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LIPOWCZAN, M. LEHKÝ, D. ŠOMODÍKOVÁ, M. NOVÁK, D.
Originální název
Study on reliability of prestressed concrete bridge using ANN-based inverse method
Typ
článek ve sborníku mimo WoS a Scopus
Jazyk
angličtina
Originální abstrakt
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.
Klíčová slova
Inverse analysis, probability analysis, artificial neural networks, decompression limit state, crack limit state and normal load-bearing capacity.
Autoři
LIPOWCZAN, M.; LEHKÝ, D.; ŠOMODÍKOVÁ, M.; NOVÁK, D.
Vydáno
12. 9. 2018
Nakladatel
Wilhelm Ernst & Sohn
Místo
Berlin
ISSN
1437-1006
Periodikum
Beton- und Stahlbetonbau
Ročník
113
Číslo
S2
Stát
Spolková republika Německo
Strany od
1
Strany do
6
Strany počet
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" }