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Lookup NU author(s): Nicko Kassotakis, Dr Vasilis SarhosisORCiD, Professor Jon MillsORCiD
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
© 2020 Elsevier B.V.This paper presents a framework for the three-dimensional structural analysis of full scale, geometrically complex rubble masonry structures from point clouds generated from Structure-from-Motion photogrammetry or terrestrial laser scanning. According to the method, a point-based voxelization algorithm was adopted, whereby a dense point cloud was down-sampled into equidistant points, bypassing the need for conventional intensive processes, such as watertight mesh conversion, to obtain the geometric model of the rubble masonry for structural analysis. The geometry of the rubble masonry structure was represented by a sum of hexahedral rigid blocks (voxels). The proposed “point cloud to structural analysis” framework was implemented to assess the structural stability of the southwest leaning tower of Caerphilly Castle in Wales, UK. Simulations were performed with the three- dimensional computational software 3DEC, based on the Discrete Element Method (DEM) of analysis. Each voxel of the rubble masonry was represented as a rigid, distinct block while mortar joints were modelled as zero thickness interfaces which can open and close depending on the magnitude and direction of the stresses applied to them. The potential of the automated procedure herein proposed has been demonstrated to quantitatively assess the three-dimensional mechanical behaviour rubble masonry structures and provide valuable information to asset owners in relation to the structural health condition of assets in their care.
Author(s): Kassotakis N, Sarhosis V, Riveiro B, Conde B, D'Altri AM, Mills J, Milani G, de Miranda S, Castellazzi G
Publication type: Article
Publication status: Published
Journal: Automation in Construction
Year: 2020
Volume: 119
Print publication date: 01/11/2020
Online publication date: 25/07/2020
Acceptance date: 15/07/2020
Date deposited: 22/01/2021
ISSN (print): 0926-5805
Publisher: Elsevier B.V.
URL: https://doi.org/10.1016/j.autcon.2020.103365
DOI: 10.1016/j.autcon.2020.103365
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