RESEARCH ARTICLE
An Enhanced Beam Model for the Analysis of Masonry Walls
Massimiliano Lucchesi1, Barbara Pintucchi1, *, Nicola Zani1
Article Information
Identifiers and Pagination:
Year: 2019Volume: 13
First Page: 52
Last Page: 66
Publisher ID: TOBCTJ-13-52
DOI: 10.2174/1874836801913010052
Article History:
Received Date: 29/11/2018Revision Received Date: 23/01/2019
Acceptance Date: 17/02/2019
Electronic publication date: 28/03/2019
Collection year: 2019
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Background:
Some typologies of masonry constructions (e.g. towers or walls with openings) can be reasonably studied through simple beam or frame-like models. For these structures, shear mechanisms often play an important role inducing failure and collapse.
Objective:
The paper presents an enriched beam model for studying the in-plane response of masonry walls. Initially formulated for masonry columns, towers and masonry slender structures in general, the model is now modified in order to also capture the shear failure mechanisms, in addition to the flexural ones.
Methods:
Starting with a one-dimensional no-tension model, a strength domain in the plane of the axial and tangential stress of the beam has been added, which has been defined by limiting both the stress shear component with respect to any possible direction and the main compressive stress.
Results:
The model, implemented in the FEM computational code MADY, allows for short computational times in studying the response of single panels as well as walls with openings.
Conclusion:
Comparisons of some experimental results from literature and some numerical results from more refined 2D models show the effectiveness and accuracy of the model’s predictions in terms of global and local response.
