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Towards Data Based Corrosion Analysis of Concrete with Supervised Machine Learning

  • 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 resistance data. Exact labels for changing environmental conditions were available in a laboratory study on a reinforced concrete slab, which deteriorated controlled and accelerated. TheHalf-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 resistance data. Exact labels for changing environmental conditions were available 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 SML results shows a significant improvement over the best single method (HP).zeige mehrzeige weniger

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Metadaten
Autor*innen:Sabine KruschwitzORCiD, Gino EbellORCiD, J. Shen
Dokumenttyp:Beitrag zu einem Tagungsband
Veröffentlichungsform:Graue Literatur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):Structural Materials Technology (SMT 2018) and the International Symposium Non-Destructive Testing in Civil Engineering
Jahr der Erstveröffentlichung:2018
Organisationseinheit der BAM:8 Zerstörungsfreie Prüfung
8 Zerstörungsfreie Prüfung / 8.0 Abteilungsleitung und andere
Erste Seite:1
Letzte Seite:7
DDC-Klassifikation:Naturwissenschaften und Mathematik / Chemie / Analytische Chemie
Freie Schlagwörter:Data based decision making; Data fusion; Half-cell potential mapping; Non-destuctive corrosion testing of concrete; Supervised machine learning
Themenfelder/Aktivitätsfelder der BAM:Chemie und Prozesstechnik
Veranstaltung:NDE/NDT for Highway and Bridges: Structural Materials Technology (SMT 2018) and the International Symposium Non-Destructive Testing in Civil Engineering (NDT-CE 2018)
Veranstaltungsort:New Brunswick, NJ, USA
Beginndatum der Veranstaltung:27.08.2018
Enddatum der Veranstaltung:28.08.2018
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:29.08.2018
Referierte Publikation:Nein
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