Study on the deterioration of concrete performance in saline soil area under the combined effect of high low temperatures, chloride and sulfate salts

硫酸盐 材料科学 氯化物 土壤盐分 生理盐水 复合材料 岩土工程 冶金 土壤科学 土壤水分 环境科学 地质学 医学 内分泌学
作者
Daming Luo,Fan Li,Ditao Niu
出处
期刊:Cement & Concrete Composites [Elsevier]
卷期号:150: 105531-105531
标识
DOI:10.1016/j.cemconcomp.2024.105531
摘要

Concrete structures in saline soil regions are prone to degradation due to chloride and sulfate erosion, compounded by the concurrent influences of drying, high and low temperatures, and freeze-thaw cycles. This study establishes a simulation test system for complex saline soil environments, integrating findings from real-world environmental investigations. The investigation focused on the degradation mechanism of concrete under the combined impacts of dry-wet and high-low temperature cycles, coupled with composite salt erosion. Additionally, the impacts of water-cement ratio, fly ash content, and basalt fiber content on concrete's mechanical properties and ion erosion resistance were analyzed. The alterations in the internal pore structure of corroded concrete were examined through nuclear magnetic resonance (NMR) technology. Utilizing the XGBoost algorithm, a predictive model for chloride and sulfate ion concentrations in concrete, under the combined influence of dry-wet and high-low temperature cycles, coupled with composite salt erosion, was developed. The findings reveal that the rate of concrete deterioration is gradually accelerating under the combined erosion to dry-wet cycles, high-low temperature cycles, and composite salt. Optimal fly ash and basalt fiber dosages for corrosion resistance are determined to be 10% and 0.10%, respectively. During advanced erosion stages, concrete porosity, capillary and macropore volume fractions increase, while gel pore volume fraction declines significantly. The XGBoost-based chloride and sulfate concentration prediction model demonstrates strong agreement with experimental measurements, yielding correlation indices of R2 = 0.98 and 0.97, respectively. Interpretation results obtained using SHAP from the machine learning model align with experimental outcomes.
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