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A new hybrid simulated annealing-based genetic programming technique to predict the ultimate bearing capacity of piles

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Abstract

The aim of this research is to develop three soft-computing techniques, including adaptive-neuro-fuzzy inference system (ANFIS), genetic-programming (GP) tree-based, and simulated annealing–GP or SA–GP for prediction of the ultimate-bearing capacity (Qult) of the pile. The collected database consists of 50 driven piles properties with pile length, pile cross-sectional area, hammer weight, pile set and drop height as model inputs and Qult as model output. Many GP and SA–GP models were constructed for estimating pile bearing capacity and the best models were selected using some performance indices. For comparison purposes, the ANFIS model was also applied to predict Qult of the pile. It was observed that the developed models are able to provide higher prediction performance in the design of Qult of the pile. Concerning the coefficient of correlation, and mean square error, the SA–GP model had the best values for both training and testing data sets, followed by the GP and ANFIS models, respectively. It implies that the neural-based predictive machine learning techniques like ANFIS are not as powerful as evolutionary predictive machine learning techniques like GP and SA–GP in estimating the ultimate-bearing capacity of the pile. Besides, GP and SA–GP can propose a formula for Qult prediction which is a privilege of these models over the ANFIS predictive model. The sensitivity analysis also showed that the Qult of pile looks to be more affected by pile cross-sectional area and pile set.

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Acknowledgements

This research was funded by the National Science Foundation of China (41807259) and the Natural Science Foundation of Hunan Province (2018JJ3693).

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Authors and Affiliations

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

    Weixun Yong & Jian Zhou

  2. Kunming Prospecting Design Institute Of China Nonferrous Metals Industry Co., Ltd, Kunming, 650051, China

    Weixun Yong

  3. Modeling Evolutionary Algorithms Simulation and Artificial Intelligence, Faculty of Electrical & Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Danial Jahed Armaghani & Van Van Huynh

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

    M. M. Tahir

  5. Faculty of Civil Engineering, University of Tabriz, 29 Bahman Blvd, Tabriz, 51666, Iran

    Reza Tarinejad

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

    Binh Thai Pham

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  1. Weixun Yong
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Yong, W., Zhou, J., Jahed Armaghani, D. et al. A new hybrid simulated annealing-based genetic programming technique to predict the ultimate bearing capacity of piles. Engineering with Computers 37, 2111–2127 (2021). https://doi.org/10.1007/s00366-019-00932-9

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