Detail publikačního výsledku

Study on reliability of prestressed concrete bridge using ANN-based inverse method

LIPOWCZAN, M.; LEHKÝ, D.; ŠOMODÍKOVÁ, M.; NOVÁK, D.

Original Title

Study on reliability of prestressed concrete bridge using ANN-based inverse method

English Title

Study on reliability of prestressed concrete bridge using ANN-based inverse method

Type

Paper in proceedings outside WoS and Scopus

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.

English 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.

Key words in English

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.

RIV year

2019

Released

12.09.2018

Publisher

Wilhelm Ernst & Sohn

Location

Berlin

Book

16th International Probabilistic Workshop

ISBN

1437-1006

Periodical

Beton- und Stahlbetonbau

Volume

113

Number

S2

State

Federal Republic of Germany

Pages from

1

Pages to

6

Pages count

6

URL

https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1002%2Fbest.201800059&file=best201800059-sup-0001-suppinfo.pdf

Full text in the Digital Library

http://hdl.handle.net/

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="0005-9900",
  url="https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1002%2Fbest.201800059&file=best201800059-sup-0001-suppinfo.pdf"
}