Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 118210 |
| Fachzeitschrift | Engineering structures |
| Jahrgang | 312 |
| Frühes Online-Datum | 23 Mai 2024 |
| Publikationsstatus | Veröffentlicht - 1 Aug. 2024 |
Abstract
This study introduces a novel point selection procedure for the Probability Density Evolution Method (PDEM) to estimate time-dependent reliability and failure probabilities in dynamic systems under first-passage failure conditions. The method integrates and modifies features of the Subset simulation procedure to adaptively generate dependent sample sets suitable for a reliability analysis by a direct probability integration approach levering PDEM. Performance function assessments are used as weighting factors in the Subset supported Point Selection (S-PS), enhancing the ultimate failure probability estimation accuracy. The presented approach effectively identifies samples in the failure region, particularly benefiting for dynamic systems under stochastic excitation, tested with random dimensions up to 60. It also offers a computationally efficient structural reliability estimation procedure by analyzing full time–history responses. The proposed method provides deeper insights into rare failure events and mechanisms through the visualization of intermediate results. This research presents an advanced framework for estimating structural reliability and understanding critical events in dynamic systems.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Tief- und Ingenieurbau
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in: Engineering structures, Jahrgang 312, 118210, 01.08.2024.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Efficient reliability analysis of stochastic dynamic first-passage problems by probability density evolution analysis with subset supported point selection
AU - Bittner, Marius
AU - Broggi, Matteo
AU - Beer, Michael
N1 - Publisher Copyright: © 2024 The Author(s)
PY - 2024/8/1
Y1 - 2024/8/1
N2 - This study introduces a novel point selection procedure for the Probability Density Evolution Method (PDEM) to estimate time-dependent reliability and failure probabilities in dynamic systems under first-passage failure conditions. The method integrates and modifies features of the Subset simulation procedure to adaptively generate dependent sample sets suitable for a reliability analysis by a direct probability integration approach levering PDEM. Performance function assessments are used as weighting factors in the Subset supported Point Selection (S-PS), enhancing the ultimate failure probability estimation accuracy. The presented approach effectively identifies samples in the failure region, particularly benefiting for dynamic systems under stochastic excitation, tested with random dimensions up to 60. It also offers a computationally efficient structural reliability estimation procedure by analyzing full time–history responses. The proposed method provides deeper insights into rare failure events and mechanisms through the visualization of intermediate results. This research presents an advanced framework for estimating structural reliability and understanding critical events in dynamic systems.
AB - This study introduces a novel point selection procedure for the Probability Density Evolution Method (PDEM) to estimate time-dependent reliability and failure probabilities in dynamic systems under first-passage failure conditions. The method integrates and modifies features of the Subset simulation procedure to adaptively generate dependent sample sets suitable for a reliability analysis by a direct probability integration approach levering PDEM. Performance function assessments are used as weighting factors in the Subset supported Point Selection (S-PS), enhancing the ultimate failure probability estimation accuracy. The presented approach effectively identifies samples in the failure region, particularly benefiting for dynamic systems under stochastic excitation, tested with random dimensions up to 60. It also offers a computationally efficient structural reliability estimation procedure by analyzing full time–history responses. The proposed method provides deeper insights into rare failure events and mechanisms through the visualization of intermediate results. This research presents an advanced framework for estimating structural reliability and understanding critical events in dynamic systems.
KW - First-passage failure probability
KW - Probability density evolution method
KW - Reliability analysis
KW - Stochastic processes
KW - Stochastic structural dynamics
UR - http://www.scopus.com/inward/record.url?scp=85194034473&partnerID=8YFLogxK
U2 - 10.1016/j.engstruct.2024.118210
DO - 10.1016/j.engstruct.2024.118210
M3 - Article
VL - 312
JO - Engineering structures
JF - Engineering structures
SN - 0141-0296
M1 - 118210
ER -