In-service performance assessment of fire-corrosion damaged cables of bridges

被引:11
作者
Feng, Jinpeng [1 ,2 ]
Li, Jinglun [1 ,2 ]
Gao, Kang [1 ,2 ]
Li, Yi [1 ,2 ]
Li, Tao [3 ]
Wu, Gang [1 ,2 ]
Zhao, Weigang [4 ]
机构
[1] Southeast Univ, Natl & Local Joint Engn Res Ctr Intelligent Constr, Nanjing, Peoples R China
[2] Southeast Univ, Minist Educ, Key Lab Concrete & Prestressed Concrete Struct, Nanjing, Peoples R China
[3] Xianyang Municipal Engn Adm, Xianyang, Peoples R China
[4] Shijiazhuang Tiedao Univ, State Key Lab Mech Behav & Syst Safety Traff Engn, Shijiazhuang 050043, Peoples R China
基金
中国国家自然科学基金;
关键词
In -service cable; Safety assessment; Combined corrosion and fire; Damage degree; Predictive models; STEEL;
D O I
10.1016/j.engstruct.2023.117221
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Cable-supported bridges are prone to fire hazards, and the corrosion within further compromises their structural integrity. The present study provides a novel framework for assessing fire-corrosion damage of steel wires currently in use. The relations and failure modes of steel wires under coupled effects of fire-corrosion are established by conducting the tensile test utilizing the replaced steel wires after the fire. Experimental studies found that the deterioration tendency of in-service steel wires after the fire is consistent with that of intact ones, while the corrosion level and position of steel wire have a significant impact on its ultimate strain. To predict the residual mechanical properties of cables after the fire-corrosion coupled damage, a comprehensive database of steel wires properties after the fire is established, and then a data-driven surrogate model is proposed. Finally, four grades are suggested to evaluate various damage degrees of in-service cables under corrosion and fire. The findings can provide bridge owners with guidance on how to replace or repair the damaged components after accidents or natural disaster, thereby increasing the reliability and lifespan of bridges and decreasing the costs associated with maintenance, reconstruction, and replacement.
引用
收藏
页数:15
相关论文
共 39 条
[1]   RAI: Rapid, Autonomous and Intelligent machine learning approach to identify fire-vulnerable bridges [J].
Abedi, M. ;
Naser, M. Z. .
APPLIED SOFT COMPUTING, 2021, 113
[2]  
[Anonymous], 2008, GB/T 22315-2008 Metallic Materials-Test Method for Elastic Modulus and Poisson's Ratio
[3]  
[Anonymous], 2021, ISO 8407:2021
[4]  
[Anonymous], 2015, 22822015 GBT
[5]  
[Anonymous], 2011, 22812010 GBT
[6]  
[Anonymous], 2011, Standard for Technical Condition Evaluation of Highway Bridges
[7]  
Bergstra J, 2012, J MACH LEARN RES, V13, P281
[8]   XGBoost: A Scalable Tree Boosting System [J].
Chen, Tianqi ;
Guestrin, Carlos .
KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2016, :785-794
[9]   Corrosion detection and evaluation for steel wires based on a multi-vision scanning system [J].
Dong, Yiqing ;
Pan, Yue ;
Wang, Dalei ;
Cheng, Tianzheng .
CONSTRUCTION AND BUILDING MATERIALS, 2022, 322
[10]   EXPERIMENTAL INVESTIGATION ON MECHANICAL PROPERTIES OF GRADE 1670 STEEL WIRES UNDER AND AFTER ELEVATED TEMPERATURE [J].
Du, Er-Feng ;
Hu, Xiao-Bo ;
Zhou, Zhong ;
Li, Qian ;
Lyu, Xiao ;
Tang, Yi-Qun .
ADVANCED STEEL CONSTRUCTION, 2023, 19 (01) :9-16