Detail publikačního výsledku

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

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

Originální název

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

Anglický název

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

Druh

Stať ve sborníku mimo WoS a Scopus

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.

Anglický 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.

Klíčová slova v angličtině

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.

Rok RIV

2019

Vydáno

12.09.2018

Nakladatel

Wilhelm Ernst & Sohn

Místo

Berlin

Kniha

16th International Probabilistic Workshop

ISSN

1437-1006

Periodikum

Beton- und Stahlbetonbau

Svazek

113

Číslo

S2

Stát

Spolková republika Německo

Strany od

1

Strany do

6

Strany počet

6

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