Stochastic model construction of observed random phenomena
A method for constructing probabilistic models of non-stationary time dependent natural hazards is proposed. It is based on the use of Karhunen–Loève expansion and of a kernel estimator for the distribution of the multivariate random variables appearing in the expansion. The terms of the expansion a...
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| Veröffentlicht in: | Probabilistic engineering mechanics 2014-04, Vol.36, p.63-71 |
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| container_title | Probabilistic engineering mechanics |
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| creator | Poirion, Fabrice Zentner, Irmela |
| description | A method for constructing probabilistic models of non-stationary time dependent natural hazards is proposed. It is based on the use of Karhunen–Loève expansion and of a kernel estimator for the distribution of the multivariate random variables appearing in the expansion. The terms of the expansion and the distribution are identified from available measures. The approach is assessed through an academic example and is then applied to seismic ground motion modelling based on recorded data.
•A stochastic model for non-Gaussian and nonstationary random phenomena is proposed.•No a priori assumptions are introduced in the model.•The model probability distribution is derived explicitly.•The model is applied for the construction of seismic acceleration models. |
| doi_str_mv | 10.1016/j.probengmech.2014.03.005 |
| format | Article |
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| ispartof | Probabilistic engineering mechanics, 2014-04, Vol.36, p.63-71 |
| issn | 0266-8920 1878-4275 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1677941808 |
| source | Elsevier ScienceDirect Journals |
| subjects | Construction Estimators Ground motion Identification Kernel estimator Kernels Non-Gaussian process Non-stationary process Principal component analysis Probabilistic methods Probability theory Random variables Simulation |
| title | Stochastic model construction of observed random phenomena |
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