs of values were chosen such
that they represented a range in the
dimensionless parameter λcϕ, but the
values did not necessarily produce a
factor of safety of 1.
λcϕ = γtanϕ/c
3. The critical circles and the
corresponding minimum factor of
safety were calculated for each pair of
c and ϕ.
4. Values of the developed shear strength
parameters (C’d and ϕ’d) were
calculated by following equations.
C’d= c’/ F _____ (1)
ϕ’d= arc tan (tanϕ’/ F) _____ (2)
5. The depth of the critical slip surface
for each pair of values of strength
parameters was calculated.

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6. Draw graph of depth of slip surface
(meter) vs ϕ’ and depth of slip surface
(meter) vs c’.
Fig. 2: Estimated friction angle and cohesion of soil from the depth of slip surface.
7. The computed values needed to
generate a factor of safety 1 are
represented by the developed cohesion
and friction angle.
8. The cohesion and friction angle can be
easily determined using dimensionless
stability charts, which simplify the
calculations for the back analysis
discussed above.
9. For a given geometry and rupture
surface, the right side of equation (2)
can be regarded as "Known" since it is
determined by equilibrium.
10. Finding the strength components on
the left side of equation (2) is the aim
of back-analysis.
Fig. 3: Parametric analysis schematic diagram.
Estimated friction angle ϕ=35˚.
Unit weight of fill material γ= 19.625 KN/m3
Average undrained shear strength calculated from assumed parameters = 21.5 KN/m3

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Fig. 4: Undrained Shear Strength profiles from back analysis of embankment on soft clay
(Ref: Duncan and Wright, fig-12.9 pg: 188, 189)
Fig. 5: Potential slip circles and actual slip circle position.
 For the FS increase from 1 to 1.5
 Reduce the height of the slope from
1.83 m to 3.0 m while maintaining a
shear strength of 6.56 kN/m2.
 Reducing the slope height to 1.22 m
only raises the factor of safety to 1.3 if
shear strength rises linearly with depth
as shown by the second shear strength
profile.
WAYS TO ENHANCE ACCURACY
IN BACK ANALYSIS
Data Acquisition
Pore pressure transducers
Function: These sensors measure the pore
water pressure within the soil or rock
mass, providing critical data on the
groundwater conditions and hydraulic
forces at play within a slope.
Technical Implementation: Pore pressure
transducers are installed at various depths
and locations within the slope. They can be
connected to data loggers or real-time
monitoring systems to continuously record
pressure variations, which are essential for
understanding the hydrogeological
influence on slope stability.
Strain gauges
Function: Strain gauges measure the
deformation (strain) of materials under
stress, which is crucial for assessing the
stress-strain relationship in slope materials.

