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Communication Dans Un Congrès Année : 2017

Computation of Seismic Fragility Curves Using Artificial Neural Network Metamodels

Résumé

In earthquake engineering, the fragility curve is defined as the conditional probability of failure of a structure, or its critical components, at given values of seismic intensity measures (IMs). For simulation-based fragility curve estimations, this conditional probability of failure is usually computed with log-normal assumption. The artificial neural network (ANN) is used to improve the computational efficiency of the simulation-based fragility analysis. An ANN metamodel is built from 100 finite element soil-structure-interaction simulation results. The most relevant IMs are selected based on semi-partial correlation coefficients. The ANN metamodel is trained with the selected IMs, and a large number of Monte Carlo simulations are performed with this metamodel. Fragility curves are computed with both parametric (log-normal model) and non-parametric methods for the estimation of the risk of failure of an electrical cabinet in a reactor building studied in the framework of the KARISMA benchmark.
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Dates et versions

hal-01716960 , version 1 (09-03-2018)

Identifiants

  • HAL Id : hal-01716960 , version 1

Citer

Zhiyi Wang, Nicola Pedroni, Irmela Zentner, Enrico Zio. Computation of Seismic Fragility Curves Using Artificial Neural Network Metamodels. 12th International Conference on Structural Safety and Reliability (ICOSSAR 2017), Aug 2017, Vienne, Austria. pp.1525-1534. ⟨hal-01716960⟩
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