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Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Improved active learning probabilistic approach for the computation of failure probability
Highlights An improved Kriging-based approach is proposed for failure probability estimation. The proposed approach is a variant of AK-MCS method where some improvements were introduced. The proposed approach is more efficient than the AK-MCS approach in terms of the computation time. The present approach is of great interest for geotechnical problems involving soil spatial variability.
Abstract This paper presents a cost-effective probabilistic approach to be used in engineering applications. The proposed approach consists of an improved Kriging-based method aiming at reducing to a minimum the number of evaluations of the true performance function when computing a failure probability. It is a kind of variant of the classical Active learning method combining Kriging and Monte Carlo Simulation (AK-MCS) developed by Echard et al. (2011) [1], where some improvements are introduced to enhance the learning process. Some illustrative and practical examples are presented and discussed. The proposed approach has shown a great efficiency as compared to the classical AK-MCS approach.
Improved active learning probabilistic approach for the computation of failure probability
Highlights An improved Kriging-based approach is proposed for failure probability estimation. The proposed approach is a variant of AK-MCS method where some improvements were introduced. The proposed approach is more efficient than the AK-MCS approach in terms of the computation time. The present approach is of great interest for geotechnical problems involving soil spatial variability.
Abstract This paper presents a cost-effective probabilistic approach to be used in engineering applications. The proposed approach consists of an improved Kriging-based method aiming at reducing to a minimum the number of evaluations of the true performance function when computing a failure probability. It is a kind of variant of the classical Active learning method combining Kriging and Monte Carlo Simulation (AK-MCS) developed by Echard et al. (2011) [1], where some improvements are introduced to enhance the learning process. Some illustrative and practical examples are presented and discussed. The proposed approach has shown a great efficiency as compared to the classical AK-MCS approach.
Improved active learning probabilistic approach for the computation of failure probability
El Haj, Abdul-Kader (Autor:in) / Soubra, Abdul-Hamid (Autor:in)
Structural Safety ; 88
27.08.2020
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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