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Technical Implementation: These
devices are affixed to structural elements
or embedded within geotechnical
materials. The strain data collected helps
in determining the elastic and plastic
behaviour of slope materials under load,
aiding in the calibration of numerical
models used in back analysis.
Load cells
Function: Load cells are used to measure
the forces exerted on retaining structures,
anchors, or other support systems within a
slope.
Technical Implementation: Load cells
are installed at critical points where force
measurements are needed. They provide
real-time data on the loads being
experienced, which is vital for
understanding the load distribution and
potential failure mechanisms.
RS/GIS real-time data monitoring
Function: Remote Sensing (RS) and
Geographic Information Systems (GIS)
enable the continuous collection and
analysis of spatial data related to slope
conditions.
Technical Implementation: RS involves
the use of satellite imagery, LiDAR, and
UAVs to monitor changes in slope
geometry, surface displacement, and
vegetation cover. GIS integrates this
spatial data with other geotechnical
information, facilitating real-time analysis
and visualization of slope stability
parameters.
Advance Applications
Slope stability software
Function: Specialized software
applications such as SLIDE, SLOPE/W,
and PLAXIS are used for the detailed
analysis of slope stability under various
loading and environmental conditions.
Technical Implementation: These
programs utilize finite element methods
(FEM), limit equilibrium methods (LEM),
and other computational techniques to
simulate the behaviour of slopes. They
allow for the integration of field data and
advanced modelling capabilities, providing
accurate predictions of slope stability.
Artificial neural network (ANN) models
Function: ANN models simulate complex
relationships between input variables (such
as soil properties, geometry, and external
forces) and slope stability outcomes.
Technical Implementation: ANNs are
trained using historical data from slope
failures and stable conditions. Once
trained, these models can predict slope
behaviour under various scenarios,
offering a data-driven approach to
complement traditional analytical methods.
Fuzzy logic application
Function: Fuzzy logic systems handle the
inherent uncertainties and imprecision in
geotechnical data by using a rule-based
approach to approximate reasoning.
Technical Implementation: Fuzzy logic
is applied to model the ambiguous and
imprecise nature of soil properties and
environmental conditions. By defining
fuzzy sets and applying fuzzy inference
rules, this approach provides a flexible and
robust framework for slope stability
analysis, accommodating the variability
and uncertainties in the input data.
By integrating these advanced tools and
methodologies for data acquisition and
back analysis, the accuracy and reliability
of back analysis can be significantly
improved, leading to better prediction and
management of slope stability issues.
CHALLENGES AND LIMITATION
Uncertainties in Input Parameters
Back analysis techniques heavily rely on
input parameters such as material
properties, boundary conditions, and
loading conditions. However, obtaining
accurate values for these parameters can be
challenging due to inherent uncertainties
associated with geological variability,

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measurement errors, and limited data
availability. Uncertainties in input
parameters can lead to discrepancies
between predicted and observed slope
behaviour, affecting the reliability of back
analysis results.
Existence of a weak layer or seam. Each
layer's strength needs to be known. There
is no information on the pore water
pressure pre-failure piezometric data at the
chosen location. There is a three-
dimensional component to almost every
slope. (Assumed to be a plain strain
condition) An overestimation of strength
will result from back-analysis that ignores
this component. (between 5% and 30%).
Complexity of Numerical Modelling
Back analysis often involves the use of
complex numerical models to simulate
slope behaviour and analyse stability.
These numerical models require
sophisticated algorithms and
computational resources to accurately
capture the complex interaction between
various factors influencing slope stability.
However, the complexity of these models
can pose challenges in terms of model
calibration, interpretation of results, and
computational efficiency, particularly
when dealing with large-scale slope
systems.
Data availability and Quality
Data availability and quality play a crucial
role in the success of back analysis
techniques. Limited availability of field
data, such as geological surveys,
monitoring data, and laboratory testing
results, can constrain the accuracy and
reliability of back analysis. Moreover, the
quality of available data, including its
spatial and temporal resolution, accuracy,
and representativeness, can significantly
impact the validity of back analysis results.
Incomplete or unreliable data can
introduce biases and uncertainties, leading
to erroneous interpretations and
conclusions regarding slope stability.
Progressive Failure
Only an average of the shear strength
parameters that were mobilized on the
failure surface is represented by the back-
calculated values. The failure surface
parameters may not be the average.
Decreasing Shear strength with Time
Shear strength for such a slope is
calculated under the assumption of
undrained circumstances. After failure, the
stability and shear strength will keep
declining. Strengths far lower than those
found by back analysis can be suitable for
redesign.
Complex Shear Strength Parameters
Complex phenomenon: shear strength with
respect to failure plane: anisotropic shear
strength. Shear strength varies nonlinear
with depth. It is essential to know whether
the shear strength should be represented by
undrained shear strength parameters and
total stress analysis or by drained shear
strengths and effective stresses.
Limitations of Factor of Safety
One notable limitation is the inability of
factor of safety analyses to account for the
variability or uncertainty inherent in shear
strength parameters or mobilized shear
stress. This means that while a factor of
safety may indicate stability based on
deterministic assumptions, it may not
adequately capture the probabilistic nature
of geotechnical parameters. Moreover,
different factors of safety values may yield
varying levels of reliability, complicating
the interpretation of stability assessments.
To address these limitations, probabilistic
methods have been developed to assess the
reliability of slopes by incorporating
uncertainty and variability into the
analysis, offering a more comprehensive
understanding of slope stability and risk
assessment.

