国外文献 > 1.轴承 > 4. 设计与分析 > 算法与优化
关键词搜索：
| 标 题： |
A novel whale optimization algorithm optimized XGBoost regression for estimating bearing capacity of concrete piles
一种新颖的鲸鱼优化算法对XGBoost回归模型进行优化，用于估算混凝土桩的承载力 |
| 作 者： |
Hieu Nguyen
Cao, Minh-Tu
Xuan-Linh Tran
Thu-Hien Tran
Nhat-Duc Hoang |
| 关键词： |
Pile bearing capacity [桩的承载能力]
Machine learning [ 机器学习]
Metaheuristic [ 元启发式]
XGBoost [ XGBoost]
Whale optimization algorithm [ 鲸鱼优化算法]
GENETIC ALGORITHM [ 遗传算法]
DRIVEN PILE [ 驱动桩]
PREDICTION [ 预测]
MODEL [ 型号]
STRENGTH [ 力量]
LOAD [ 加载] |
| 出版年： |
2023 |
| 期刊名： |
Neural computing & applications
神经计算与应用 |
| 年卷期： |
2023, vol.35, no.5 |
| 摘 要： |
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.
本文提出一种混合模型，结合极端梯度提升机（XGBoost）和鲸鱼优化算法（WOA）来预测混凝土桩的承载能力。XGBoost通过一组解释性实验变量提供最终预测结果，而WOA则用于搜索最优的XGBoost参数组合，从而提高模型的准确性和鲁棒性。该混合方法基于从越南静载试验中收集的472个样本数据构建而成。实验结果表明，混合模型在性能上始终优于默认的XGBoost模型以及深度神经网络（DNN）回归模型。在20次重复实验中，所提出的模型相对于具有2、3、4和5层隐藏层的DNN模型，分别将均方根误差降低了约12%、11.7%、9%和12%。Wilcoxon符号秩检验证实，该模型非常适用于混凝土桩承载力的预测。 |
| 中文内容为网络AI翻译，专业术语未必准确，仅供参考。 |
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