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Stochastic modelling and assessment of long-span precast prestressed concrete elements failing in shear

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F21%3APU140965" target="_blank" >RIV/00216305:26110/21:PU140965 - isvavai.cz</a>

  • Result on the web

    <a href="https://www.sciencedirect.com/science/article/pii/S0141029620341018" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0141029620341018</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1016/j.engstruct.2020.111500" target="_blank" >10.1016/j.engstruct.2020.111500</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Stochastic modelling and assessment of long-span precast prestressed concrete elements failing in shear

  • Original language description

    The shear behaviour of reinforced and prestressed concrete structures has been extensively studied over the last decades. However, there are still numerous open questions, concerning, e.g. the effects of normal-shear force interaction and material properties on shear performance. While the elastic behaviour of structures can be accurately captured by existing analytical approximations available within code standards, the description of the plastic behaviour of prestressed concrete elements occurring before typically quasi-brittle shear failure requires nonlinear analysis. Therefore, most prestressed concrete structures are designed to utilise only the elastic capacity of the material to avoid the performance of a complex nonlinear finite element analysis (hereinafter NLFEA) of pre-failure behaviour. In the case of mass-produced precast elements, however, the higher cost of performing NLFEA to provide valuable information on the complete loading of such element's history might be justified and economically beneficial. NLFEA can give much more objective information on a structure's performance and ultimate capacity, its cracking behaviour and failure indicators which can be utilised for the optimisation of the design, maintenance and inspection of produced structural elements. However, deterministic NLFEA cannot capture the naturally uncertain character of structural response. Current code standards provide a framework for NLFEA using several safety formats. The fully probabilistic approach remains the most general, straightforward and least conservative way of considering uncertainties, however. The stochastic modelling of a precast element's shear response requires the performance of a series of fracture-mechanical experiments with material samples, the evaluation of stochastic features of material parameters, and the use of identified random parameters as inputs for highly accurate nonlinear finite element models of destructive experiments. The information on mater

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    20102 - Construction engineering, Municipal and structural engineering

Result continuities

  • Project

    <a href="/en/project/GA17-02862S" target="_blank" >GA17-02862S: Probabilistic modelling and optimization of shear strength of concrete beams (PROMOSS)</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2021

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    ENGINEERING STRUCTURES

  • ISSN

    0141-0296

  • e-ISSN

    1873-7323

  • Volume of the periodical

    228

  • Issue of the periodical within the volume

    111500

  • Country of publishing house

    GB - UNITED KINGDOM

  • Number of pages

    16

  • Pages from-to

    1-16

  • UT code for WoS article

    000606747600004

  • EID of the result in the Scopus database