|Table of Contents|

Research on failure mode prediction of rectangular reinforced concrete columns based on machine learning(PDF)

《建筑科学与工程学报》[ISSN:1673-2049/CN:61-1442/TU]

Issue:
2025年02期
Page:
48-57
Research Field:
建筑结构
Publishing date:

Info

Title:
Research on failure mode prediction of rectangular reinforced concrete columns based on machine learning
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
Keywords:
reinforced concrete;  rectangular column;  machine learning;  failure mode prediction;  experimental data
PACS:
TU393
DOI:
10.19815/j.jace.2023.08007
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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Last Update: 2025-03-20