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.

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Despite the crucial role of structural health monitoring (SHM), traditional contact-based sensors are costly and lacking automation. Vision-based sensors have recently emerged as a potential substitute, due to their non-contact nature and low cost. However, investigations lack validating their long-term performance. This study assesses vision-based sensing for robust and efficient long-term displacement monitoring. Additionally, the suitability of various machine learning (ML) algorithms in automatically extracting information from the generated data are examined. A first experiment examines the capability to monitor ambiently excited structures over long periods. The parameters assessed are: a) noise and drift (e.g., related to environmental changes such as temperature, humidity, and lighting); and b) obtainable displacement resolution related to ambient excitation. Then, a second experiment examines the capability to monitor dynamically excited structures over long periods (via the employment of an artificial excitation, e.g., an eccentrically loaded mass motor). Here, the following parameters assessed are the: a) frequency bandwidth range; b) frequency resolution; c) displacement resolution related to dynamic excitation; and d) maximum number monitored points for given computational resources. Finally, for processing the generated data, the following ML algorithms are examined: a) Artificial Neural Networks (ANNs); b) support vector machines (SVMs); c) decision trees; and d) Convolutional Neural Networks (CNNs). Concerning long-term monitoring, the paper found that non-contact sensing had comparable accuracy with traditional contact-based sensors. About the ML models, the CNNs were found to be advantageous. Whilst this study was carried out on a small-scale structure, the potential of vision-based sensing for long-term monitoring of full-scale structures was highlighted.

This paper presents “Image2DEM”, a framework for the semi-automated structural analysis of arches. Numerical models of arch specimens are semi-automatically developed from Structure-from-Motion (SfM) photogrammetry, by employing image processing techniques (IPTs) to bypass laborious CAD-based processes. Then, the numerical models of the proposed framework are assessed by comparison to a conventional CAD-based framework in terms of: a) geometrical agreement (i.e., joint and block properties); and b) structural capacity agreement (i.e., stiffness, load multipliers and normal forces between joints at each hinge formation). Results firstly demonstrated SfM photogrammetry as an effective avenue of structural surveying for numerical modelling. After, the numerical models developed from the proposed and conventional framework were demonstrated to have a good agreement in terms of geometry. Furthermore, good agreement was also found in the predicted structural capacity, with differences in structural capacity of less than 7% Although only the potential to capture both the geometry and structural capacity of small-scale arch structures was demonstrated here, this study lays the foundation for automated geometry acquisition for arches for their structural analysis.