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- Chemical analysis (1)
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We present the results of a machine learning (ML)- inspired data fusion approach, applied to multi-sensory nondestructive testing (NDT) data. Our dataset consists of Impact-Echo (IE), Ultrasonic Pulse Echo (US) and Ground Penetrating Radar (GPR) measurements collected on large-scale concrete specimens with built–in simulated honeycombing defects. In a previous study we were able to improve the detectability of honeycombs by fusing the information from the three different sensors with the density based clustering algorithm DBSCAN. We demonstrated the advantage of data fusion in reducing the false positives up to 10% compared to the best single sensor, thus, improving the detectability of the defects. The main objective of this contribution is to investigate the generality, i.e. whether the conclusions from one specimen can be adapted to the other. The effectiveness of the proposed approach on a separate full-scale concrete specimen was evaluated.
Laser-induced breakdown spectroscopy (LIBS) is a combination of laser ablation and optical emission spectroscopy. Due to the possibility of direct measurements on the sample surface with a minimum of sample preparation investigations of building materials can be conducted quite fast. In combination with a scanning technique (translation stage or scanning mirrors head) 2D element distributions of harmful species like chlorides and alkalis are evaluated with a sub-mm resolution. By scanning a surface of typically 100 mm x 100 mm the heterogeneity of the material may be considered. LIBS is capable to identify hot spots in element concentration with a resolution of 0.1 mm or even smaller. A LIBS-system for the on-site application on building materials is, in cooperation with system developers and companies which will use this technique for investigation on real structures, under test at BAM. We present an example. Further work will focus on providing guidelines to establish LIBS as a standard procedure for chemical investigations of building materials.