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Improving the estimation of the pile bearing capacity via hybridization technique based on adaptive network based fuzzy inference

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Abstract

Improving the estimation of pile bearing capacity (Pu) via the use of artificial intelligence (AI) methods and field experiment data becomes an extremely important and complex undertaking in the pile analysis and design sector. The primary objective of this study is to create cutting-edge AI prediction algorithms for determining Pu. This research employs the Adaptive Network-Based Fuzzy Inference System (ANFIS) architecture as the foundational predictive approach. To get accurate and ideal forecasts, a special hybrid approach that combines Leader-Harris Hawks Optimization (LHHO) and Snake optimization (SO) has been used. Data collection consists of 200 case instances from static load testing on driven piles, which were used in the process of constructing and verifying the model. These data collections are used in training, validating, and testing phases of the model creation process. The methodology used in this study produced accurate findings, demonstrating the effectiveness of the recommended approaches. The efficacy of the conventional ANFIS has been greatly improved by the addition of a hybridization approach, which has shown reliable results in Pu prediction. The ANFIS + LHHO (ANLH) hybrid model has demonstrated remarkable accuracy, attaining a substantial R2 of 0.993 and a minimal RMSE of 29.770, indicating a significant achievement.

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Authors and Affiliations

  1. Southwest Jiaotong University Hope College, Chengdu, 610400, Sichuan, China

    Li Gang

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  1. Li Gang
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Correspondence to Li Gang.

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Gang, L. Improving the estimation of the pile bearing capacity via hybridization technique based on adaptive network based fuzzy inference. J Ambient Intell Human Comput 15, 4043–4060 (2024). https://doi.org/10.1007/s12652-024-04878-9

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  • DOI: https://doi.org/10.1007/s12652-024-04878-9

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