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Application of Soft Computing Techniques for Slope Stability Analysis
Past studies have indicated significant uncertainties in determining the safety factor of slope using a deterministic approach. To reduce these uncertainties and enhance the stability of slopes, this study utilized machine learning (ML) techniques. The primary goal was to develop an efficient ML model to predict the factor of safety (FOS) of slope in c-φ soil. Three ML models were evolved: artificial neural network (ANN), Gaussian process regression (GPR) and hybrid ANN model, which combines with the meta-heuristic optimization technique namely particle swarm optimization (PSO) to make ANN-PSO. Five input parameters, i.e. unit weight of soil, cohesion, angle of shear resistance, slope angle and slope height, are used to compute FOS. The efficacy of the ML models is evaluated using a range of performance indicators, such as coefficient of determination (R2), variance account factor, Legate and McCabe’s index, a-10 index, root mean square error (RMSE), RMSE-observations standard deviation ratio, mean absolute error and median absolute deviance in both the training (TR) and testing (TS) stages. Among all the models, ANN-PSO performed better due to its higher value of R2 (TR = 0.932, TS = 0.833 and Overall = 0.920) and lowest value of RMSE (TR = 0.060, TS = 0.073 and Overall = 0.063) followed by GPR and ANN. The reliability index is calculated using the first-order second moment method for all the models and compared with the observed value. Further tools used to evaluate the model’s performance are rank analysis, reliability index, regression curve, William’s plot, error matrix and confusion matrix. The overall performance of ANN-PSO is superior to the other two ML models while predicting FOS. The influence of each input parameter on the output is also computed using sensitivity analysis.
Application of Soft Computing Techniques for Slope Stability Analysis
Past studies have indicated significant uncertainties in determining the safety factor of slope using a deterministic approach. To reduce these uncertainties and enhance the stability of slopes, this study utilized machine learning (ML) techniques. The primary goal was to develop an efficient ML model to predict the factor of safety (FOS) of slope in c-φ soil. Three ML models were evolved: artificial neural network (ANN), Gaussian process regression (GPR) and hybrid ANN model, which combines with the meta-heuristic optimization technique namely particle swarm optimization (PSO) to make ANN-PSO. Five input parameters, i.e. unit weight of soil, cohesion, angle of shear resistance, slope angle and slope height, are used to compute FOS. The efficacy of the ML models is evaluated using a range of performance indicators, such as coefficient of determination (R2), variance account factor, Legate and McCabe’s index, a-10 index, root mean square error (RMSE), RMSE-observations standard deviation ratio, mean absolute error and median absolute deviance in both the training (TR) and testing (TS) stages. Among all the models, ANN-PSO performed better due to its higher value of R2 (TR = 0.932, TS = 0.833 and Overall = 0.920) and lowest value of RMSE (TR = 0.060, TS = 0.073 and Overall = 0.063) followed by GPR and ANN. The reliability index is calculated using the first-order second moment method for all the models and compared with the observed value. Further tools used to evaluate the model’s performance are rank analysis, reliability index, regression curve, William’s plot, error matrix and confusion matrix. The overall performance of ANN-PSO is superior to the other two ML models while predicting FOS. The influence of each input parameter on the output is also computed using sensitivity analysis.
Application of Soft Computing Techniques for Slope Stability Analysis
Transp. Infrastruct. Geotech.
Mustafa, Rashid (author) / Kumar, Akash (author) / Kumar, Sonu (author) / Sah, Navin Kumar (author) / Kumar, Abhishek (author)
Transportation Infrastructure Geotechnology ; 11 ; 3903-3940
2024-12-01
38 pages
Article (Journal)
Electronic Resource
English
Application of Soft Computing Techniques for Slope Stability Analysis
| Springer Verlag | 2024
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