Electrical performance of conductive cementitious composites under different curing regimes: Enhanced conduction by carbon fibers towards self-sensing function

被引:14
作者
Tian, Weichen [1 ,2 ]
Zhang, Zhanlin [1 ,3 ]
Qiu, Ruipeng [4 ]
Lu, Jian-Xin [2 ]
Li, Ruisen [1 ]
Liu, Yushi [1 ]
Wang, Wei [1 ]
机构
[1] Harbin Inst Technol, Sch Civil Engn, Harbin 150090, Peoples R China
[2] Hong Kong Polytech Univ, Dept Civil & Environm Engn, Hung Hom, Hong Kong, Peoples R China
[3] Dept Transportat Heilongjiang Prov, Harbin 150000, Peoples R China
[4] Hong Kong Polytech Univ, Dept Appl Math, Hung Hom, Kowloon, Hong Kong, Peoples R China
基金
中国国家自然科学基金;
关键词
Electrical conductivity; Self-sensing property; Ohmic heating curing; Winter concrete construction; MECHANICAL-PROPERTIES; BRICK MIXTURES; CONCRETE; FILLERS;
D O I
10.1016/j.conbuildmat.2024.135771
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Electrical performance is a significant indicator for conductive self-sensing cement-based composites, while the application of the composites is largely restricted in cold region due to the harsh environment. In this work, the electrical performance of carbon fibers reinforced conductive cement-based composites (CFR-CCBC) was evaluated, aiming to disclose the potential of ohmic heating (OH) cured conductive composites for structural health monitoring at cold region. Results showed that both OH curing and freezing condition were detrimental to the electrical conductivity, while the increase of CFs was an effective strategy to stabilize the electrical performance. However, the increasing CFs fraction was not economical and may induce strength degradation for concrete. At this time, the use of antifreeze could significantly stabilize the electrical conductivity by lowering CFs contents in CFR-CCBC with comparable electrical performance, even in severely cold environment. Piezoresistive property indicated that the combined use of 0.75 vol% CFs and antifreeze significantly increased gauge factor and linearity, indicating the improved self-sensing ability. Further, the fully connected neural network (FCNN) was employed to establish a model to systematically analysis the multiple factors on the electrical performance of CFR-CCBC.
引用
收藏
页数:17
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