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On the Treatment of Measurement Uncertainty in Stochastic Modeling of Basic Variables
- The acquisition and appropriate processing of relevant information about the considered system remains a major challenge in assessment of existing structures. Both the values and the validity of computed results such as failure probabilities essentially depend on the quantity and quality of the incorporated knowledge. One source of information are onsite measurements of structural or material characteristics to be modeled as basic variables in reliability assessment. The explicit use of (quantitative) measurement results in assessment requires the quantification of the quality of the measured information, i.e., the uncertainty associated with the information acquisition and processing. This uncertainty can be referred to as measurement uncertainty. Another crucial aspect is to ensure the comparability of the measurement results.This contribution attempts to outline the necessity and the advantages of measurement uncertainty calculations in modeling of measurement data-based randomThe acquisition and appropriate processing of relevant information about the considered system remains a major challenge in assessment of existing structures. Both the values and the validity of computed results such as failure probabilities essentially depend on the quantity and quality of the incorporated knowledge. One source of information are onsite measurements of structural or material characteristics to be modeled as basic variables in reliability assessment. The explicit use of (quantitative) measurement results in assessment requires the quantification of the quality of the measured information, i.e., the uncertainty associated with the information acquisition and processing. This uncertainty can be referred to as measurement uncertainty. Another crucial aspect is to ensure the comparability of the measurement results.This contribution attempts to outline the necessity and the advantages of measurement uncertainty calculations in modeling of measurement data-based random variables to be included in reliability assessment. It is shown, how measured data representing time-invariant characteristics, in this case non-destructively measured inner geometrical dimensions, can be transferred into measurement results that are both comparable and quality-evaluated. The calculations are based on the rules provided in the guide to the expression of uncertainty in measurement (GUM). The GUM-framework is internationally accepted in metrology and can serve as starting point for the appropriate processing of measured data to be used in assessment. In conclusion, the effects of incorporating the non-destructively measured data into reliability analysis are presented using a prestressed concrete bridge as case-study.…
Autor*innen: | Stefan KüttenbaumORCiD, Stefan MaackORCiD, A. Taffe, T. Braml |
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Persönliche Herausgeber*innen: | M. Sykora, R. Lenner, N. de Koker |
Dokumenttyp: | Zeitschriftenartikel |
Veröffentlichungsform: | Verlagsliteratur |
Sprache: | Englisch |
Titel des übergeordneten Werkes (Englisch): | Acta Polytechnica |
Jahr der Erstveröffentlichung: | 2022 |
Organisationseinheit der BAM: | 8 Zerstörungsfreie Prüfung |
8 Zerstörungsfreie Prüfung / 8.2 Zerstörungsfreie Prüfmethoden für das Bauwesen | |
Veröffentlichende Institution: | Bundesanstalt für Materialforschung und -prüfung (BAM) |
Verlag: | Czech Technical University |
Verlagsort: | Prague |
Jahrgang/Band: | 36 |
Erste Seite: | 109 |
Letzte Seite: | 118 |
DDC-Klassifikation: | Naturwissenschaften und Mathematik / Chemie / Analytische Chemie |
Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurbau | |
Freie Schlagwörter: | Assessment; Existing structures; Measurement uncertainty; Reliability |
Themenfelder/Aktivitätsfelder der BAM: | Chemie und Prozesstechnik |
Infrastruktur | |
Veranstaltung: | International Probabilistic Workshop 2022 (IPW2022) |
Veranstaltungsort: | Stellenbosch, South Africa |
Beginndatum der Veranstaltung: | 09.09.2022 |
Enddatum der Veranstaltung: | 08.09.2022 |
DOI: | 10.14311/APP.2022.36.0109 |
URN: | urn:nbn:de:kobv:b43-556580 |
ISSN: | 2336-5382 |
Verfügbarkeit des Dokuments: | Datei für die Öffentlichkeit verfügbar ("Open Access") |
Lizenz (Deutsch): | Creative Commons - CC BY - Namensnennung 4.0 International |
Datum der Freischaltung: | 12.09.2022 |
Referierte Publikation: | Nein |
Schriftenreihen ohne Nummerierung: | Wissenschaftliche Artikel der BAM |