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dc.contributor.authorNagesh M.
dc.contributor.authorBalu A.S.
dc.date.accessioned2021-05-05T10:15:57Z-
dc.date.available2021-05-05T10:15:57Z-
dc.date.issued2020
dc.identifier.citationLecture Notes in Mechanical Engineering , Vol. , , p. 267 - 273en_US
dc.identifier.urihttps://doi.org/10.1007/978-981-15-5432-2_23
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/14897-
dc.description.abstractReliability-based design of complex structural systems is a computationally tedious task. In order to reduce the computational effort, approximation methods, such as classical response surface method, Kriging model and artificial neural network, can be adopted. Response surface model is a conventional method, where the limit state function is approximated using a suitable surrogate model. For the construction of response surface, variables of stochastic model should be known well in advance. However, the design parameters are unknown during initial stages of reliability-based design optimization (RBDO). For such structural design cases using RBDO, an adaptive inverse response surface procedure is proposed in this paper. The procedure is developed by coupling the adaptive response surface method with suitable experimental design (Halton low-discrepancy sequence sampling) for estimating reliability indicators and artificial neural network-based inverse reliability method for design optimization. The validity and accuracy of the proposed method are tested on example with explicit nonlinear limit state function. © Springer Nature Singapore Pte Ltd 2020.en_US
dc.titleInverse response surface method for structural reliability analysisen_US
dc.typeConference Paperen_US
Appears in Collections:2. Conference Papers

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