Effect of external loads on chloride ingress into concrete: A state-of-the-art review

被引:6
作者
Guo, Bingbing [1 ,2 ]
Chu, Jia [1 ]
Zhang, Zhidong [3 ]
Wang, Yan [2 ]
Niu, Ditao [1 ,2 ]
机构
[1] Xian Univ Architecture & Technol, Sch Civil Engn, Xian 710055, Peoples R China
[2] Xian Univ Architecture & Technol, Natl Key Lab Green Bldg West China, Xian 710055, Peoples R China
[3] Swiss Fed Inst Technol, Inst Bldg Mat, CH-8093 Zurich, Switzerland
关键词
Concrete durability; Chloride transport; External loads; Influence mechanisms; Machine learning; MICRO-STRUCTURAL DAMAGE; DIFFUSION-COEFFICIENT; SURFACE CHLORIDE; TENSILE FATIGUE; ION DIFFUSION; SERVICE LIFE; COLD JOINT; TRANSPORT; PENETRATION; CORROSION;
D O I
10.1016/j.conbuildmat.2024.138657
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
This study presents a state-of-the-art review on the influence of external loads on chloride ingress into concrete, including compressive, tensile, flexural and fatigue loads. The macro-experimental phenomenon, micro-influence mechanism, analytical models, numerical simulation are critically reviewed, and the further trends are proposed. For compressive stress, there is a critical stress level, below which chloride transport is slowed down, and above which it is accelerated. The critical value ranges from 0.3 to 0.8. Tensile stress would accelerate chloride transport in concrete. The influence of bending loads on chloride transport depends on the local stress-strain state. For fatigue loads, their effect is determined by the accumulated damage of concrete. Overall, the influence mechanism of high stress levels on chloride transport is well understood from existing micro-CT experimental results. However, in order to elucidate the mechanisms by which low stress levels reduce chloride ingress, further research is needed to investigate the variations in small pores and narrow cracks in concrete caused by external loads. In addition, in view of the individual limitations of physical models and ML models, physics- guided deep learning may present a highly effective solution to establish an accurate and reliable platform for concrete durability assessment, as it harmoniously combines the formidable predictive power of ML with the explanatory power of traditional physical models.
引用
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页数:17
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