建筑结构学报2025，Vol.46Issue(4)：198-209,12.DOI:10.14006/j.jzjgxb.2024.0285
基于机器学习的受压混凝土氯离子传输预测模型
Prediction model for chloride ion transport in compressed concrete based on machine learning
摘要
Abstract
Machine learning prediction model has gradually become an important method to study the chloride transport behavior in concrete,and existing machine learning prediction models have not considered the effect of load on the chloride transport.Thus,machine learning prediction model for chloride transport in concrete under compressive loading is developed.Based on the experimental research in the existing literature,a database containing 2 458 samples has been established.After evaluating the performance of six machine learning prediction models with different machine algorithms,it is found that ML model based on XGBoost algorithm is most suitable for the chloride transport model in compressive concrete.Furthermore,SHAP method is combined into the XGBoost model to analyze the interpretability of machine learning prediction.The results show that apart from exposure time,stress level has the second most effect on the chloride transport.In addition,the transport rate of chloride in concrete decreases first and then increases with increasing compressive stress level.Compared with the analytical model of Fick's second law,the established ML model can accurately predict the peak of the chloride concentration,and thereby it can predict the chloride transport behaviors in concrete better.关键词
受压混凝土/氯离子传输/机器学习/应力水平/可解释性Key words
compressed concrete/chloride ion transport/machine learning/stress level/interpretability分类
建筑与水利引用本文复制引用
郭冰冰,陈楠,李京钊,王艳,张永利,牛荻涛..基于机器学习的受压混凝土氯离子传输预测模型[J].建筑结构学报,2025,46(4):198-209,12.基金项目
国家自然科学基金青年基金项目(51908453),深圳市承接国家重大科技项目产业化应用研究项目(CJGJZD20220517141806015). （51908453）
