Multi-parameter inverse analysis of concrete dams using kernel extreme learning machines-based response surface model

•A novel method for parameter inverse analysis of concrete dams based on an intelligent response surface model is presented.•Sample data of the proposed model are generated by Latin hypercube sampling.•The optimization algorithms are utilized to minimize the objective function for material parameter...

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Veröffentlicht in:	Engineering structures Jg. 256; S. 113999
Hauptverfasser:	Kang, Fei, Liu, Xi, Li, Junjie, Li, Hongjun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Kidlington Elsevier Ltd 01.04.2022 Elsevier BV
Schlagworte:	Algorithms Artificial neural networks Comparative studies Computation Concrete dams Cost analysis Dams Displacements Economic analysis Finite element method Gravity dams Hypercubes Inverse/back analysis Jaya optimization algorithm Kernel extreme learning machines Kernels Latin hypercube sampling Machine learning Mathematical models Objective function Optimization Parameter identification Response surface methodology Sampling Sampling methods Jaya optimization algorithm Inverse/back analysis Concrete dams Kernel extreme learning machines Displacements
ISSN:	0141-0296, 1873-7323
Online-Zugang:	Volltext
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