Hui Gao1 and Zhao Jun-Wei This email address is being protected from spambots. You need JavaScript enabled to view it.2

1Zhengzhou Railway Vocational & Technical College, Henan, 451460, China
2North China Institute of Science and Technology, Computer Science Institute, Beijing, 101601, China


 

Received: September 25, 2021
Accepted: December 2, 2021
Publication Date: February 27, 2022

 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.202212_25(6).0014  


ABSTRACT


Deep foundations (piles) are pushed into the soil so as to carry out as permanent support of structures. Because piles can carry a large value of load, they must be precisely designed in terms of settlement. Hence, controlling and estimating of piles settlement is an important subject in pilling design and construction. The primitive objective of the present document is to discover the appropriateness of applying an optimized radial basis function neural network for foreseeing the pile settlement in rock. Here, ant lion optimization (ALO), biogeography-based optimization (BBO), and grey wolf optimization (GWO) were integrated with radial basis function (RBFNN), named ALO-RBFNN, BBO-RBFNN, and GWO-RBFNN, to determine the optimal determinative parameters of RBFNN. To use these algorithms, the results of pile driving analyzer tests and earth’s properties were measured for the Klang Valley Mass Rapid Transit (KVMRT) project built and operating in Kuala Lumpur, Malaysia. All three RBFNN models have high-level potential in the SP prediction process, in which the lowest value of R2 for the training stage is 0.9073 and 0.9015 for the testing phase. ALO-RBFNN model owns the most appropriate performance by considering coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and variance account factor (VAF) values, which are highest in the training and testing stages. Therefore, it could be concluded that all three hybrid RBFNN models are really capable of predicting SP. However, the ALO algorithm represents a higher ability to determine the RBFNN parameters’ optimal value than other proposed algorithms.


Keywords: Pile in rock; Settlement; Prediction; Radial basis function; Ant lion optimization; Biogeography-based optimization; Grey wolf optimization


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