Geomechanics and Engineering

  • 제31권2호
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  • Pages.129-147
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  • 2022
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  • 2005-307X(pISSN)
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  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Metaheuristic models for the prediction of bearing capacity of pile foundation

  • Kumar, Manish (Department of Civil Engineering, SRM Institute of Science and Technology (SRMIST), Deemed to be University,) ;
  • Biswas, Rahul (Department of Applied Mechanics, Visvesvaraya National Institute of Technology Nagpur) ;
  • Kumar, Divesh Ranjan (Department of Civil Engineering, National Institute of Technology Patna) ;
  • T., Pradeep (Department of Civil Engineering, National Institute of Technology Patna) ;
  • Samui, Pijush (Department of Civil Engineering, National Institute of Technology Patna)
  • 투고 : 2021.12.30
  • 심사 : 2022.10.07
  • 발행 : 2022.10.25
⟨ 이전 논문 다음 논문 ⟩

초록

The properties of soil are naturally highly variable and thus, to ensure proper safety and reliability, we need to test a large number of samples across the length and depth. In pile foundations, conducting field tests are highly expensive and the traditional empirical relations too have been proven to be poor in performance. The study proposes a state-of-art Particle Swarm Optimization (PSO) hybridized Artificial Neural Network (ANN), Extreme Learning Machine (ELM) and Adaptive Neuro Fuzzy Inference System (ANFIS); and comparative analysis of metaheuristic models (ANN-PSO, ELM-PSO, ANFIS-PSO) for prediction of bearing capacity of pile foundation trained and tested on dataset of nearly 300 dynamic pile tests from the literature. A novel ensemble model of three hybrid models is constructed to combine and enhance the predictions of the individual models effectively. The authenticity of the dataset is confirmed using descriptive statistics, correlation matrix and sensitivity analysis. Ram weight and diameter of pile are found to be most influential input parameter. The comparative analysis reveals that ANFIS-PSO is the best performing model in testing phase (R2 = 0.85, RMSE = 0.01) while ELM-PSO performs best in training phase (R2 = 0.88, RMSE = 0.08); while the ensemble provided overall best performance based on the rank score. The performance of ANN-PSO is least satisfactory compared to the other two models. The findings were confirmed using Taylor diagram, error matrix and uncertainty analysis. Based on the results ELM-PSO and ANFIS-PSO is proposed to be used for the prediction of bearing capacity of piles and ensemble learning method of joining the outputs of individual models should be encouraged. The study possesses the potential to assist geotechnical engineers in the design phase of civil engineering projects.

키워드

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