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<p>This study develops and compares hybrid artificial intelligence models for predicting the shaft resistance of driven piles using 65 field records. A multilayer perceptron neural network was combined with four metaheuristic optimization algorithms: whale optimization algorithm, artificial bee colony, ant lion optimization, and ant colony optimization. The input parameters included pile length, pile diameter, effective vertical stress, and undrained shear strength, while shaft friction was considered as the target output. The models were trained and tested, and their performance was evaluated using R² and RMSE. The final comparison showed that ALO–MLP provided the best overall performance, with a testing R² of 0.981 and RMSE of 5.26. WOA–MLP also showed strong and reliable prediction ability, ranking second with a testing R² of 0.981 and RMSE of 6.79. ABC–MLP produced competitive results, while ACO–MLP was less accurate and more sensitive to population size. Overall, the findings suggest that ALO–MLP and WOA–MLP can serve as practical and dependable tools for estimating pile shaft resistance in geotechnical design.</p>

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Comparative assessment of hybrid AI models using metaheuristic algorithms for predicting shaft resistance of driven piles

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月4日

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  • Shengnan Wang,
  • Hossein Moayedi,
  • Mesut Gör,
  • Hossein Ahmadi Dehrashid

摘要

This study develops and compares hybrid artificial intelligence models for predicting the shaft resistance of driven piles using 65 field records. A multilayer perceptron neural network was combined with four metaheuristic optimization algorithms: whale optimization algorithm, artificial bee colony, ant lion optimization, and ant colony optimization. The input parameters included pile length, pile diameter, effective vertical stress, and undrained shear strength, while shaft friction was considered as the target output. The models were trained and tested, and their performance was evaluated using R² and RMSE. The final comparison showed that ALO–MLP provided the best overall performance, with a testing R² of 0.981 and RMSE of 5.26. WOA–MLP also showed strong and reliable prediction ability, ranking second with a testing R² of 0.981 and RMSE of 6.79. ABC–MLP produced competitive results, while ACO–MLP was less accurate and more sensitive to population size. Overall, the findings suggest that ALO–MLP and WOA–MLP can serve as practical and dependable tools for estimating pile shaft resistance in geotechnical design.