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., ...Daha Fazla

ROAD MATERIALS AND PAVEMENT DESIGN, cilt.24, sa.8, ss.1885-1916, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 24 Sayı: 8
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1080/14680629.2022.2117063
  • Dergi Adı: ROAD MATERIALS AND PAVEMENT DESIGN
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, Compendex, ICONDA Bibliographic, INSPEC
  • Sayfa Sayıları: ss.1885-1916
  • Anahtar Kelimeler: 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 Üniversitesi Adresli: Evet

Özet

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.