Comparative Study of Hybrid Artificial Intelligence Approaches for Predicting Peak Shear Strength Along Soil-Geocomposite Drainage Layer Interfaces

Zhiming Chao,S M Dassanayake,Gary Fowmes

doi:10.1007/s40891-021-00299-2

Abstract

Peak shear strength of soil-Geocomposite Drain Layer (GDL) interfaces is an important parameter in the designing and operating related engineering structures. In this paper, a database compiled from 316 large direct shear tests on soil-GDL interfaces has been established. Based on this database, five different machine learning models: Back Propagation Artificial Neural Network (BPANN) and Support Vector Machine (SVM), with hyperparameters optimised by Particle Swarm Optimisation Algorithm (PSO) and Genetic Algorithm (GA), respectively, and Extreme Learning Machine (ELM) optimised by Exhaustive Method, were adopt to assess the peak shear strength of soil-GDL interfaces. Then, a comprehensive investigation and comparison of the predictive performance for the models was conducted. Also, based on the selected optimal machine learning model, sensitivity analysis was conducted, and an empirical equation developed based on it. The research indicated that GA and PSO could significantly increase forecasting precision in a small number of iterations. The BPANN model optimised by PSO has the highest forecasting precision based on the statistics criteria: Root-Mean-Square Error, Correlation Coefficient, Coefficient of Determination, Wilmot’s Index of Agreement, and Mean Absolute Percentage Error. The normal stress has the biggest impact on the peak shear strength, followed by drainage core type, moisture saturation of the soil layer, shearing surface, soil type, consolidation condition, geotextile specification, soil density and drainage core thickness, and the ranking is affected partly by the data distribution of input parameters in the database based on mechanism analysis. An empirical equation developed from the optimal model was proposed to estimate the peak shear strength, which provides convenience for geotechnical engineering personnel with limited knowledge of machine learning technique.

Highlights

Geocomposite Drainage Layers (GDL) are increasingly applied in extensive geotechnical and geoenvironmental applications [1,2,3]
A comprehensive investigation and comparison of the applicability for five different machine learning models including, Backpropagation Artificial Neural Network (BPANN) and Support Vector Machine (SVM), with hyperparameters optimised by Particle Swarm Optimisation Algorithm (PSO) and Genetic Algorithm (GA), respectively, and Extreme Learning Machine (ELM) optimised by Exhaustive Method, in estimating the peak shear strength of soil-GDL interfaces was conducted
It indicates that GA and PSO are powerful in optimising the hyperparameters of the established Back Propagation Artificial Neural Network (BPANN) and SVM models, which is able to improve the forecasting precision of the established machine learning models markedly with satisfactory efficacy

Summary

Introduction

Geocomposite Drainage Layers (GDL) are increasingly applied in extensive geotechnical and geoenvironmental applications [1,2,3]. GDLs can replace the need for graded sand and gravel to effectively drain excess water and reduce pore water pressure, improving the stability of engineering. Many series of laboratory tests have been conducted to determine the shear strength along soil-GDL interfaces [7,8,9,10,11]. Soil-GDL interface testing is expensive and time-consuming. In real engineering projects, the specific materials to be used on site are usually selected well after the design stage.

Methods

Results

Discussion

Conclusion