An advanced meta-learner based on artificial electric field algorithm optimized stacking ensemble techniques for enhancing prediction accuracy of soil shear strength

Abstract

Shear strength is a crucial property of soils regarded as its intrinsic capacity to resist failure when forces act on the soil mass. This study proposes an advanced meta-leaner to discern the shear strength property and generate a reliable estimation of the ultimate shear strength of the soil. The proposed model is named as metaheuristic-optimized meta-ensemble learning model (MOMEM) and aims at helping geotechnical engineers accurately predict the parameter of interest. The MOMEM was established with the integration of the artificial electric field algorithm (AEFA) to dynamically blend the radial basis function neural network (RBFNN) and multivariate adaptive regression splines (MARS). In the framework of forming MOMEM, the AEFA consistently monitor the learning phases of the RBFNN and MARS in mining soil shear strength property through optimizing their controlling parameters, including neuron number, Gaussian spread, regularization coefficient, and kernel function parameter. Simultaneously, RBFNN and MARS are stacked via a linear combination method with dynamic weights optimized by the AEFA metaheuristic. The one-tail t test on 20 running times affirmed that with the greatest mean and standard deviation of RMSE (mean = 0.035 kg/cm2; Std. = 0.005 kg/cm2), MAE (mean = 0.026 kg/cm2; Std. = 0.004 kg/cm2), MAPE (mean = 7.9%; Std. = 1.72%), and R2 (mean = 0.826; Std. = 0.055), the MOMEM is significantly superior to other artificial intelligence-based methods. These analytical results indicate that MOMEM is an innovative tool for accurate calculating soil shear strength; thus, it provides geotechnical engineers with reliable figures to significantly increase soil-related engineering design.