基于集成学习的锈蚀钢筋混凝土梁抗弯承载力预测模型研究

张雪; 门进杰; 荣强; 乔德浩

doi:10.3724/j.gyjzG24012901

基于集成学习的锈蚀钢筋混凝土梁抗弯承载力预测模型研究

doi: 10.3724/j.gyjzG24012901

张雪¹,
门进杰^1,2, ,,
荣强³,
乔德浩¹

1. 西安建筑科技大学土木工程学院, 西安 710055;
2. 西安建筑科技大学结构工程与抗震教育部重点实验室, 西安 710055;
3. 烟台大学土木工程学院, 山东烟台 264005

基金项目:

国家自然科学基金项目(52178160)

陕西省教育厅重点科学研究计划项目(21JY023)。

详细信息

作者简介:
张雪,博士研究生,主要从事工程结构智能防灾研究。

通讯作者:
门进杰,博士,教授,主要从事高性能结构及抗震韧性研究,mjj_xauat@126.com。

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- 被引次数: 0
出版历程
- 收稿日期: 2024-01-29
- 网络出版日期: 2025-01-04
- 刊出日期: 2024-12-20

Research on Prediction Models of Flexural Capacity of Corroded RC Beams Based on Ensemble Learning

1. School of Civil Engineering, Xi'an University of Architecture & Technology, Xi'an 710055, China;
2. Key Lab of Structural Engineering and Earthquake Resistance, Ministry of Education (XAUAT), Xi'an 710055, China;
3. School of Civil Engineering, Yantai University, Yantai 264005, China

摘要

摘要: 为了快速准确地判定锈蚀钢筋混凝土(Reinforced Concrete,RC)梁的抗弯承载力,利用集成学习研究锈蚀RC梁基于数据驱动的承载力预测模型。根据现有文献搜集并建立了锈蚀RC梁抗弯承载力试验数据库,基于数据集样本建立基于随机森林(RandomForest)、自适应增强(Adaboost)、梯度提升决策树(GBDT)、极限梯度提升算法(XGBoost)、轻量级梯度提升机算法(LightGBM)等5种集成学习算法的承载力预测模型,并借助网格搜索对模型进行超参数优化以提高其泛化性能。对比了不同集成学习算法的性能,即通过数据集分析了输入参数的特征重要性,对比分析了预测模型的平均绝对误差(MAE)、决定系数(R²)、均方根误差(RMSE)以判定其合理性与精确性。分析结果表明:该预测模型可以高效地确定锈蚀RC梁抗弯承载力的关键影响因素,即钢筋配筋率和钢筋锈蚀率;基于RandomForest的模型表现最优,其次是基于XGBoost的预测模型,预测模型在训练集和测试集上的拟合度可以达到90%以上。
- RC结构 /
- 承载力预测 /
- 集成学习 /
- 锈蚀 /
- 钢筋混凝土梁
Abstract: To quickly and accurately determine the flexural capacity of corroded reinforced concrete (RC) beams, an ensemble learning-based data-driven bearing capacity prediction model for corroded RC beams was studied. A database of experimental tests on the flexural bearing capacity of corroded RC beams was established based on existing literature. Based on the dataset, five types of ensemble learning algorithms, namely Random Forest (Random Forest), Adaptive Boosting (Adaboost), Gradient Boosting Decision Tree (GBDT), Limit Gradient Boosting Algorithm (XGBoost) and Light Gradient Boosting Algorithm (LightGBM), were used to establish prediction models. Grid search was employed to optimize the hyperparameters of the models to improve their generalization performance. The performance of different ensemble learning algorithms was compared, and the feature importance of input parameters was analyzed through the dataset. The mean absolute error (MAE), determination coefficient (R²) and root mean square error (RMSE) of the prediction models were compared to assess their rationality and accuracy. The analysis results indicated that the prediction model could effectively determine the key influencing factors of the flexural bearing capacity of corroded RC beams, namely the reinforcement ratio and the corrosion rate of the rebar. The model based on RandomForest performed the best, followed by the model based on XGBoost. The fitting degree of the prediction models on the training and test sets could reach over 90%.
- RC structures /
- prediction of bearing capacity /
- ensemble learning /
- corrosion /
- reinforced concrete beam

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