Probabilistic Engineering Mechanics

Journal: Probabilistic Engineering Mechanics

Abbreviation

Probab. eng. mech.

Publisher

Elsevier

ISSN

0266-8920

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Publications 1 - 10 of 11

Metamodel-based importance sampling for structural reliability analysis
Dubourg, V.; Sudret, Bruno; Deheeger, F. (2013)
Probabilistic Engineering Mechanics
Reliability analysis of high-dimensional models using low-rank tensor approximations
Konakli, Katerina; Sudret, Bruno (2016)
Probabilistic Engineering Mechanics
Multivariate GP-VAR models for robust structural identification under operational variability
Avendaño Valencia, Luis David; Chatzi, Eleni (2020)
Probabilistic Engineering Mechanics
Seismic fragility analysis using stochastic polynomial chaos expansions
Zhu, Xujia; Broccardo, Marco; Sudret, Bruno (2023)
Probabilistic Engineering Mechanics
Within the performance-based earthquake engineering (PBEE) framework, the fragility model plays a pivotal role. Such a model represents the probability that the engineering demand parameter (EDP) exceeds a certain safety threshold given a set of selected intensity measures (IMs) that characterize the earthquake load. The-state-of-the art methods for fragility computation rely on full non-linear time–history analyses. Within this perimeter, there are two main approaches: the first relies on the selection and scaling of recorded ground motions; the second, based on random vibration theory, characterizes the seismic input with a parametric stochastic ground motion model (SGMM). The latter case has the great advantage that the problem of seismic risk analysis is framed as a forward uncertainty quantification problem. However, running classical full-scale Monte Carlo simulations is intractable because of the prohibitive computational cost of typical finite element models. Therefore, it is of great interest to define fragility models that link an EDP of interest with the SGMM parameters — which are regarded as IMs in this context. The computation of such fragility models is a challenge on its own and, despite a few recent studies, there is still an important research gap in this domain. This comes with no surprise as classical surrogate modeling techniques cannot be applied due to the stochastic nature of SGMM. This study tackles this computational challenge by using stochastic polynomial chaos expansions to represent the statistical dependence of EDP on IMs. More precisely, this surrogate model estimates the full conditional probability distribution of EDP conditioned on IMs. We compare the proposed approach with some state-of-the-art methods in two case studies. The numerical results show that the new method prevails over its competitors in estimating both the conditional distribution and the fragility functions.
Parametric hierarchical kriging for multi-fidelity aero-servo-elastic simulators — Application to extreme loads on wind turbines
Abdallah, Imad; Lataniotis, Christos; Sudret, Bruno (2019)
Probabilistic Engineering Mechanics
Uncertainty propagation of a multiscale poromechanics-hydration model for poroelastic properties of cement paste at early-age
Venkovic, N.; Sorelli, L.; Sudret, Bruno; et al. (2013)
Probabilistic Engineering Mechanics
Sparse polynomial chaos expansions of frequency response functions using stochastic frequency transformation
Yaghoubi, Vahid; Marelli, Stefano; Sudret, Bruno; et al. (2017)
Probabilistic Engineering Mechanics
A new system formulation for the tolerance analysis of overconstrained mechanisms
Dumas, A.; Gayton, N.; Dantan, J.-Y.; et al. (2015)
Probabilistic Engineering Mechanics
A unified framework for multilevel uncertainty quantification in Bayesian inverse problems
Nagel, Joseph B.; Sudret, Bruno (2016)
Probabilistic Engineering Mechanics
A general framework for data-driven uncertainty quantification under complex input dependencies using vine copulas
Torre, Emiliano; Marelli, Stefano; Embrechts, Paul; et al. (2019)
Probabilistic Engineering Mechanics

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