Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
An uncertainty-informed Bayesian probabilistic framework for re-evaluating dynamic increase factors in progressive collapse of reinforced concrete frames. |
| Συγγραφείς: |
Jin, Yu1 (AUTHOR) YU008@e.ntu.edu.sg, Yu, Zecheng2 (AUTHOR) zecheng.yu@ntu.edu.sg, Li, Bing2 (AUTHOR) cbli@ntu.edu.sg |
| Πηγή: |
Engineering Structures. May2026, Vol. 354, pN.PAG-N.PAG. 1p. |
| Θεματικοί όροι: |
*Bayesian analysis, *Progressive collapse, *Simulation methods & models, *Mathematical optimization, *Reinforced concrete, *Uncertain systems, *Stochastic models |
| Εταιρία/Οντότητα: |
United States. Dept. of Defense |
| Περίληψη: |
Dynamic analysis accurately captures progressive collapse behavior but is computationally expensive. To improve efficiency, the U.S. DoD guidelines use a Dynamic Increase Factor (DIF) to approximate inertial effects. However, its empirical provision for RC frames is ambiguous and ignores structural uncertainties. This study proposes a scenario-specific model with a boundary correction parameter to simplify the DoD procedure and improve applicability for both slab-incorporated and bare frames. A Bayesian framework is then introduced to incorporate structural uncertainties, enabling a probabilistic reassessment of the DIF. Results show that although slab participation has limited influence on the mean DIF, it substantially reduces the impact of uncertainties. Posterior parameter estimates derived from 400 slab-incorporated frame simulations, generated from uncertainty-informed samples. This Bayesian framework successfully addresses the deterministic limitations of the DoD guidelines, producing mean predictions consistent with observations and a 95 % credible interval that captures nearly all observed data. For practical application, two design-oriented models are developed: a high-fidelity probabilistic analytical model that replicates Bayesian predictions at a lower computational cost, and a deterministic envelope model that conservatively bounds 90 % of observed DIF values. Both models provide efficient, reliable design tools that mitigate uncertainty-related risks using only deterministic data. • A scenario-specific estimation model reduces DoD computational complexity. • Slab-incorporated frames yield more stable DIF distributions than bare frames. • Bayesian framework quantifies uncertainties and improves DIF prediction accuracy. • Probabilistic analytical model reproduces Bayesian results at reduced cost. • Deterministic envelope model encloses 90 % of observed DIF values for design use. [ABSTRACT FROM AUTHOR] |
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