Slope stability analysis using artificial intelligence techniques

Suman, Shakti; Khan, Shakilu Zama; Das, Sarat Kumar; Chand, Shyam

doi:10.1007/s11069-016-2454-2

Cited by 83 publications

(21 citation statements)

References 41 publications

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“…Pourkhosravani and Kalantari (2011) summarizes the current methods for slope stability evaluation, which were grouped into Limit Equilibrium (LE) methods, Numerical Analysis methods, Artificial Neural Networks and Limit Analysis methods. There are also approaches based on finite elements methods (Suchomel et al 2010), reliability analysis (Sivakumar Babu and Murthy 2005;Husein Malkawi et al 2000), as well as some methods making use of soft computing algorithms (Gavin and Xue 2009;Wang and Sassa 2005;Cheng and Hoang 2016;Ahangar-Asr et al 2010;Lu and Rosenbaum 2003;Sakellariou and Ferentinou 2005;Cheng et al 2012b;Yao et al 2008;Kang et al 2015;Kang et al 2016b; Kang and Li 2016;Kang et al 2016a;Kang et al 2017;Das et al 2011;Suman et al 2016). More recently, a new flexible statistical system was proposed by Pinheiro et al (2015), based on the assessment of different factors that affect the behavior of a given slope.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Model for Stability Condition Prediction of Soil Embankments Based on Visual Data Features

Tinoco

Correia

Cortez

et al. 2018

J. Comput. Civ. Eng.

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Keeping large-scale transportation infrastructure networks, such as railway networks, operational under all conditions is one of the major challenges today. The budgetary constraints for maintenance purposes and the network dimension are two of the main factors that make the management of a transportation network such a challenging task. Accordingly, aiming to assist the management of a transportation network, a data-driven model is proposed for stability condition prediction of embankment slopes. For such purpose, the highly flexible learning capabilities of Artificial Neural Networks (ANN) and Support Vector Machines (SVM) were used to fit data-driven models for Earthwork Hazard Category (EHC) prediction. Moreover, the data-driven models were created using visual information that is easy to collect during routine inspections. The proposed models were addressed following two different data modeling strategies: nominal classification and regression. Moreover, to overcome the problem of imbalanced data (since typically good conditions are much common than bad ones), three training sampling approaches were explored: no resampling, SMOTE and Oversampling. The achieved modeling results are presented and discussed, comparing the predictive performance of ANN and SVM algorithms, as well as the effect of the sampling approaches. A comparison between nominal classification and regression strategies was also carried out. Moreover, aiming a better understanding of the proposed data-driven 1 Tinoco et al., May 29, 2018 models, a detailed sensitivity analysis was applied, allowing to quantify the relative importance of each model input, as well as measuring their global effect on the prediction of embankments stability conditions.

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Section: Introductionmentioning

confidence: 99%

Data-Driven Model for Stability Condition Prediction of Soil Embankments Based on Visual Data Features

Tinoco

Correia

Cortez

et al. 2018

J. Comput. Civ. Eng.

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“…Artificial Neural Networks and Limit Analysis methods. There are also approaches based on finite elements methods (Suchomel et al 2010), reliability analysis (Sivakumar Babu and Murthy 2005;Husein Malkawi et al 2000), as well as some methods making use of soft computing algorithms (Gavin and Xue 2009;Cheng and Hoang 2016;Ahangar-Asr et al 2010;Lu and Rosenbaum 2003;Sakellariou and Ferentinou 2005;Cheng et al 2012b;Yao et al 2008;Kang et al 2015;Kang et al 2016b; Kang and Li 2016;Kang et al 2016a;Kang et al 2017;Das et al 2011;Suman et al 2016). More recently, a new flexible statistical system was proposed by Pinheiro et al (2015), based on the assessment of different factors that affect the behaviour of a given slope.…”

Section: Introductionmentioning

confidence: 99%

Stability Condition Identification of Rock and Soil Cutting Slopes Based on Soft Computing

Tinoco

Correia

Cortez

et al. 2018

J. Comput. Civ. Eng.

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For transportation infrastructure, one of the greatest challenges today is to keep large-scale transportation networks, such as railway networks, operational under all conditions. This task becomes even more difficult to accomplish if taken into account budget limitations for maintenance and repair works. In this paper, it is presented a tool aimed at helping in management tasks related to maintenance and repair works for a particular element of this infrastructure, the slopes. The highly flexible learning capabilities of Artificial Neural Networks (ANN) and Support Vector Machines (SVM) were applied in the development of a tool able to identify the stability condition of rock and soil cutting slopes, keeping in mind the use of information usually collected during routine inspection activities (visual information) to feed the models. This task was addressed following two different strategies: nominal classification and regression. Moreover, to overcome the problem 1 Tinoco et al.

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“…The other method Artificial Neural Networks (ANNs), possibly the most popular intelligent technique, was applied based on the function of nervous system and human brain (Shahin et al, 2004). Suman et al (2016) used the Functional Networks (FNs), Multivariate Adaptive Regression Splines (MARS) and Multigene Genetic Programming (MGGP) to predict the factor of safety by collecting the literature data of slope stability and found MARS to have comparatively better prediction accuracy than others. In Manouchehrian et al (2014) discussed the genetic algorithm model to predict the factor of safety of different slopes and showed more efficient than GP model of Yang et al (2004).…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Analysis Using Numerical and Neural Network, Near Kedarnath, Uttarakhand, India

Singh

Ansari

Vishal

et al. 2020

American Journal of Environmental Sciences

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The major concern in hilly regions is the stability of those slopes, which have been proclaimed due to unplanned excavation and uneven blasting during road widening and development activity. These slopes again become more vulnerable under dynamic loading and/or various types of human involvement, heavy rainfall and seismic activity. Failure of these slopes leads to loss of property and human being, disruption of traffics and environmental degradation. The Kedarnath area is the most vulnerable hilly terrain due to its inferred locality. To analyze the vulnerability near Kedarnath, the field observation was done to collect the geological and geotechnical details of three vulnerable locations. The present article illustrates the collective analysis of numerical simulation and artificial intelligence (ANN) models for the chosen vulnerable soil slopes. Numerical modeling was done to compute safety factor, stress distribution and maximum displacement using LEM and FEM modules. Further, the machine learning technique, ANN was also functionate to predict the stability based on geotechnical data's and numerical simulation results. The numerical analysis for the homogenous finite slopes shows that slopes are stable, critically stable and also prone to failure during rainy season. The ANN model evaluate that, the FoS by numerical modeling displays 98% validation to predictive neural networking system. The simulation result could be effectively applied to lessen/decrease the effect of regularity for the landslides in the area of particular morphology.

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Slope stability analysis using artificial intelligence techniques

Cited by 83 publications

References 41 publications

Data-Driven Model for Stability Condition Prediction of Soil Embankments Based on Visual Data Features

Data-Driven Model for Stability Condition Prediction of Soil Embankments Based on Visual Data Features

Stability Condition Identification of Rock and Soil Cutting Slopes Based on Soft Computing

Landslide Susceptibility Analysis Using Numerical and Neural Network, Near Kedarnath, Uttarakhand, India

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