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Half-Cell-Potential Mapping (HP) is the most popular non-destructive testing (NDT) method for the detection of active corrosion in reinforced concrete. HP is influenced by parameters such as moisture and chloride gradients in the component. The sensitivity to the spatially small, but dangerous pitting is low. In this study we show how additional measurement information can be used with multi-sensor data fusion to improve the detection performance and to automate data evaluation. The fusion is based on supervised machine learning (SML). SML are methods that recognize relationships in (sensor) data based on given labels. We use SML to distinguish "defective" and "intact" labeled areas in our dataset. It consists of 18 measurement - each contains HP, ground radar, microwave moisture and Wenner resistivity data. Exact labels for changing environmental conditions were determined in a laboratory study on a reinforced concrete slab, which deteriorated controlled and accelerated. The deterioration progress was monitored continuously and corrosion was generated targeted at a predefined location. The detection results are quantified and statistically evaluated. The data fusion shows a significant improvement over the best single method (HP). We describe the challenges of data-driven approaches in nondestructive testing and show possible solutions.
Non-destructive testing methods are available in civil engineering for decades to estimate concrete properties or to detect flaws and features. But recently we have seen the dawn of next-generation tools, methods, and applications. Some of them will be discussed in the web talk: – Better tools: deeper and more detailed insight into concrete constructions – Better methods: Quantitative use in probabilistic structural assessment – Better rules: Towards standardization, qualification, and certification – Better application: Digitalization and Elimination of the boundaries between NDT, SHM, and BIM: NDT-CE 4.0 Not enough? I might show, how cosmic rays might become a game-changer in NDT-CE. This live webinar record was provided by https://eurostruct.org