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PAN, L. NOVÁK, D. NOVÁK, L.
Original Title
Surrogate modelling of concrete girders using artificial neural network ensemble
Type
conference paper
Language
English
Original Abstract
Surrogate model as approximation model is widely applied in engineering application, perform-ing sufficient amount of simulations for a fully probabilistic design of computationally demanding tasks. There are several types of new surrogate modelling techniques in last decades. Herein, artificial neural network en-semble (ANNE) is employed for surrogate modelling, which is very efficient method as will be shown on analytical example. Moreover, a comparison of traditional single artificial neural network approach and ANNE will be presented as well. Main part of the paper will be focused on application of ANNE for stochastic analysis of concrete girders represented by mathematical model in form of nonlinear finite element model. Therefore, in the last part of the paper, results obtained by ANNE will be presented and discussed.
Keywords
Artificial Neural Network, Surrogate modeling, probabilistic design
Authors
PAN, L.; NOVÁK, D.; NOVÁK, L.
Released
15. 12. 2020
Location
Shanghai, China
ISBN
9780429343292
Book
Life-Cycle Civil Engineering: Innovation, Theory and Practice. Proceedings of the 7th International Symposium on Life-Cycle Civil Engineering (IALCCE 2020), October 27-30, 2020, Shanghai, China
Pages from
1
Pages to
5
Pages count
BibTex
@inproceedings{BUT169150, author="Lixia {Pan} and Drahomír {Novák} and Lukáš {Novák}", title="Surrogate modelling of concrete girders using artificial neural network ensemble", booktitle="Life-Cycle Civil Engineering: Innovation, Theory and Practice. Proceedings of the 7th International Symposium on Life-Cycle Civil Engineering (IALCCE 2020), October 27-30, 2020, Shanghai, China", year="2020", pages="1--5", address="Shanghai, China", doi="10.1201/9780429343292-162", isbn="9780429343292" }