Automated Defect Detection For Masonry Arch Bridges

The condition of masonry arch bridges is predominantly monitored with manual visual inspection. This process has been found to be subjective, relying on an inspection engineer’s interpretation of the condition of the structure. This paper initially presents a workflow that has been developed that can be used by a future automated bridge monitoring system to determine underlying faults in a bridge and suggest appropriate remedial action based on a set of detectable symptoms. This workflow has been used to identify the main classes of defects that an automated visual detection system for masonry should be capable of detecting. Subsequently, a convolutional neural network is used to classify these identified defect classes from images of masonry. As the mortar joints in the masonry are more distinctive than the defects being sought, their effect on the performance of an automated defect classifier is investigated. Compared to classifying all the regions of the masonry with a single classifier, it is found that where the mortar and brick regions have been classified separately, defect and defect free areas of the masonry have been predicted both with more confidence and with better accuracy

Crack detection limits in unit based masonry with terrestrial laser scanning

This paper presents the fundamental mathematics to determine the minimum crack width detectable with a terrestrial laser scanner in unit-based masonry. Orthogonal offset, interval scan angle, crack orientation, and crack depth are the main parameters. The theoretical work is benchmarked against laboratory tests using 4 samples with predesigned crack widths of 1-7 mm scanned at orthogonal distances of 5.0-12.5 m and at angles of 0 -30. Results showed that absolute errors of crack width were mostly less than 1.37 mm when the orthogonal distance varied 5.0-7.5 m but significantly increased for greater distances. The orthogonal distance had a disproportionately negative effect compared to the scan angle

Large Scale Experimental Settlement Tests to Evaluate Structural Models for Tunnelling-Induced Damage Analysis

Underground construction activities, such as tunnelling, cause local ground movements to occur. Nearby surface structures interact with the moving ground, potentially leading to building damage. Although it is understood that the severity of building damage is influenced by the façade opening ratio (OpR) and the stiffness of the floors, experimental work in this area is lacking. This paper describes the specification and design of an experimental campaign on brick masonry buildings subjected to vertical base movements. The specimens are half-scale models of walls of two-storey buildings; models with different window arrangements and with/without floor slabs are examined. To design the experimental setup, 3D finite element analyses of the model walls were conducted. Key analysis results, presented in this paper, indicate how the examined structural properties (OpR, building weight, floor stiffness) are expected to influence the patterns of damage in the masonry. The finite element results are also used to design an instrumentation system comprising Fibre Bragg Grating (FBG) sensors and a digital image correlation (DIC) system. Data from the tests will support the formulation and validation of structural models for predicting tunnelling-induced damage in masonry buildings.Accepted Author ManuscriptApplied MechanicsGeo-engineerin