Hybrid neuro swarm intelligence paradigms for predicting the shear strength of sub-soil of heavy-haul freight corridor

Abstract

This study aims to circumvent the operation of conducting laboratory tests of soil shear strength through a hybrid machine learning approach. The proposed approach integrates extreme learning machine (ELM) and particle swarm optimisation (PSO) with adaptive acceleration coefficients. Three hybrid ELMs, namely PSO optimised ELM with time-varying acceleration coefficients (ELM-TP), ELM optimised by improved PSO (ELM-IP), and ELM optimised by modified PSO (ELM-MP), have been established. Subsequently, the concept of mean PSO has been incorporated, and three additional hybrid models, namely ELM-TP integrated with mean PSO (ELM-TMP), ELM-IP integrated with mean PSO (ELM-IMP), and ELM-MP integrated with mean PSO (ELM-MMP), are constructed. The proposed concept is also used to construct six artificial neural network (ANN)-based hybrid models (i.e. ANN-TP, ANN-IP, ANN-MP, ANN-TMP, ANN-IMP, and ANN-MMP). Experimental results exhibit that the constructed ELM-IMP and ANN-IMP models can achieve the most desired accuracies in predicting the shear strength of soils.