Zitieren Sie bitte immer diesen URN: urn:nbn:de:kobv:b43-498563
Machine learning based multi-sensor fusion for the nondestructive testing of corrosion in concrete
- Half-cell potential mapping (HP) is the most popular non-destructive testing method (NDT) for locating corrosion damage in concrete. It is generally accepted that HP is susceptible to environmental factors caused by salt-related deterioration, such as different moisture and chloride gradients. Additional NDT methods are able to identify distinctive areas but are not yet used to estimate more accurate test results. We present a Supervised Machine Learning (SML) based approach to data fusion of seven different signal features to obtain higher quality information. SMLs are methods that explore (or learn) relationships between different (sensor) data from predefined data labels. To obtain a representative, labelled data set we conducted a comprehensive experiment simulating the deterioration cycle of a chloride exposed device in the laboratory. Our data set consists of 18 measurement campaigns, each containing HP, Ground Penetrating- Radar, Microwave Moisture and Wenner resistivity data.Half-cell potential mapping (HP) is the most popular non-destructive testing method (NDT) for locating corrosion damage in concrete. It is generally accepted that HP is susceptible to environmental factors caused by salt-related deterioration, such as different moisture and chloride gradients. Additional NDT methods are able to identify distinctive areas but are not yet used to estimate more accurate test results. We present a Supervised Machine Learning (SML) based approach to data fusion of seven different signal features to obtain higher quality information. SMLs are methods that explore (or learn) relationships between different (sensor) data from predefined data labels. To obtain a representative, labelled data set we conducted a comprehensive experiment simulating the deterioration cycle of a chloride exposed device in the laboratory. Our data set consists of 18 measurement campaigns, each containing HP, Ground Penetrating- Radar, Microwave Moisture and Wenner resistivity data. We compare the performance of different ML approaches. Many outperform the best single method, HP. We describe the intrinsic challenges posed by a data-driven approach in NDT and show how future work can help overcome them.…
Autor*innen: | T. Haller, Christoph VölkerORCiD, T. Hartmann |
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Dokumenttyp: | Zeitschriftenartikel |
Veröffentlichungsform: | Graue Literatur |
Sprache: | Englisch |
Titel des übergeordneten Werkes (Englisch): | The e-journal of nondestructive testing & ultrasonics |
Jahr der Erstveröffentlichung: | 2020 |
Organisationseinheit der BAM: | 8 Zerstörungsfreie Prüfung |
8 Zerstörungsfreie Prüfung / 8.0 Abteilungsleitung und andere | |
Veröffentlichende Institution: | Bundesanstalt für Materialforschung und -prüfung (BAM) |
Verlag: | NDT.net |
Verlagsort: | Kirchwald |
Jahrgang/Band: | 25 |
Ausgabe/Heft: | 1 |
Erste Seite: | 24890-1 |
Letzte Seite: | 24890-8 |
DDC-Klassifikation: | Naturwissenschaften und Mathematik / Chemie / Analytische Chemie |
Freie Schlagwörter: | Corrosion; Data Fusion; Half-Cell Potential Mapping; Machine Learning; NDT; Reinforced Concrete |
Themenfelder/Aktivitätsfelder der BAM: | Chemie und Prozesstechnik |
Veranstaltung: | SMAR2019 - 5th International Conference on Smart Monitoring, Assessment and Rehabilitation of Civil Structures |
Veranstaltungsort: | Potsdam, Germany |
Beginndatum der Veranstaltung: | 27.08.2019 |
Enddatum der Veranstaltung: | 29.08.2019 |
URN: | urn:nbn:de:kobv:b43-498563 |
URL: | http://www.ndt.net/?id=24890 |
Verfügbarkeit des Dokuments: | Datei für die Öffentlichkeit verfügbar ("Open Access") |
Lizenz (Deutsch): | Creative Commons - CC BY - Namensnennung 4.0 International |
Datum der Freischaltung: | 04.12.2019 |
Referierte Publikation: | Nein |
Schriftenreihen ohne Nummerierung: | Wissenschaftliche Artikel der BAM |