Journal of Applied Science and Engineering

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Yan Peng1This email address is being protected from spambots. You need JavaScript enabled to view it. and Haiquan Gao2

1Anhui Technical College of Industry and Economy, Hefei, Anhui, 230051, China

2CSCEC4 CIVIL ENGINEERING CO.LTD, Hefei, Anhui, 230000, China

 

 

Received: September 29, 2024
Accepted: April 4, 2025
Publication Date: June 15, 2025

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202602_29(2).0023  


Several empirical and theoretical methods have been used in civil infrastructure to ascertain the deep foundation’s load capacity. The models in this scenario are primarily driven by physical presumptions as well as the construction of estimations utilizing mathematical frameworks. In this article, innovative design patterns were developed, and three hybrid adaptive neuro-fuzzy inference systems (ANFIS) optimized with artificial rabbit optimization (ARO), cuckoo optimization algorithm (COA), and grey wolf optimization (GWO) have been applied to use experimental data to calculate the driven piles’ bearing capacity (Qt). To increase the optimal networks’ modeling efficacy, optimization methods were deployed to determine the essential parameters of the simulations. Also, other algorithms were developed for comparison purposes, such as single ANFIS, support vector regression (SVR) M5P, multi-adaptive regression spline (MARS), random forests (RF), and random trees (RT). It was concluded that both ANFIS systems optimized with ARO, GWO, and COA accomplish admirably among the categories of trains and tests, with a minimum R2 of 0.9285 in the learning dataset and 0.9313 in the examining dataset, respectively, indicating a strong similarity between experimental and estimated Qt. Comparing the outcomes of the single and hybrid models, the highest performance belonged to ARO-ANFIS, by gaining the largest values of correlation metrics and the lowest values of error-based metrics. After examining the dependability and considering the justifications, the ANFIS paired with ARO outperformed the COA-ANFIS and GWOANFIS in the Qt of driven piles forecasting model, this is the suggested system.


Keywords: Driven piles; Bearing capacity; Evaluation; Deep foundation; ANFIS; artificial rabbit optimization


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