TY - JOUR A1 - Völker, Christoph A1 - Shokouhi, P. T1 - Data aggregation for improved honeycomb detection in concrete using machine learning-based algorithms JF - The e-journal of nondestructive testing & ultrasonics N2 - We present the results of several machine learning (ML)- inspired data fusion algorithms applied to multi-sensory nondestructive testing (NDT) data. Our dataset consists of Impact-Echo (IE), Ultrasonic Pulse Echo (US) and Ground Penetrating Radar (GPR) data collected on large-scale concrete specimens with built–in simulated honeycombing defects. The main objective is to improve the detectability of honeycombs by fusing the information from the three different sensors. We describe normalization, feature detection and optimal feature selection. We have used unsupervised and supervised ML, i.e., classification and clustering, for data fusion. We demonstrate 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 methods were evaluated on a concrete specimen. The effectiveness of the proposed approach was demonstrated on a separate full-scale concrete specimen. The results indicate the transportability of the conclusions from one specimen to the other. T2 - NDT-CE 2015 - International symposium non-destructive testing in civil engineering CY - Berlin, Germany DA - 15.09.2015 KW - Data fusion KW - Concrete evaluation KW - Honeycombing KW - Machine learning KW - Clustering PY - 2015 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-350968 UR - https://www.ndt.net/?id=18364 SN - 1435-4934 VL - 20 IS - 11 SP - 1 EP - 8 PB - NDT.net CY - Kirchwald AN - OPUS4-35096 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Heideklang, René A1 - Shokouhi, P. T1 - Decision-level fusion of spatially scattered multi-modal data for nondestructive inspection of surface defects JF - Sensors N2 - This article focuses on the fusion of flaw indications from multi-sensor nondestructive materials testing. Because each testing method makes use of a different physical principle, a multi-method approach has the potential of effectively differentiating actual defect indications from the many false alarms, thus enhancing detection reliability. In this study, we propose a new technique for aggregating scattered two- or three-dimensional sensory data. Using a density-based approach, the proposed method explicitly addresses localization uncertainties such as registration errors. This feature marks one of the major of advantages of this approach over pixel-based image fusion techniques. We provide guidelines on how to set all the key parameters and demonstrate the technique's robustness. Finally, we apply our fusion approach to experimental data and demonstrate its capability to locate small defects by substantially reducing false alarms under conditions where no single-sensor method is adequate. KW - Multi-sensor data fusion KW - Density estimation KW - Scattered data KW - Defect detection KW - Nondestructive testing KW - Registration errors PY - 2016 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-352977 DO - https://doi.org/10.3390/s16010105 SN - 1424-8220 VL - 16 SP - Article Number: 105 PB - MDPI CY - Basel, Switzerland AN - OPUS4-35297 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -