Chloride-induced corrosion of steel reinforcing bar (rebar) is the primary cause of deterioration in reinforced concrete structures, posing a significant infrastructure challenge. The chloride threshold level (CTL) of rebar, which represents the critical amount of chloride needed to initiate active corrosion, is crucial in corrosion and service life prediction models. However, substantial uncertainties and a multitude of influencing factors, along with the absence of a universally accepted testing framework, hinder the achievement of a consistent CTL range for service life models and complicate comparisons of published values. This study addresses these challenges by developing multiple machine learning models to predict CTL, considering 21 carefully selected features. A comprehensive database of 423 data points was compiled from an exhaustive literature review. Seven machine learning models—linear regression, decision tree, random forest, K-nearest neighbors, support vector machine, artificial neural network, and an ensemble model—were developed and optimized. The ensemble model achieved superior prediction performance, with a mean absolute error of 0.218 % by weight of binder, root mean square error of 0.321 %, and a coefficient of determination of 0.751 on unseen CTL data. Partial dependence plots generated using the support vector machine model quantified the effect of each feature on CTL. The random forest model identified SiO₂ binder content and exposed rebar area to chlorides as the most influential factors. The study also examined the impact of supplementary cementitious materials (SCMs), finding that only blast furnace slag positively affected CTL.

中文翻译：

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探索机器学习以研究和预测碳钢加固的氯化物阈值水平


氯化物引起的钢筋腐蚀是钢筋混凝土结构劣化的主要原因，对基础设施构成了重大挑战。螺纹钢的氯化物阈值水平 （CTL） 代表引发主动腐蚀所需的氯化物临界量，在腐蚀和使用寿命预测模型中至关重要。然而，大量的不确定性和众多的影响因素，以及缺乏普遍接受的测试框架，阻碍了使用寿命模型实现一致的 CTL 范围，并使已发布值的比较复杂化。本研究通过开发多个机器学习模型来预测 CTL，并考虑了 21 个精心挑选的特征，从而解决了这些挑战。根据详尽的文献综述汇编了一个包含 423 个数据点的综合数据库。开发并优化了 7 个机器学习模型 — 线性回归、决策树、随机森林、K 最近邻、支持向量机、人工神经网络和集成模型。集成模型实现了卓越的预测性能，粘合剂重量的平均绝对误差为 0.218 %，均方根误差为 0.321 %，对看不见的 CTL 数据的决定系数为 0.751。使用支持向量机模型生成的部分依赖图量化了每个特征对 CTL 的影响。随机森林模型确定 SiO₂ 粘合剂含量和暴露于氯化物的钢筋面积是影响最大的因素。该研究还检查了补充胶凝材料 （SCM） 的影响，发现只有高炉炉渣对 CTL 有积极影响。