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

Authors :
Guo, Bingbing
Chu, Jia
Zhang, Zhidong
Wang, Yan
Niu, Ditao
Source :
Construction & Building Materials. Nov2024, Vol. 450, pN.PAG-N.PAG. 1p.
Publication Year :
2024

Abstract

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. • Critical compressive stress level that affects chloride ingress is between 0.3 and 0.8. • Influence of bending loads on chloride ingress depends on local stress-strain state. • Variations in small pores and narrow cracks under external loads need to be further studied. • Physics-guided ML may offer an effective solution to establish a reliable platform for concrete durability assessment. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09500618
Volume :
450
Database :
Academic Search Index
Journal :
Construction & Building Materials
Publication Type :
Academic Journal
Accession number :
180423136
Full Text :
https://doi.org/10.1016/j.conbuildmat.2024.138657