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


Bardhan A., Kardani N., GuhaRay A., Samui P., Wu C., Zhang Y., ...More

ROAD MATERIALS AND PAVEMENT DESIGN, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Publication Date: 2022
  • Doi Number: 10.1080/14680629.2022.2117063
  • Journal Name: ROAD MATERIALS AND PAVEMENT DESIGN
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, Compendex, ICONDA Bibliographic, INSPEC
  • Keywords: Railway embankment, slope stability analysis, pavement design, Indian railways, artificial intelligence, adaptive particle swarm optimisation, ADAPTIVE REGRESSION SPLINES, FUZZY INFERENCE SYSTEM, MACHINE, FORMULATION, PSO
  • Hacettepe University Affiliated: Yes

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.