Extreme Learning Machine Based Prediction of Soil Shear Strength: A Sensitivity Analysis Using Monte Carlo Simulations and Feature Backward Elimination

Abstract

Machine Learning (ML) has been applied widely in solving a lot of real-world problems. However, this approach is very sensitive to the selection of input variables for modeling and simulation. In this study, the main objective is to analyze the sensitivity of an advanced ML method, namely the Extreme Learning Machine (ELM) algorithm under different feature selection scenarios for prediction of shear strength of soil. Feature backward elimination supported by Monte Carlo simulations was applied to evaluate the importance of factors used for the modeling. A database constructed from 538 samples collected from Long Phu 1 power plant project was used for analysis. Well-known statistical indicators, such as the correlation coefficient (R), root mean squared error (RMSE), and mean absolute error (MAE), were utilized to evaluate the performance of the ELM algorithm. In each elimination step, the majority vote based on six elimination indicators was selected to decide the variable to be excluded. A number of 30,000 simulations were conducted to find out the most relevant variables in predicting the shear strength of soil using ELM. The results show that the performance of ELM is good but very different under different combinations of input factors. The moisture content, liquid limit, and plastic limit were found as the most critical variables for the prediction of shear strength of soil using the ML model. Sustainability 2020, 12, 2339 2 of 29 the laboratory, including direct shear test, triaxial compression tests, or unconfined compression tests which might increase the cost and prolong the time of completing the projects. Moreover, the test accuracy depends significantly on the instruments, the meticulous procedures, and the expertise of the experimenters [1]. Therefore, the development of new advanced techniques for quick and accurate prediction of shear strength of soil is essential and practical.Traditionally, the shear strength of soil is often predicted by using traditional formula-based methods. Garven and Vanapalli [2] summarized and evaluated nineteen empirical techniques that are available for the prediction of the shear strength of unsaturated soils. Out of these, six techniques used tool of the soil-water retention curve (SWRC) and the remainder thirteen procedures are based on mathematical formulations. In these empirical techniques, various parameters of soil were used to correlate with the shear strength in unsaturated soils such as the texture of soil surface, pore size distribution, residual suction. In another study, Sheng et al. [3] proposed different empirical equations for the prediction of shear strength of unsaturated soils using different approaches, which are based on the independent stress, Bishop's stress, and constitutive models. Vanapalli and Fredlund [4] compared different empirical approaches for the prediction of shear strength of unsaturated soils. Various parameters used for forming the correlation equations such as particle gain distribution, liquid limit, plasticity indices, water con...

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

“…Validation performance is a critical step in a modeling procedure, for which several statistical indices has been suggested and used [13,14,[49][50][51][52]. In this study, we used Area Under Receiver Operating Characteristic (ROC) curve (AUC) [39,[53][54][55][56], Root Mean Squared Error (RMSE) [57][58][59][60][61][62][63][64], Kappa, Accuracy (ACC), Specificity (SPF), Sensitivity (SST), Negative predictive value (NPV), and Positive predictive value (PPV) [65][66][67][68][69]. Detail description of these indices is presented in published literature [61,[70][71][72][73][74][75][76][77].…”

“…Detail description of these indices is presented in published literature [61,[70][71][72][73][74][75][76][77]. In general, lower RMSE and higher values of AUC, Kappa, ACC, SPF, SST, NPV, and PPV indicate higher model performance [57,58,65,[78][79][80][81][82]. Mathematically, these performance indices are given by [60,77,[83][84][85][86][87]:…”

Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping

“…In this study, we used positive (TP), true negative (TN), false positive (FP), false negative (FN), positive predictive value (PPV), negative predictive value (NPV), sensitivity (SST), specificity (SPF), accuracy (ACC), root mean square error (RMSE), and Kappa for the comparison and validation of the models. The equation for each one of these indices is as follows [75][76][77][78][79][80][81][82]:…”

Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams

“…The NN model exhibits crucial profits not found in traditional computational methods. Hypotheses or constraints are not necessary when optimizing NNs [111][112][113], and they are also able to analyze and explore complex (even nonlinear) relationships in data [114][115][116]. From a computational point of view, NNs are powerful at solving high dimensional problems because of their processing capabilities in parallel [19,117,118].…”