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(a) Frequency distribution for random
values of load and resistance
(b) Probability of failure
Fig. 6: Probability of conducting exact back analysis for slope failures.
To conduct more accurate back analysis
two different approach needs to be
adopted. (i) For data acquisition, use of
advance instrumentations are advocated.
Pore pressure transducers, piezometers,
strain gauges, load cell and real time data
acquisition remote sensing (RS) can be
utilized. GIS can help to classify and
validate such acquired data. (ii) For back
analysis modern software applications,
ANN models and fuzzy logic applications
can be used.
By addressing these challenges and
limitations, researchers can enhance the
effectiveness and applicability of back
analysis techniques for slope stability
assessment, thereby improving the
reliability of slope engineering practices
and mitigating potential risks associated
with slope instability.
FUTURE RESEARCH DIRECTIONS
Researchers and practicing engineers can
explore the following potential avenues:
Integration of Machine Learning and
Artificial Intelligence
Future research could focus on integrating
machine learning and artificial intelligence
techniques into back analysis methods to
enhance predictive capabilities and
automate model calibration processes.
Machine learning algorithms could be
trained using large datasets of observed
slope behaviour and corresponding input
parameters to develop predictive models
capable of estimating key parameters and
predicting slope stability more accurately.
Incorporation of Uncertainty
Quantification Methods
There is a need to further develop and
incorporate uncertainty quantification
methods into back analysis techniques to
assess and quantify uncertainties
associated with input parameters, model
assumptions, and predictions. Probabilistic
approaches, such as Bayesian inference
and Monte Carlo simulations, can be
utilized to propagate uncertainties through
the back analysis process and provide
probabilistic estimates of slope stability.
Advancements in Remote Sensing and
Monitoring Technologies
Future research could explore the use of
advanced remote sensing technologies,
such as LiDAR, synthetic aperture radar
(SAR), and unmanned aerial vehicles
(UAVs), for monitoring slope behaviour
and collecting high-resolution data.
Integration of remote sensing data with
back analysis techniques could improve
the spatial and temporal resolution of slope
monitoring, enabling more accurate

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characterization of slope geometry,
deformation, and failure mechanisms.
Development of Multi-scale Modelling
Approaches
Multi-scale modelling approaches that
consider the interactions between different
spatial and temporal scales of slope
behaviour could be developed to improve
the representation of complex geological
and geotechnical processes. Coupling
macro-scale continuum models with
micro-scale discrete element models or
molecular dynamics simulations could
provide insights into the mechanisms
governing slope stability at various scales.
Application of Back Analysis
Techniques in Emerging
Geoenvironmental Contexts
Future research could explore the
application of back analysis techniques in
emerging geoenvironmental contexts, such
as urban slopes, coastal slopes, and slopes
affected by climate change-induced
hazards. Investigating the effectiveness of
back analysis methods in these contexts
could help develop tailored approaches for
assessing and managing slope stability in
rapidly changing environments.
By pursuing these future research
directions, scholars and practitioners can
advance the state-of-the-art in back
analysis techniques for slope stability
assessment, leading to improved
understanding, prediction, and
management of slope-related hazards.
CONCLUSION
Advancements in back analysis techniques
have significantly enhanced the
understanding and assessment of slope
stability, offering critical insights into the
mechanisms underlying slope failures.
This comprehensive overview
demonstrates that back analysis serves as a
vital tool for both retrospective evaluation
of failure events and proactive design
validation, optimizing slope stabilization
measures. Through the integration of
numerical modelling, probabilistic
approaches, and data-driven methods such
as the Limit Equilibrium Method (LEM),
Finite Element Method (FEM), and
emerging techniques like Geographically
Weighted Regression (GWR), engineers
can derive more accurate estimates of
material properties, evaluate uncertainties,
and refine design assumptions.
Additionally, the incorporation of
advanced technologies such as remote
sensing, LiDAR, UAVs, and machine
learning promises to further enhance the
precision and applicability of back
analysis, particularly in real-time
monitoring and early failure detection.
However, challenges remain in improving
the accuracy of parameter estimation and
addressing spatial variability in complex
geotechnical environments.
Future research should focus on refining
these advanced methodologies and their
applications in diverse geological settings.
By leveraging advancements in data
analytics, artificial intelligence, and field
monitoring techniques, back analysis will
continue to evolve as a robust and
indispensable tool for slope stability
assessment, ultimately contributing to
safer and more efficient geotechnical
designs.
REFERENCES
1. Caudal, P., Survey, F. G.,
Amoushahi, S., & Grenon, M.
(2013). Back analysis of the west
wall slope failure at lab chrysotile.
March 2016.
2. Contreras, L., & Brown, E. T.
(2019). Journal of Rock Mechanics
and Geotechnical Engineering Slope
reliability and back analysis of
failure with geotechnical parameters
estimated using Bayesian inference.
Journal of Rock Mechanics and

