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LEHKÝ, D. ŠOMODÍKOVÁ, M.
Original Title
Small-sample artificial neural network based response surface method for reliability analysis of concrete bridges
Type
conference paper
Language
English
Original Abstract
In the paper, an artificial neural network based response surface method (ANN-RSM) in combination with a small-sample simulation technique is proposed. ANN as powerful parallel computational system is used for approximation of limit state function (LSF). Thanks to its ability to generalize it is efficient to fit LSF even with small number of simulations compared to polynomial RSM. Efficiency is emphasized by utilization of stratified simulation for selection of ANN training set elements. Proposed method is tested using simple limit state function taken from literature as well as employed for reliability and load-bearing capacity assessment of concrete bridge within the framework of fully probabilistic analysis. Results are compared with those obtained by other reliability methods.
Keywords
Artificial neural network, Response surface method, Probability of failure, Reliability index.
Authors
LEHKÝ, D.; ŠOMODÍKOVÁ, M.
RIV year
2015
Released
1. 1. 2015
Publisher
Taylor & Francis Group
Location
London, UK
ISBN
978-1-138-00120-6
Book
Proceedings of the Fourth International Symposium on Life-Cycle Civil Engineering (IALCCE 2014) – Life-Cycle of Structural Systems: Design, Assessment, Maintenance and Management
Pages from
1903
Pages to
1909
Pages count
7
BibTex
@inproceedings{BUT112148, author="David {Lehký} and Martina {Sadílková Šomodíková}", title="Small-sample artificial neural network based response surface method for reliability analysis of concrete bridges", booktitle="Proceedings of the Fourth International Symposium on Life-Cycle Civil Engineering (IALCCE 2014) – Life-Cycle of Structural Systems: Design, Assessment, Maintenance and Management", year="2015", pages="1903--1909", publisher="Taylor & Francis Group", address="London, UK", isbn="978-1-138-00120-6" }