Prediction of critical safety factor of slopes using multiple regression and neural network

Chakraborty, Arunav; Goswami, Diganta

doi:10.3233/jgs-170047

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…The finite element method can be used to assess the stability of slopes using a failure definition, such as the finite element strength reduction method. In the strength reduction method, soil strength parameters is reduced until the slope becomes unstable and, thus, the factor of safety is calculated as the ratio between the initial strength parameter and the critical strength parameter [13,41,[43][44][45][46][47][48][49][50][51].…”

Section: Introductionmentioning

confidence: 99%

Stability Analysis of Plant-Root-Reinforced Shallow Slopes along Mountainous Road Corridors Based on Numerical Modeling

2019

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Engineering methods such as soil nails, geosynthetic reinforcement, retaining structures, gabions, and shotcrete are implemented to stabilize road cut slopes along mountainous areas. However, these structures are not environmentally friendly and, particularly in Ethiopia, it is impossible to address all road problems due to financial limitations. Nowadays, soil reinforcement with plant roots is recognized as an environmentally sustainable alternative to improve shallow slope failure along mountainous transportation corridors. The aims of this study was, therefore, to conduct slope stability analysis along a road corridor by incorporating the effect of plant roots. Five plant species were selected for the analysis based on their mechanical characteristics. Namely, Eucalyptus globules (tree), Psidium guajava (shrub), Salix subserrata (shrub), Chrysopogon zizanioides, and Pennisetum macrourum (grasses). The roots' tensile strength and soil parameters were determined through tensile strength testing and triaxial compression tests, respectively. The factor of safety of the slope was calculated by the PLAXIS-2D software. The study showed that when the slope was reinforced with plant roots, the factor of safety (FOS) improved from 22-34%. The decreasing effect of vegetation on slope stability was observed when soil moisture increased. The sensitivity analysis also indicated that: (1) as the spacing between plants decreased, the effect of vegetation on the slope increased. (2) Slope angle modification with a combination of plant roots had a significant impact on slope stabilization. Of the five-selected plant species, Salix subserrata was the promising plant species for slope stabilization as it exhibited better root mechanical properties among selected plant species.

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Section: Introductionmentioning

confidence: 99%

Stability Analysis of Plant-Root-Reinforced Shallow Slopes along Mountainous Road Corridors Based on Numerical Modeling

2019

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“…It was concluded that the predictions made by the multiple regression model were acceptable. In a similar study, Chakraborty and Goswami [12] used the height of cut or slope height H, material properties, cohesion (c), friction (φ), slope inclination (β), unit weight (γ), and dimensionless parameter (m) as input parameters to predict the status of stability. They also reported a very similar conclusion to the study by Erzin and Cetin [11].…”

Section: Introductionmentioning

confidence: 99%

Slope Stability Classification under Seismic Conditions Using Several Tree-Based Intelligent Techniques

et al. 2022

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Slope stability analysis allows engineers to pinpoint risky areas, study trigger mechanisms for slope failures, and design slopes with optimal safety and reliability. Before the widespread usage of computers, slope stability analysis was conducted through semi analytical methods, or stability charts. Presently, engineers have developed many computational tools to perform slope stability analysis more efficiently. The challenge associated with furthering slope stability methods is to create a reliable design solution to perform reliable estimations involving a number of geometric and mechanical variables. The objective of this study was to investigate the application of tree-based models, including decision tree (DT), random forest (RF), and AdaBoost, in slope stability classification under seismic loading conditions. The input variables used in the modelling were slope height, slope inclination, cohesion, friction angle, and peak ground acceleration to classify safe slopes and unsafe slopes. The training data for the developed computational intelligence models resulted from a series of slope stability analyses performed using a standard geotechnical engineering software commonly used in geotechnical engineering practice. Upon construction of the tree-based models, the model assessment was performed through the use and calculation of accuracy, F1-score, recall, and precision indices. All tree-based models could efficiently classify the slope stability status, with the AdaBoost model providing the highest performance for the classification of slope stability for both model development and model assessment parts. The proposed AdaBoost model can be used as a screening tool during the stage of feasibility studies of related infrastructure projects, to classify slopes according to their expected status of stability under seismic loading conditions.

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“…This is regarded as among the most significant benefits of the standard soft computing methods used in today's world. In the literature on slope stability, successful implementations of these soft computing approaches may be identified (Wang et al, 2005;Choobbasti et al, 2009;Li et al, 2009;Chakraborty and Goswami, 2017;Kumar and Basudhar, 2018;Qian et al, 2019;Ahour et al, 2020;Li et al, 2020;Ray et al, 2020;Zheng et al, 2020;Che Mamat et al, 2021;Palazzolo et al, 2021); (Das et al, 2011;Erzin and Cetin, 2012;Erzin and Cetin, 2014;Abdalla et al, 2015;Ai and Zsaki, 2017;Chakraborty and Goswami, 2018;Rukhaiyar et al, 2018;Moayedi et al, 2019;Bui et al, 2020;Chen et al, 2020;He et al, 2020;Liao and Liao, 2020;Che Mamat et al, 2020;Markovic Brankovic et al, 2021;Meng et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Evaluation Circular Failure of Soil Slopes Using Classification and Predictive Gene Expression Programming Schemes

Hanandeh

2022

Front. Built Environ.

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This article presents an analysis of a dataset related to slope stability circular failure cases using the GeneXproTools program. Two models were developed: The first model was utilized to find a classification model that shows the stability status (S), and the second model was utilized to estimate the factor of safety and predict the mathematical model to serve as an indicator of slope stability. The proposed models include cohesion, unit weight, slope angle, coefficient of pore water pressure, internal friction angle, slope height, internal friction angle, and safety factor. The results showed that the values of accuracy and error were 93.1 and 6.9%, respectively, for classification model 1. The relationship between the actual values of the factor of safety and the output of model 2 was an R2 value of 0.96. The models developed were subjected to sensitivity analysis to determine the input variables’ impact on output for the two models. The selected slope locations with different material characterizations were introduced into geox5 software, and the results were compared to those of the two developed models. Furthermore, when the proposed models were compared to the available models in the literature, the results showed that the proposed models in this study performed better than the available models.

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Prediction of critical safety factor of slopes using multiple regression and neural network

Cited by 4 publications

References 14 publications

Stability Analysis of Plant-Root-Reinforced Shallow Slopes along Mountainous Road Corridors Based on Numerical Modeling

Stability Analysis of Plant-Root-Reinforced Shallow Slopes along Mountainous Road Corridors Based on Numerical Modeling

Slope Stability Classification under Seismic Conditions Using Several Tree-Based Intelligent Techniques

Evaluation Circular Failure of Soil Slopes Using Classification and Predictive Gene Expression Programming Schemes

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