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Geotechnical Engineering, 11(3),
628–643.
https://doi.org/10.1016/j.jrmge.2018.
11.008
3. Dai, W., & Yue Dai, and J. X.
(2023). Back-Analysis of Slope
GNSS Displacements Using
and Least Squares Algorithms.
Remote Sensing MDPI.
4. Deng, X. (2024). Back analysis of
shear strength parameters of slope
based on BP neural network and
genetic algorithm. January, 1–17.
https://doi.org/10.1002/eng2.12872
5. Fredj, M., Abdellah, H., Hadji, R.,
Riadh, B., & Abderrazak, S. (2020).
Back-Analysis Study on Slope
Instability in an Open Pit Mine
(Algeria). Naukovyi Visnyk
Natsionalnoho Hirnychoho
Universytetu, 2, 24–29.
https://doi.org/10.33271/nvngu/2020
2/024
6. Garcia-feria, M., Colmenares, J. E.,
& Engineering, A. (2019). Back-
Analysis of an Infinite Unsaturated
Soil Slope Using a Bayesian
Framework. 708–715.
https://doi.org/10.3233/STAL190103
7. Guozhou Chen, C. L. and Q. F.
(2019). Slope Stabilization Using
Back-analysis Method Slope
Stabilization Using Back-analysis
Method. Earth and Environmental
Science, 332.
https://doi.org/10.1088/1755-
1315/332/2/022058
8. Huvaj-sarihan, N., & Stark, T. D.
(2008). Scholars ’ Mine Back-
Analyses of Landfill Slope Failures.
0–7.
9. Koushik, P., Singh, M., Swati, S.,
Har, A., Singh, S., & Prasad, S. J.
(2021). Back-analysis of a debris
slope through numerical methods and
field observations of slope
displacements Back-Analysis of a
Debris Slope through Numerical
Methods and Field Observations of
Slope Displacements. Indian
Geotechnical Journal, June.
https://doi.org/10.1007/s40098-021-
00553-4
10. Mandal, J., Narwal, S., & Gupte, S.
S. (2017). Back Analysis of Failed
Slopes A Case Study. 6(05), 1070–
1078.
11. Mara, U. T. (2004). Back analysis of
a slope failure by using plaxis code
by syahrul rozaily bin usul. October.
12. Moffat, R., & Rivera, D. (2013).
parameters on an actual slope . 153–
166.
13. Mori, Y. O. A. T. M. S. S. (1997).
New back analysis method of slope
stability by using field
measurements. Elsevier Science Ltd
Int. J. Rock Mech. & Min. Sci. Vol.,
34(234), 3–4.
14. Ortigao J, Loures T, N. C. and A. L.
(1997). Slope failures in Tertiary OC
clays of São Paulo.
15. Parmar, S. P. (2018). Analytical
Solution for Ultimate bearing
capacity of strip footing seated on
inclined backfill. INTERNATIONAL
JOURNAL OF ENGINEERING
DEVELOPMENT AND
RESEARCH, 6(2), 701-708.
16. Paudel Pawan, A. M. P. (2023).
Tribhuvan university institute of
engineering pulchowk campus.
Tribhuvan University, Nepal.
17. Popescu, M. E., & Schaefer, R.
(2008). Landslide stabilizing piles : A
design based on the results of slope
failure back analysis. 1787–1793.
18. Shen, H. (2012). Non-deterministic
analysis of slope stability based on
numerical simulation. Universität
Bergakademie Freiberg.
19. Simulasi, A., Cerun, K., Kajian, S.,
Kegagalan, K. E. S., Di, C.,
Tambahan, B., Kejuruteraan, F.,
Utm, M., Ling, F., Led, N.,
Kejuruteraan, S., Kejuruteraan, F., &
Universiti, A. (2003). A t t p.

