TY - GEN A1 - Völker, Christoph T1 - Feasibility study on adapting a machine learning based multi-sensor data fusion approach for honeycomb detection in concrete N2 - 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. T2 - NDE/NDT for Highway and Bridges: Structural Materials Technology 2016 CY - Portland, Oregon, USA DA - 29.08.2016 KW - Data fusion KW - Concrete evaluation KW - Honeycombing KW - Machine learning KW - Clustering PY - 2016 UR - https://opus4.kobv.de/opus4-bam/frontdoor/index/index/docId/38289 AN - OPUS4-38289 AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany