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

  • Vasilis Sarhosis University of Leeds
  • Tamas Forgacs 2Budapest University of Technology and Economics, H-1111 Budapest, Műegyetem rkp. 3, Hungary
  • Jose Lemos National Laboratory for Civil Engineering (LNEC), Lisbon, Portogal
Keywords: masonry, material, numerical modelling, monte-carlo simulations, discrete element method

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.

Published
2020-02-03
How to Cite
Sarhosis, V., Forgacs, T. and Lemos, J. (2020) “Stochastic strength prediction of masonry structures: a methodological approach or a way forward?”, RILEM Technical Letters, 40, pp. 122-129. doi: 10.21809/rilemtechlett.2019.100.
Section
Articles