Artificial intelligence algorithms for predicting peak shear strength of clayey soil-geomembrane interfaces and experimental validation

• Published in: Geotextiles and geomembranes Vol. 51; no. 1; pp. 179 - 198
• Main Authors: Chao, Zhiming, Shi, Danda, Fowmes, Gary, Xu, Xu, Yue, Wenhan, Cui, Peng, Hu, Tianxiang, Yang, Chuanxin
• Format: Journal Article
• Language: English
• Published: Essex Elsevier Ltd 01.02.2023; Elsevier BV
• Subjects: Adaptive algorithms; Algorithms; Artificial intelligence; Artificial neural networks; Back propagation networks; Clay soils; Clayey soil; Design parameters; Evolutionary algorithms; Genetic algorithms; Geoengineering; Geomembrane; Interfaces; Machine learning; Neural networks; Particle swarm optimization; Peak shear strength; Sensitivity analysis; Shear strength; Soil permeability; Soil strength; Support vector machines; Swarm intelligence; Interfaces; Clayey soil; Peak shear strength; Geomembrane
• ISSN: 0266-1144; 1879-3584
• DOI: 10.1016/j.geotexmem.2022.10.007

The peak shear strength of clayey soil-geomembrane interfaces is a vital parameter for the design of relevant engineering infrastructure. However, due to the large number of influence factors and the complex action mechanism, accurate prediction of the peak shear strength for clayey soil-geomembrane interfaces is always a challenge. In this paper, a machine learning model was established by combining Mind Evolutionary Algorithm (MEA) and the ensemble algorithm of Adaptive Boosting Algorithm (ADA)-Back Propagation Artificial Neural Network (BPANN) to predict the peak shear strength of clayey soil-geomembrane interfaces based on the results of 623 laboratory interface direct shear experiments. By comparing with the conventional machine learning algorithms, including Particle Swarm Optimisation Algorithm (PSO) and Genetic Algorithm (GA) tuned ADA-BPANN, MEA tuned Support Vector Machine (SVM) and Random Forest (RF), the superior performance of MEA tuned ADA-BPANN has been validated, with higher predicting precision, shorter training time, and the avoidance of local optimum and overfitting. By adopting the proposed novel model, sensitivity analysis was carried out, which indicates that normal pressure has the largest influence on the peak shear strength, followed by geomembrane roughness. Furthermore, an analytical equation was proposed to assess the peak shear strength that allows the usage of machine learning skills for the practitioners with limited machine learning knowledge. The present research highlights the potential of the MEA tuned ADA-BPANN model as a useful tool to assist in preciously estimating the peak shear strength of clayey soil-geomembrane interfaces, which can provide benefits for the design of relevant engineering applications. •The ensemble artificial intelligence algorithms in predicting the peak shear strength of interfaces is compared.•An analytical equation for estimating the peak shear strength is proposed.•Physical experiments are conducted to validate the effectiveness of the proposed analytical equation.