TY - JOUR A1 - Völker, Christoph A1 - Kruschwitz, Sabine A1 - Ebell, Gino T1 - A machine learning‑based data fusion approach for improved corrosion testing JF - Surveys in Geophysics N2 - This work presents machine learning-inspired data fusion approaches to improve the non-destructive 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 largescale 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. KW - Corrosion KW - Potential mapping KW - Machine learning PY - 2019 DO - https://doi.org/10.1007/s10712-019-09558-4 SN - 1573-0956 SN - 0169-3298 VL - 41 IS - 3 SP - 531 EP - 548 PB - Springer Nature AN - OPUS4-48799 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Baeßler, Matthias A1 - Babutzka, Martin A1 - Trappe, Volker A1 - Pittner, Andreas A1 - Krankenhagen, Rainer A1 - Kühne, Hans-Carsten T1 - Vorstellung des Aktivitätsfelds Erneuerbare Energie N2 - Auf der Beiratssitzung des TF Energie wurde das Aktivitätsfeld Erneuerbare Energien in seinem breiten Spektrum (aber selektive Auswahl) vorgestellt. T2 - Beiratssitzung TF Energie CY - BAM Berlin-Adlershof, Germany DA - 09.04.2019 KW - Erneuerbare Energien KW - Windenergie PY - 2019 AN - OPUS4-47751 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -