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Predicting Changes of the State of a Bridge Reinforced with Concrete Superstructures in View of Operational Changes

Auteur(s): (Dep. “Bridges and Tunnels” , Dnipro National University of Railway Transport named after Academician V. Lazaryan)
(Dep. “Bridges and Tunnels” , Dnipro National University of Railway Transport named after Academician V. Lazaryan)
(Dep. “Bridges and Tunnels” , Dnipro National University of Railway Transport named after Academician V. Lazaryan)
(Dep. “Bridges and Tunnels” , Dnipro National University of Railway Transport named after Academician V. Lazaryan)
(Dep. “Basement and Foundations” , Prydniprovska State Academy of Civil Engineering and Architecture)
(Dep. “Basement and Foundations” , Prydniprovska State Academy of Civil Engineering and Architecture)
Médium: article de revue
Langue(s): anglais
Publié dans: Civil and Environmental Engineering Reports, , n. 3, v. 29
Page(s): 134-152
DOI: 10.2478/ceer-2019-0030
Abstrait:

To solve the problem of predicting the service life of superstructures, this work proposes the basis and methodological developments of creep theory with increasing loads as well as regression analysis of the results of laboratory experiments. The main limitation in terms of reinforcement is corrosion in the concrete cracks, which was determined during laboratory experiments. Based on the results, the approximate analytical dependences concerning reinforcement corrosion depth change over time at a constant value of crack width were selected. The paper substantiates the validity of the analytical dependences as a result of regression analysis; it proposes formulae for determining the corrosion rate of rebars in reinforced concrete superstructures. The obtained analytical dependences allowed for the developing of a process for predicting changes to the state of the superstructure in light of operational changes.

Structurae ne peut pas vous offrir cette publication en texte intégral pour l'instant. Le texte intégral est accessible chez l'éditeur. DOI: 10.2478/ceer-2019-0030.
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  • Reference-ID
    10705176
  • Publié(e) le:
    19.02.2023
  • Modifié(e) le:
    19.02.2023
 
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