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LIPOWCZAN, M. LEHKÝ, D. ŠOMODÍKOVÁ, M. NOVÁK, D.
Original Title
Study on reliability of prestressed concrete bridge using ANN-based inverse method
Type
article in a collection out of WoS and Scopus
Language
English
Original Abstract
The paper describes an application of artificial neural network-based inverse reliability method for reliability-based design of selected parameters of the concrete bridge. The design reliability level is determined using a fully probabilistic approach. The analysed structure is a single-span concrete bridge made of precast MPD3 and MPD4 girders post-tensioned by longitudinal as well as transversal tendons. According to diagnostic survey the bridge exhibits a spatial variability of deterioration which brings uncertainty into actual values of concrete strength in transverse joints and of actual loss of pre-stressing. Mean value and coefficient of variation of these two variables were considered as the design parameters with the aim of finding their critical values corresponding to desired reliability level and load-bearing capacity. Here, various load levels together with several values of mean tensile strength were considered.
Keywords
Inverse analysis, probability analysis, artificial neural networks, decompression limit state, crack limit state and normal load-bearing capacity.
Authors
LIPOWCZAN, M.; LEHKÝ, D.; ŠOMODÍKOVÁ, M.; NOVÁK, D.
Released
12. 9. 2018
Publisher
Wilhelm Ernst & Sohn
Location
Berlin
ISBN
1437-1006
Periodical
Beton- und Stahlbetonbau
Year of study
113
Number
S2
State
Federal Republic of Germany
Pages from
1
Pages to
6
Pages count
URL
https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1002%2Fbest.201800059&file=best201800059-sup-0001-suppinfo.pdf
BibTex
@inproceedings{BUT155480, author="Martin {Lipowczan} and David {Lehký} and Martina {Sadílková Šomodíková} and Drahomír {Novák}", title="Study on reliability of prestressed concrete bridge using ANN-based inverse method", booktitle="16th International Probabilistic Workshop", year="2018", journal="Beton- und Stahlbetonbau", volume="113", number="S2", pages="1--6", publisher="Wilhelm Ernst & Sohn", address="Berlin", issn="1437-1006", url="https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1002%2Fbest.201800059&file=best201800059-sup-0001-suppinfo.pdf" }