{"title":"Fire resistance of reinforced concrete columns: State of the art, analysis and prediction","authors":"Yuzhuo Wang, Zejian Liu, Xiao Zhang, Shuang Qu, Tiangui Xu","doi":"10.1016/j.jobe.2024.110690","DOIUrl":null,"url":null,"abstract":"Reinforced concrete (RC) columns are commonly used as the main load-bearing component of building structures owing to their excellent mechanical properties, and the fire resistance of RC columns has become a pivotal focus with the growing occurrence of building fires. In this paper, the fire resistance of 148 specimens in 89 references was first summarized, and 15 influencing parameters of fire resistance are obtained such as concrete cover thickness, load ratio and load eccentricity. The influence rules of the parameters, theoretical analysis, finite element (FE) analysis and fire resistance prediction formula of RC columns were summarized and analyzed. Secondly, the correlation analysis between 15 parameters and fire resistance was performed, and it was found that 7 parameters with a correlation coefficient higher than 0.15 can be considered as crucial parameters and used for the subsequent analysis. Furthermore, the prediction models of eight Machine Learning (ML) algorithms were established separately, and the Random Forest (RF) with high prediction accuracy ( is 0.95) was carefully chosen to build the database. Finally, the prediction formula of RC columns was proposed through non-linear regression analysis on the database, and it was observed that the formula with of 0.895 has high accuracy in evaluating fire resistance. The formula can be regarded as a reference for the fire resistance design of RC columns.","PeriodicalId":15064,"journal":{"name":"Journal of building engineering","volume":null,"pages":null},"PeriodicalIF":6.7000,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of building engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1016/j.jobe.2024.110690","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Reinforced concrete (RC) columns are commonly used as the main load-bearing component of building structures owing to their excellent mechanical properties, and the fire resistance of RC columns has become a pivotal focus with the growing occurrence of building fires. In this paper, the fire resistance of 148 specimens in 89 references was first summarized, and 15 influencing parameters of fire resistance are obtained such as concrete cover thickness, load ratio and load eccentricity. The influence rules of the parameters, theoretical analysis, finite element (FE) analysis and fire resistance prediction formula of RC columns were summarized and analyzed. Secondly, the correlation analysis between 15 parameters and fire resistance was performed, and it was found that 7 parameters with a correlation coefficient higher than 0.15 can be considered as crucial parameters and used for the subsequent analysis. Furthermore, the prediction models of eight Machine Learning (ML) algorithms were established separately, and the Random Forest (RF) with high prediction accuracy ( is 0.95) was carefully chosen to build the database. Finally, the prediction formula of RC columns was proposed through non-linear regression analysis on the database, and it was observed that the formula with of 0.895 has high accuracy in evaluating fire resistance. The formula can be regarded as a reference for the fire resistance design of RC columns.
期刊介绍:
The Journal of Building Engineering is an interdisciplinary journal that covers all aspects of science and technology concerned with the whole life cycle of the built environment; from the design phase through to construction, operation, performance, maintenance and its deterioration.