|本期目录/Table of Contents|

[1]张海,马小平,苏三庆,等.基于机器学习的钢筋混凝土矩形柱破坏模式预测研究[J].建筑科学与工程学报,2025,42(02):48-57.[doi:10.19815/j.jace.2023.08007]
　ZHANG Hai,MA Xiaoping,SU Sanqing,et al.Research on failure mode prediction of rectangular reinforced concrete columns based on machine learning[J].Journal of Architecture and Civil Engineering,2025,42(02):48-57.[doi:10.19815/j.jace.2023.08007]
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基于机器学习的钢筋混凝土矩形柱破坏模式预测研究(PDF)
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《建筑科学与工程学报》[ISSN:1673-2049/CN:61-1442/TU]

卷:
42卷
期数:
2025年02期
页码:
48-57
栏目:
建筑结构
出版日期:
2025-03-20

文章信息/Info

Title:
Research on failure mode prediction of rectangular reinforced concrete columns based on machine learning
文章编号:
1673-2049(2025)02-0048-10
作者:
张海1,2,马小平1,2,苏三庆3,王威3,蔡玉军1,2
(1. 中铁第一勘察设计院集团有限公司 极端环境岩土和隧道工程智能建养全国重点实验室,陕西 西安 710043; 2. 中铁第一勘察设计院集团有限公司 建筑与规划设计研究院/TOD研发中心,陕西 西安 710043; 3. 西安建筑科技大学 土木工程学院,陕西 西安 710055)
Author(s):
ZHANG Hai1,2, MA Xiaoping1,2, SU Sanqing3, WANG Wei3, CAI Yujun1,2
1.State Key Laboratory of Intelligent Construction and Maintenance for Extreme Geotechnical and Tunnel Engineering,China Railway First Survey and Design Institute Group Co.,Ltd.,Xi'an 710043,Shaanxi,China;2.Research Institute of Architectural&Planning Design/Research Center of Transit Oriented Development,China Railway First Survey and Design Institute Group Co.,Ltd.,Xi'an 710043,Shaanxi,China;3.School of Civil Engineering,Xi'an University of Architecture&Technology, Xi'an 710055,Shaanxi,China
关键词:
钢筋混凝土;  矩形截面柱;  机器学习;  破坏模式预测;  试验数据
Keywords:
reinforced concrete;  rectangular column;  machine learning;  failure mode prediction;  experimental data
分类号:
TU393
DOI:
10.19815/j.jace.2023.08007
文献标志码:
A
摘要:
针对传统分析方法识别效果差、数据依赖性强等问题,以既有试验数据为基础,建立矩形截面钢筋混凝土柱的数据库,应用K邻近、随机森林、支持向量机、梯度提升决策树、深度神经网络等机器学习算法,实现矩形柱破坏模式的有效识别与预测。借助机器学习强大的自学习、自适应能力,精准预测钢筋混凝土矩形柱的破坏模式,并为震后结构的维修加固与损伤评估提供依据。结果表明:机器学习技术对弯曲破坏均有良好的识别效果,随机森林和梯度提升决策树算法的准确率和回归率均达到100%,可用于矩形柱弯曲破坏模式的精准预测; 机器学习技术对于剪切破坏的识别效果差别不大,准确率均达66.67%,K邻近、支持向量机、梯度提升决策树的回归率最高,达到100%; 对于弯剪破坏模式,随机森林和梯度提升决策树的准确率最高,达到83.33%,支持向量机的预测效果较差。
Abstract:
Aiming at the problems of poor recognition effect and strong data dependence in traditional analysis methods, a database of rectangular reinforced concrete columns was established based on existing test data. The machine learning algorithms such as K-nearest neighbor, random forest, support vector machine, gradient boosting decision tree and deep neural network were applied to realize the effective recognition and prediction of failure modes for rectangular columns. By leveraging the powerful self-learning and self-adaptive ability of machine learning, the failure mode of rectangular reinforced concrete columns was accurately predicted, providing a basis for for the maintenance, reinforcement and damage assessment of post-earthquake structures. The results show that machine learning has a good recognition effect on bending failure. The accuracy and regression rate of random forest and gradient boosting decision tree are both up to 100%, and they can be used to accurately predict the bending failure mode of rectangular columns. The recognition effect of machine learning technology on shear failure is not significantly different, with an accuracy rate of 66.67%. The regression rates of the K-nearest neighbor, support vector machine, and gradient boosting decision tree are the highest, reaching 100%. For bending-shear failure mode, the accuracy of random forest and gradient boosting decision tree is the highest, reaching 83.33%, while the prediction effect of the support vector machine is poor.

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备注/Memo

备注/Memo:
收稿日期:2023-08-04
基金项目:中铁第一勘察设计院集团有限公司科研开发项目(2022KY51ZD(ZNJC)-03,2022KY54ZD(ZNXT)-05); 中国施工企业管理协会青年创新项目(2023-B-009); 中国铁路西安局集团有限公司科学技术研究开发计划项目(Y2023034)
作者简介:张 海(1967-),男,正高级工程师,E-mail:1911681566@qq.com。
通信作者:马小平(1991-),男,工学博士,高级工程师,E-mail:maxiaopin@live.xauat.edu.cn。
Author resumes: ZHANG Hai(1967-),male,senior engineer,E-mail:1911681566@qq.com; MA Xiaoping(1991-),male,PhD,senior engineer,E-mail:maxiaopin@live.xauat.edu.cn.
更新日期/Last Update: 2025-03-20