Journal of Applied Science and Engineering

Published by Tamkang University Press


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Haojie Chen1, Gongxing Yan This email address is being protected from spambots. You need JavaScript enabled to view it.1, Jie Li2, Haiyan Cui1, and Xiaolin Yao1

1School of Intelligent Construction, Luzhou vocational and technical college, Luzhou 646000, Sichuan, China
2School of Architectural Engineering, Chongqing Creation Vocational College, Yongchuan 402160, Chongqing, China


Received: December 8, 2021
Accepted: March 4, 2022
Publication Date: April 15, 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.

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In this study, two hybrid metaheuristic models are developed to discover the applicability of using an optimized radial basis function neural network to predict the undrained shear strength (USS) from cone penetration test (CPT) records. To this aim, genetic algorithm (GA) and grey wolf optimization (GWO) were integrated with radial basis function (RBFNN), named GA-RBFNN and GWO-RBFNN, to determine the optimal determinative parameters of RBFNN. Models trained with five input variables named cone tip resistance, sleeve friction, liquid limit, plastic limit, and overburden weight. To assess the precision of developed models, four statistical performance indices (R2, RMSE, MAE, and PI) were considered. The results demonstrate powerful potential in the learning section as well as approximating in the testing phase. It means that the correlation between observed and predicted USS from hybrid models is acceptable to represent the high accuracy in the training and approximating process. Concerning developed RBFNN models, it can be observed that GWO-RBFNN has better performance than GA-RBFNN based on evaluating the performance evaluator indices. Based on the outline of this paper aimed at finding RBFNN optimal parameters with optimization algorithms, GWO is more capable and rapid than GA for determining these parameters. Therefore, GWO-RBFNN outperforms the GA-RBFNN model with a total ranking of 16, consequently being recognized as the proposed model.

Keywords: Undrained Shear Strength of Soil; Cone Penetration Test; Prediction; Radial basis function; Grey Wolf Optimization; Genetic algorithm


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