Abstract For structural health monitoring, electrical resistivity measurement (ERM) method is commonly employed for the detection of concrete's durability, as indicated by the chloride permeability and the corrosion of steel reinforcement. However, according to previous experimental studies, ERM results are susceptible to significant uncertainties due to multiple influencing factors such as concrete water/cement ratio and structure curing environment as well as their complex interrelationships. The present study therefore proposes an XGBoost algorithm-based prediction model which considers all potential influential factors simultaneously. A database containing 800 experimental instances composed of 16 input attributes is constructed according to existing reported studies and utilized for training and testing the XGBoost model. Statistical scores (RMSE, MAE and R2) and the GridsearchCV feature are applied to evaluate and optimize the established model respectively. Results show that the proposed XGBoost model achieves satisfactory predictive performance as demonstrated by high coefficients of regression fitting lines (0.991 and 0.943) and comparatively low RMSE values (4.6 and 11.3 kΩ·cm) for both training and testing sets respectively. The analyses of the attribute importance ranking also reveal that curing age and cement content have the greatest influence on ERM results.

中文翻译：

---

基于 XGBoost 算法的混凝土电阻率预测用于结构健康监测

摘要 对于结构健康监测，通常采用电阻率测量（ERM）方法来检测混凝土的耐久性，如氯化物渗透率和钢筋腐蚀。然而，根据以往的实验研究，由于混凝土水灰比和结构养护环境等多重影响因素及其复杂的相互关系，ERM结果容易受到重大不确定性的影响。因此，本研究提出了一种基于 XGBoost 算法的预测模型，该模型同时考虑了所有潜在的影响因素。根据现有报告的研究构建了一个包含 800 个由 16 个输入属性组成的实验实例的数据库，用于训练和测试 XGBoost 模型。应用统计分数（RMSE、MAE 和 R2）和 GridsearchCV 特征分别对建立的模型进行评估和优化。结果表明，所提出的 XGBoost 模型实现了令人满意的预测性能，如训练集和测试集的回归拟合线的高系数（0.991 和 0.943）和相对较低的 RMSE 值（4.6 和 11.3 kΩ·cm）所证明的那样。对属性重要性排名的分析还表明，养护龄期和水泥含量对 ERM 结果的影响最大。943) 和相对较低的 RMSE 值 (4.6 和 11.3 kΩ·cm) 分别用于训练和测试集。对属性重要性排名的分析还表明，养护龄期和水泥含量对 ERM 结果的影响最大。943) 和相对较低的 RMSE 值 (4.6 和 11.3 kΩ·cm) 分别用于训练和测试集。对属性重要性排名的分析还表明，养护龄期和水泥含量对 ERM 结果的影响最大。