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Neural Computing and Applications

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15-10-2022 | Original Article

A novel whale optimization algorithm optimized XGBoost regression for estimating bearing capacity of concrete piles

Authors: Hieu Nguyen, Minh-Tu Cao, Xuan-Linh Tran, Thu-Hien Tran, Nhat-Duc Hoang

Published in: Neural Computing and Applications | Issue 5/2023

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Abstract

This paper presents a hybrid model combining the extreme gradient boosting machine (XGBoost) and the whale optimization algorithm (WOA) to predict the bearing capacity of concrete piles. The XGBoost provides the ultimate prediction from a set of explanatory experiment variables. The WOA, which is configured to search for an optimal set of XGBoost parameters, helps increase the model’s accuracy and robustness. The hybrid method is constructed by a dataset of 472 samples collected from static load tests in Vietnam. The results indicate that the hybrid model consistently outperforms the default XGBoost model and deep neural network (DNN) regression. In an experiment of 20 runs, the proposed model has gained roughly 12, 11.7, 9, and 12% reductions in root mean square error compared to the DNN with 2, 3, 4, and 5 hidden layers, respectively. The Wilcoxon signed-rank tests confirm that the proposed model is highly suitable for concrete pile capacity prediction.

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Title: A novel whale optimization algorithm optimized XGBoost regression for estimating bearing capacity of concrete piles
Authors: Hieu Nguyen
Minh-Tu Cao
Xuan-Linh Tran
Thu-Hien Tran
Nhat-Duc Hoang
Publication date: 15-10-2022
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 5/2023
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-022-07896-w

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