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An adaptive ANN‐based inverse response surface method
Author(s) -
Šomodíková Martina,
Lehký David
Publication year - 2018
Publication title -
beton‐ und stahlbetonbau
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.486
H-Index - 25
eISSN - 1437-1006
pISSN - 0005-9900
DOI - 10.1002/best.201800043
Subject(s) - artificial neural network , reliability (semiconductor) , inverse , computer science , nonlinear system , mathematical optimization , limit (mathematics) , surface (topology) , process (computing) , inverse problem , limit state design , algorithm , mathematics , artificial intelligence , engineering , geometry , mathematical analysis , power (physics) , physics , structural engineering , quantum mechanics , operating system
Abstract The paper deals with an adaptive artificial neural network‐based inverse response surface method utilized when performing reliability‐based design optimization of complex structural systems. Since calculating their reliability indicators (failure probabilities or reliability indices) is usually a time‐consuming task, the utilization of approximation methods with a view to reducing the computational effort to an acceptable level is an appropriate solution. A popular approximation method is the response surface method, where the limit state function is approximated using a suitable surrogate model. In this case, an artificial neural network is utilized. Construction of a response surface requires all variables of stochastic model to be known in advance. However, during the structural design, which is an inverse task, there are design parameters which are subject of reliability‐based design optimization procedure and thus not known at the start of the process. For such cases, an adaptive inverse response surface procedure is proposed. The procedure is based on a coupling of the adaptive response surface method and the artificial neural network‐based inverse reliability method. The validity and accuracy of the method is tested using example with explicit nonlinear limit state function. Obtained results as well as important aspects of the method are discussed.