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GA-SVR: a novel hybrid data-driven model to simulate vertical load capacity of driven piles

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Abstract

Piles are widely applied to substructures of various infrastructural buildings. Soil has a complex nature; thus, a variety of empirical models have been proposed for the prediction of the bearing capacity of piles. The aim of this study is to propose a novel artificial intelligent approach to predict vertical load capacity of driven piles in cohesionless soils using support vector regression (SVR) optimized by genetic algorithm (GA). To the best of our knowledge, no research has been developed the GA-SVR model to predict vertical load capacity of driven piles in different timescales as of yet, and the novelty of this study is to develop a new hybrid intelligent approach in this field. To investigate the efficacy of GA-SVR model, two other models, i.e., SVR and linear regression models, are also used for a comparative study. According to the obtained results, GA-SVR model clearly outperformed the SVR and linear regression models by achieving less root mean square error (RMSE) and higher coefficient of determination (R2). In other words, GA-SVR with RMSE of 0.017 and R2 of 0.980 has higher performance than SVR with RMSE of 0.035 and R2 of 0.912, and linear regression model with RMSE of 0.079 and R2 of 0.625.

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Authors and Affiliations

  1. School of Resources and Safety Engineering, Central South University, Changsha, 410083, China

    Zhenyan Luo

  2. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Mahdi Hasanipanah

  3. School of Mining, College of Engineering, University of Tehran, Tehran, 11155-4563, Iran

    Hassan Bakhshandeh Amnieh

  4. Innovative Green Product Synthesis and Renewable Environment Development Research Group, Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Kathirvel Brindhadevi

  5. UTM Construction Research Centre, Institute for Smart Infrastructure and Innovative Construction (ISIIC), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru, 81310, Malaysia

    M. M. Tahir

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  1. Zhenyan Luo
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  2. Mahdi Hasanipanah
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  3. Hassan Bakhshandeh Amnieh
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  4. Kathirvel Brindhadevi
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  5. M. M. Tahir
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Correspondence to Mahdi Hasanipanah or Kathirvel Brindhadevi.

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Luo, Z., Hasanipanah, M., Bakhshandeh Amnieh, H. et al. GA-SVR: a novel hybrid data-driven model to simulate vertical load capacity of driven piles. Engineering with Computers 37, 823–831 (2021). https://doi.org/10.1007/s00366-019-00858-2

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