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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- 文章访问数: 59
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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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参考文献(41)

[1]	ZHANG H F, ZHANG W, GU X, et al. Chloride penetration in concrete under marine atmospheric environment-analysis of the influencing factors[J]. Structure and Infrastructure Engineering, 2016, 12(11): 1428-1438.
[2]	COSTA A, APPLETON J. Chloride penetration into concrete in marine environment—Part I: main parameters affecting chloride penetration[J]. Materials and Structures, 1999, 32(4): 252-259.
[3]	牛荻涛.混凝土结构耐久性与寿命预测[M]. 北京: 科学出版社, 2003: 95-126.
[4]	李鹏飞,许乐欣,毛燕,等.锈蚀-疲劳耦合作用下钢筋混凝土梁挠度计算方法研究[J].工程力学,2023,40(增刊1):207-212,247.
[5]	金伟良,赵羽习.锈蚀钢筋混凝土梁抗弯强度的试验研究[J]. 工业建筑, 2001, 31(5): 9-11.
[6]	袁迎曙,贾福萍, 蔡跃. 锈蚀钢筋混凝土梁的结构性能退化模型[J]. 土木工程学报, 2001, 34(3): 47-52.
[7]	邢国华,牛荻涛.锈蚀钢筋混凝土梁的受弯分析模型[J].中南大学学报(自然科学版),2014,45(1):193-201.
[8]	姜鹏洲,王玉田,姜福香,等.荷载与海水环境耦合作用下钢筋混凝土梁的抗弯性能研究[J].混凝土与水泥制品,2023(5):36-41.
[9]	杨晓明,吴天宇,陈永林.锈蚀钢筋混凝土梁受弯性能试验研究[J].自然灾害学报,2018,27(5):70-78.
[10]	范颖芳,周晶,黄振国.受氯化物腐蚀钢筋混凝土构件承载力研究[J].工业建筑,2001,31(5):3-5.
[11]	徐善华,曾柯,牛荻涛.锈蚀钢筋混凝土受弯构件正截面承载力计算模型[J].建筑结构,2006(10):79-81,97.
[12]	彭建新,胡守旺,宋波,等.锈蚀RC梁抗弯性能试验与数值分析[J].中国公路学报,2015,28(6):34-41 ,50.
[13]	张猛,李瑶亮,赵桂峰,等.锈蚀钢筋混凝土梁承载力退化特性数值分析[J].混凝土,2016(3):34-39,43.
[14]	姜超,丁豪,顾祥林,等.锈蚀钢筋混凝土梁正截面受弯破坏模式及承载力简化计算方法[J].建筑结构学报,2022,43(6):1-10.
[15]	戴理朝,梁紫璋,胡卓,等.基于改进PF算法的锈蚀RC结构抗弯承载力预测方法[J].工程力学,2023,40(9):108-116 ,189.
[16]	ADELI H. Neural networks in civil engineering:1989—2000[J].Computer-Aided Civil and Infrastructure Engineering, 2001, 16(2): 126-142.
[17]	AMEZQUITA-SANCHEZ P, VALTIERRA-RODRIGUEZ M, ADELI H. Machine learning in structural engineering[J]. ScientiaIranica, 2020,27(6):2645-2656.
[18]	MANGALATHU S, JEON J S. Classification of failure mode and prediction of shear strength for reinforced concrete beam-column joints using machine learning techniques[J]. Engineering Structures, 2018, 160: 85-94.
[19]	MANGALATHU S, JEON J S. Machine learning-based failure mode recognition of circular reinforced concrete bridge columns: comparative study[J]. Journal of Structural Engineering, 2019, 145(10), 04019104.
[20]	GONG H, SUN Y, DONG Y, et al. An efficient and robust method for predicting asphalt concrete dynamic modulus[J]. International Journal of Pavement Engineering, 2021, 23(8): 2565-2576.
[21]	HUANG H, BURTON H. Classification of in-plane failure modes for reinforced concrete frames with infills using machine learning[J]. Journal of Building Engineering, 2019, 25,100767.
[22]	HADZIMA-NYARKO M, NYARKO E K, LU H F, et al. Machine learning approaches for estimation of compressive strength of concrete[J]. The European Physical Journal Plus,2020,135(8): 682.
[23]	张书颖,陈适之,韩万水,等.基于集成学习的FRP加固混凝土梁抗弯承载力预测研究[J].工程力学,2022,39(8):245-256.
[24]	狄春锋.钢纤维混凝土梁抗剪强度的集成学习预测方法[J].工业建筑,2023,53(11):139-144.
[25]	冯德成,吴刚.混凝土结构基本性能的可解释机器学习建模方法[J].建筑结构学报,2022,43(4):228-238.
[26]	孙晓燕,王海龙,于凤荣.锈损不锈钢钢筋混凝土梁受弯性能退化试验研究[J].建筑结构学报,2021,42(6):160-168.
[27]	郭诗惠,刘炳.锈蚀钢筋混凝土梁抗弯承载力计算与分析[J].建筑结构,2017,47(4):44-48.
[28]	牛荻涛,翟彬,王林科,等.锈蚀钢筋混凝土梁的承载力分析[J].建筑结构,1999,29(8):23-25.
[29]	吴庆,汪俊华,耿欧,等.硫酸盐和氯盐侵蚀的混凝土梁抗弯性能[J].中国矿业大学学报,2012,41(6):923-929.
[30]	王庆霖,池永亮,牛荻涛.锈后无粘结钢筋混凝土梁的模拟试验与分析[J].建筑结构,2001,31(4):51-53 ,63.
[31]	张建仁,张克波,彭晖,等.锈蚀钢筋混凝土矩形梁正截面抗弯承载力计算方法[J].中国公路学报,2009,22(3):45-51.
[32]	孙马,周建庭,徐略勤,等.基于加速锈蚀试验的RC梁抗弯性能劣化研究[J].重庆交通大学学报(自然科学版),2020,39(8):51-58.
[33]	卫军,张萌,董荣珍,等.钢筋锈蚀对混凝土梁破坏模式影响的试验研究[J].湖南大学学报(自然科学版),2013,40(10): 15-21.
[34]	惠云玲,李荣,林志伸,等.混凝土基本构件钢筋锈蚀前后性能试验研究[J].工业建筑,1997,27(6):15-19 ,58.
[35]	陶峰,王林科,王庆霖,等.服役钢筋混凝土构件承载力的试验研究[J].工业建筑,1996,26(4):17-20 ,27.
[36]	卢朝辉, 李海, 赵衍刚,等. 锈蚀钢筋混凝土梁抗剪承载力预测经验模型[J]. 工程力学, 2015, 32(增刊1): 261-270.
[37]	游士兵,严研.逐步回归分析法及其应用[J].统计与决策,2017(14):31-35.
[38]	张威,肖麟,王斓凯.2019世界幸福报告的可视化分析[J].大众标准化,2020(11):120-121.
[39]	李宁,杨镇华,马伟中,等.基于CatBoost算法的SAP混凝土抗压强度预测[J].内蒙古公路与运输,2023(5):1-6.
[40]	徐继伟,杨云.集成学习方法:研究综述[J].云南大学学报(自然科学版),2018,40(6):1082-1092.
[41]	文洁.MSE与MAE对机器学习性能优化的作用比较[J].信息与电脑(理论版),2018(15):42-43.