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Stochastic strength prediction of masonry structures: a methodological approach or a way forward?

Author(s):


Medium: journal article
Language(s): en 
Published in: RILEM Technical Letters, , v. 4
Page(s): 122-129
DOI: 10.21809/rilemtechlett.2019.100
Abstract:

Today, there are several computational models to predict the mechanical behaviour of masonry structures subjected to external loading. Such models require the input of material parameters to describe the mechanical behaviour and strength of masonry constructions. Although such masonry material parameters are characterised by stochastic-probabilistic nature, engineers are assigning the same material properties throughout the structure to be analysed. The aim of this paper is to propose a methodology which considers material spatial variability and stochastic strength prediction for masonry structures. The methodology is illustrated on a case study covering the in-plane behaviour of a low bond strength masonry wall panel containing an opening. A 2D non-linear computational model based on the Discrete Element Method (DEM) is used. The computational results are compared against those obtained from the experimental findings in terms of failure mode and structural capacity. It is shown that computational models which consider the spatial variability of masonry material properties better predict the load carrying capacity, stiffness and failure mode of the masonry structures. These observations provide new insights into structural behaviour of masonry constructions and lead to suggestions for improving assessment techniques for masonry structures.

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This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.

  • About this
    data sheet
  • Reference-ID
    10412149
  • Published on:
    08/02/2020
  • Last updated on:
    08/02/2020