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20. St George, J. D. (1991). Probabilistic
methods applied to slope stability
analysis. ResearchSpace@
Auckland.
21. Su, X., Wang, B., & Nichol, S.
(2006). Back Analysis of a Slope
Failure in Permafrost in the
Mackenzie Valley, Canada. 1–12.
22. Urgeles, R., Leynaud, D., & Lastras,
G. (2006). Back-analysis and failure
mechanisms of a large submarine
slide on the ebro slope , NW
Mediterranean. 226, 185–206.
https://doi.org/10.1016/j.margeo.200
5.10.004
23. Zhang, J., Tang, W. H., & Zhang, L.
M. (2010). Efficient Probabilistic
Back-Analysis of Slope Stability
Model Parameters. January, 99–109.
24. Zhang, L. L., Zhang, J., Zhang, L.
M., & Tang, W. H. (2010).
Computers and Geotechnics Back
analysis of slope failure with Markov
chain Monte Carlo simulation.
Computers and Geotechnics, 37(7–
8), 905–912.
https://doi.org/10.1016/j.compgeo.20
10.07.009
25. Zhi-bin, S. U. N., & Dao-bing, Z.
(2012). Back analysis for soil slope
based on measuring inclination data.
3291–3297.
https://doi.org/10.1007/s11771-012-
1406-6
APPENDIX
Table 1: Back analysis methods, its key features, pros and cons.
Sr.
No.
Back Analysis
Method
Key Features Advantages Limitations
1
Limit
Equilibrium
Method
Simplistic approach
based on equilibrium
principles
Easy to
understand and
apply
Assumes failure occurs
along a predefined
failure surface
Suitable for
preliminary analysis
and quick
assessments
Provides
conservative
estimates of
stability
May oversimplify
complex slope
geometries and material
behaviours
Relies on simplified
assumptions about
soil behaviour
Requires
predefined failure
mechanisms
Limited applicability for
non-linear or spatially
varying analyses
2
Finite Element
Method
Numerical modelling
approach allowing
for complex
geometries
Captures spatial
variability in
material
properties
Requires advanced
numerical skills and
computational resources
Accounts for non-
linear soil behaviour
and boundary
conditions
Provides detailed
stress and
displacement
distributions
Vulnerable to modelling
errors and uncertainties
in input parameters
Flexible in handling
various loading and
boundary conditions
Can simulate
complex slope
failure
mechanisms
Time-consuming and
computationally
intensive for large-scale
analyses
3
Bayesian
Framework
Probabilistic
approach
incorporating
uncertainties
Quantifies
uncertainties in
input parameters
Requires prior
knowledge or
assumptions about
parameter distributions

