A Novel Hybrid Soft Computing Model Using Random Forest and Particle Swarm Optimization for Estimation of Undrained Shear Strength of Soil

Binh Thai Pham,Lanh Si Ho,Nadhir Al-Ansari,Manh Duc Nguyen,Chongchong Qi,Hai-Bang Ly,Trung Nguyen-Thoi,Huu Duy Nguyen,Hiep Van Le,Indra Prakash

doi:10.3390/su12062218

Abstract

Determination of shear strength of soil is very important in civil engineering for foundation design, earth and rock fill dam design, highway and airfield design, stability of slopes and cuts, and in the design of coastal structures. In this study, a novel hybrid soft computing model (RF-PSO) of random forest (RF) and particle swarm optimization (PSO) was developed and used to estimate the undrained shear strength of soil based on the clay content (%), moisture content (%), specific gravity (%), void ratio (%), liquid limit (%), and plastic limit (%). In this study, the experimental results of 127 soil samples from national highway project Hai Phong-Thai Binh of Vietnam were used to generate datasets for training and validating models. Pearson correlation coefficient (R) method was used to evaluate and compare performance of the proposed model with single RF model. The results show that the proposed hybrid model (RF-PSO) achieved a high accuracy performance (R = 0.89) in the prediction of shear strength of soil. Validation of the models also indicated that RF-PSO model (R = 0.89 and Root Mean Square Error (RMSE) = 0.453) is superior to the single RF model without optimization (R = 0.87 and RMSE = 0.48). Thus, the proposed hybrid model (RF-PSO) can be used for accurate estimation of shear strength which can be used for the suitable designing of civil engineering structures.

Highlights

In civil engineering, the shear strength of the soil is an essential engineering property in the foundation design and stability analysis of all major construction projects such as dams, bridges, highways and road, railway lines, jetties, underground structures, and high-rise buildings [1,2]
The standard deviation from the Random forest (RF) models was decreased with increasing training set size (TSS)
A novel hybrid machine learning namely RF-Particle Swarm Optimization (PSO) model, which is a combination of RF and PSO models, was proposed and applied to estimate the undrained shear strength of soil for the design purpose of the construction projects

Summary

Introduction

The shear strength of the soil is an essential engineering property in the foundation design and stability analysis of all major construction projects such as dams, bridges, highways and road, railway lines, jetties, underground structures, and high-rise buildings [1,2]. The shear strength parameters are usually determined in the laboratory using direct shear test, unconfined compression test, and triaxial compression test and in the field by shear vane test, Standard Penetration Test (SPT) and in situ-shear test [3,4] In addition to these tests, the estimation of the shear strength of soil from other indirect methods is needed for quick and reliable results. The shear strength of unsaturated soil can be predicted using the empirical correlation function [10,11], using soil-water retention curve [10,12,13]

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Results

Conclusion