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 水泥 腐蚀 粉煤灰 土壤盐分 腐蚀 多孔性 含水量 玄武岩纤维 复合材料 岩土工程 纤维 冶金 土壤科学 土壤水分 环境科学 地质学 古生物学
作者
Daming Luo,Fan Li,Ditao Niu
出处
期刊:Cement & Concrete Composites [Elsevier BV]
卷期号:150: 105531-105531 被引量:95
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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
自然函完成签到 ,获得积分10
刚刚
刚刚
hottie完成签到,获得积分10
刚刚
小宇完成签到,获得积分10
1秒前
v0id应助luo采纳,获得10
1秒前
耍酷橘子完成签到,获得积分20
1秒前
英俊的铭应助125mmD91T采纳,获得10
2秒前
2秒前
好吗好的完成签到,获得积分10
3秒前
什么东西这么好看完成签到,获得积分10
3秒前
4秒前
情怀应助静子采纳,获得10
4秒前
two_dog发布了新的文献求助10
4秒前
曾小荣发布了新的文献求助30
5秒前
文静元霜发布了新的文献求助10
5秒前
852应助少吃甜多健身采纳,获得10
6秒前
6秒前
hvacr123完成签到,获得积分10
6秒前
6秒前
7秒前
是你还是我11完成签到,获得积分10
7秒前
zhouyang发布了新的文献求助10
7秒前
bxj123完成签到,获得积分10
7秒前
小蘑菇应助孙友浩采纳,获得10
7秒前
erkk发布了新的文献求助10
8秒前
小文发布了新的文献求助10
8秒前
Yummy完成签到,获得积分10
8秒前
9秒前
achun完成签到,获得积分10
9秒前
无花果应助Tsuki采纳,获得10
10秒前
SciGPT应助文静元霜采纳,获得10
11秒前
酷波er应助玻尿酸采纳,获得200
11秒前
xiaobaiyang完成签到,获得积分10
11秒前
LX有理想完成签到 ,获得积分10
11秒前
明理的蛋挞完成签到,获得积分10
13秒前
Akim应助taotie采纳,获得10
13秒前
theThreeMagi完成签到,获得积分10
14秒前
dudu发布了新的文献求助10
14秒前
文艺的梦岚完成签到,获得积分10
14秒前
高分求助中
Markov Chain Monte Carlo 10000
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Common Foundations of American and East Asian Modernisation: From Alexander Hamilton to Junichero Koizumi 5000
How to Use Machine Learning in Chemistry: An Introduction 1000
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Discerning Saints: Moralization of Intrinsic Motivation and Selective Prosociality at Work 500
Handbuch Trainingswissenschaft – Trainingslehre 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7583093
求助须知:如何正确求助?哪些是违规求助? 9161776
关于积分的说明 19604859
捐赠科研通 7165133
什么是DOI,文献DOI怎么找? 3266207
关于科研通互助平台的介绍 2431164
邀请新用户注册赠送积分活动 2257518