Cumulative Failure Probability of Deteriorating Structures: Can It Drop?

  • The reliability of deteriorating structures at time t is quantified by the probability that failure occurs within the period leading up to time t. This probability is often referred to as cumulative failure probability and is equal to the cumulative distribution function of the time to failure. In structural reliability, an estimate of the cumulative failure probability is obtained based on probabilistic Engineering models of the deterioration processes and structural performance. Information on the condition and the loading contained in inspection and monitoring data can be included in the probability estimate through Bayesian updating. Conditioning the probability of failure on the inspection or monitoring outcomes available at time t (e.g. detections or no detection of damages) can lead to a reduction in that probability. Such a drop in the cumulative failure probability might seem counterintuitive since the cumulative failure probability is a non-decreasing function of time. InThe reliability of deteriorating structures at time t is quantified by the probability that failure occurs within the period leading up to time t. This probability is often referred to as cumulative failure probability and is equal to the cumulative distribution function of the time to failure. In structural reliability, an estimate of the cumulative failure probability is obtained based on probabilistic Engineering models of the deterioration processes and structural performance. Information on the condition and the loading contained in inspection and monitoring data can be included in the probability estimate through Bayesian updating. Conditioning the probability of failure on the inspection or monitoring outcomes available at time t (e.g. detections or no detection of damages) can lead to a reduction in that probability. Such a drop in the cumulative failure probability might seem counterintuitive since the cumulative failure probability is a non-decreasing function of time. In this paper, we illustrate—with the help of a numerical example—that such a drop is possible because the cumulative probability before and after the updating is not based on the same information, hence not on the same probabilistic model.zeige mehrzeige weniger

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Metadaten
Autoren/innen:Ronald SchneiderORCiD, D. Straub
Persönliche Herausgeber/innen:J.C. Matos, P.B. Lourenço, D.V. Oliveira, J. Branco, D. Proske, R.A. Silva, H.S. Sousa
Dokumenttyp:Beitrag zu einem Tagungsband
Veröffentlichungsform:Verlagsliteratur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):18th International Probabilistic Workshop. IPW 2020. Lecture Notes in Civil Engineering
Jahr der Erstveröffentlichung:2021
Organisationseinheit der BAM:7 Bauwerkssicherheit
7 Bauwerkssicherheit / 7.2 Ingenieurbau
Verlag:Springer
Verlagsort:Cham, Switzerland
Jahrgang/Band:153
Erste Seite:253
Letzte Seite:264
DDC-Klassifikation:Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurbau
Freie Schlagwörter:Bayesian updating; Deterioration; Inspection; Monitoring; Structural systems; Time-variant reliability
Themenfelder/Aktivitätsfelder der BAM:Infrastruktur
Infrastruktur / Sicherheit und Lebensdauer von Bauwerken
Veranstaltung:18th International Probabilistic Workshop (IPW 2020)
Veranstaltungsort:Online meeting
Beginndatum der Veranstaltung:12.05.2021
Enddatum der Veranstaltung:14.05.2021
DOI:https://doi.org/10.1007/978-3-030-73616-3_18
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:09.06.2021
Referierte Publikation:Nein