Using the Radial Basis Function with the Novel Optimization Algorithms to Appraise the Pile Settlement rates

Yong  Zhang; Zhongyan  Luo; Qiuhong  Li; Daibing  Cheng; Wei  Tan

doi:10.6180/jase.202503_28(3).0007

Using the Radial Basis Function with the Novel Optimization Algorithms to Appraise the Pile Settlement rates

Yong ZhangThis email address is being protected from spambots. You need JavaScript enabled to view it., Zhongyan Luo, Qiuhong Li, Daibing Cheng, and Wei Tan

Department of Civil and Architectural Engineering, Nanchong Vocational and Technical College, Nanchong 637100, Sichuan, China

Received: April 26, 2023
Accepted: January 19, 2024
Publication Date: May 9, 2024

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.202503_28(3).0007

Pile bridges, in particular, can be immunized by estimating Pile Settlement (PS) before operating construction projects. As one of the most critical factors in transportation projects, PS should be checked to avoid failure or reduce the failure risk. Available parameters of ground characteristics and pile physical data would assist us in having the project perspective over the operation period. Theoretical strategies to calculate the pile movement have attracted expert attention to model the PS, and artificial neural networks (ANN) are considered an efficient method used in many research types. The current study has utilized the Radial Basis Function (RBF) alongside Biogeography-Based Optimization (BBO) and a novel Flow Direction Algorithm (FDA) appraising the number of neurons integrated within hidden layer optimally to estimate the PS rates trained to models. The transportation project called KVMRT in Kuala Lumpur was examined. Regarding the mentioned models, two frameworks of RBF-BBO and RBF-FDA were developed to feed the in-situ inputs and, after training models, generate the PS value. As metrics evaluated the models, the RMSE indicator for RBF-BBO and RBF-FDA reached 0.500 and 0.650mm. Also, the MAE for RBF-BBO was calculated at 0.2583 with a 1% difference. The R2 correlation index showed the RBF-FDA as high-accurate with a 1.5% difference from BBO. Using a soft-based method instead of costly experiments can increase modeling accuracy with desirable results.

Keywords: Pile Settlement; Radial basis function; Biogeography-Based Optimization; novel Flow Direction Algorithm; RMSE.

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