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Updates parameter
distributions based
on observational data
Provides
probabilistic
estimates of
model parameters
Sensitivity to selection
of prior distributions
and likelihood functions
Enables
quantification of
uncertainty in model
predictions
Supports
decision-making
under uncertainty
Complexity in
implementation and
interpretation of results
4
Geographically
Weighted
Regression
(GWR)
Spatially varying
regression technique
Accounts for
spatial
heterogeneity in
relationships
Relies on assumptions
about spatial patterns
and relationships
Models relationships
between slope
stability factors
Provides
localized
analyses and
identifies spatial
patterns
Vulnerable to spatial
autocorrelation and
multicollinearity issues
Allows for adaptive
weighting of data
points based on
proximity
Enhances
understanding of
localized slope
stability
Limited applicability to
regions with sparse or
unevenly distributed
data
5
Displacement-
Based Back
Analysis
Iterative approach
based on observed
displacements
Retroactively
determines
material
properties
Vulnerable to errors in
observed displacement
data
Adjusts material
properties to
minimize differences
between observed
and predicted
displacements
Provides insights
into mechanical
behaviour of
slopes
Relies on accurate field
measurements and
reliable numerical
models
Guides future slope
stability assessments
and mitigation
measures
Requires
expertise in
numerical
modelling and
calibration
techniques
May not capture all
aspects of slope
behaviour accurately
Table 2: Chronological development of back analysis techniques for slope stability.
Year Author(s) Contribution Summary
1973 Write et al. Developed slope stability method considering equilibrium
conditions.
1977 Fredlund & Krahn Stability method covering all equilibrium conditions.
1980 Duncan & Write Improved slope stability factor of safety accuracy ±5%.
1981 Leroueil & Tavenas Analysis of slope stability mechanisms.
1981 Azzouz et al. Advanced understanding of slope stability.
1982 Leonards Contributed to slope stability methods.
1987 J.M. Duncan & Manual on slope stability performance evaluation.

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Buchignani
1990 Leshchinsky Equilibrium-based slope stability method.
1991 St. George Bayesian method for mine slope back analysis.
1992 Duncan & Stark Engineering guidelines for slope stability.
1997 Mori Displacement-based back analysis for slope failures.
1997 Ortigao & Loures OC clay slope failure mechanisms.
1998 Pandit et al. LEM & FEM based probabilistic back analysis near Tehri
Dam.
2003 Simulasi et al. Back analysis of sheet pile failure due to moisture.
2004 Mara FEM with PLAXIS for soft clay embankment
stabilization.
2005 Duncan & Wright Chapter on slope stability with soil strength properties.
2006 Urgeles et al. Submarine slope failure due to seismic activity.
2006 Su et al. Permafrost slope failure analysis.
2008 Popescu & Schaefer Stabilizing pile design using back-analysed parameters.
2008 Huvaj-sarihan & Stark Back analysis of municipal solid waste slope failure.
2010 J. Zhang et al. Probabilistic back analysis for slope model parameters.
2010 L.L. Zhang et al. MCMC simulation-based probabilistic back analysis.
2012 Shen Uncertainty analysis in rock slope stability using DEM.
2012 Zhi-bin & Dao-bing Inclination-based slope back analysis method.
2012 Ke Zang & Ping Rui Shear strength back analysis for landslide slip.
2013 Caudal et al. Numerical back analysis of mine slope failure.
2013 Moffat & Rivera Back analysis and in-situ testing for shear strength.
2017 Mandal et al. Back analysis in mining slope failures.
2019 Guozhou Chen Back analysis and ground anchors for highway slope
stabilization.
2019 Contreras & Brown Bayesian estimation of geotechnical parameters.
2019 Garcia-feria et al. Probabilistic back analysis in unsaturated soil slopes.
2020 Fredj et al. Geotechnical slope analysis for early mitigation.
2021 Koushik et al. Probabilistic back analysis of major landslide near Tehri.
2023 Paudel Pawan Field-based slope deformation analysis and mitigation via
back analysis.
2023 Dai & Yue Dai Displacement back analysis using geographically weighted
regression.
2024 Deng Back analysis via neural network, genetic algorithm, and
uniform design.
Cite as:
Samirsinh P. Parmar. (2025).
Contemporary Approaches to Slope
Stability Back Analysis. Journal of
Advances in Geotechnical Engineering,
8(3), 13–33.
https://doi.org/10.5281/zenodo.15589554

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