This work presents machine learning-inspired data fusion approaches to improve the nondestructive testing of reinforced concrete. The principal effects that are used for data fusion are shown theoretically. Their effectiveness is tested in case studies carried out on large-scale concrete specimens with built-in chloride-induced rebar corrosion. The dataset consists of half-cell potential mapping, Wenner resistivity, microwave moisture and ground penetrating radar measurements. Data fusion is based on the logistic regression algorithm. It learns an optimal linear decision boundary from multivariate labeled training data, to separate intact and defect areas. The training data are generated in an experiment that simulates the entire life cycle of chloride-exposed concrete building parts. The unique possibility to monitor the deterioration, and targeted corrosion initiation, allows data labeling. The results exhibit an improved sensitivity of the data fusion with logistic regression compared to the best individual method half-cell potential.

中文翻译：

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一种用于改进腐蚀测试的基于机器学习的数据融合方法

这项工作提出了受机器学习启发的数据融合方法，以改进钢筋混凝土的无损检测。理论上显示了用于数据融合的主要影响。它们的有效性在对具有内置氯化物引起的钢筋腐蚀的大型混凝土试样进行的案例研究中进行了测试。该数据集包括半电池电位映射、温纳电阻率、微波湿度和探地雷达测量。数据融合基于逻辑回归算法。它从多变量标记的训练数据中学习最佳线性决策边界，以分离完整区域和缺陷区域。训练数据是在模拟暴露于氯化物的混凝土建筑部件的整个生命周期的实验中生成的。监测恶化的独特可能性，和有针对性的腐蚀引发，允许数据标记。结果表明，与最佳个体方法半电池潜力相比，逻辑回归数据融合的灵敏度有所提高。