Bridge damage: Detection, IFC-based semantic enrichment and visualization

Application of Terrestrial Laser Scanning (TLS) in the Architecture, Engineering and Construction (AEC) Industry

“…Compared to other general techniques like a total station, the main advantage of terrestrial laser scanning is the fast collection of high-density points with x, y, z coordinates and RGB and intensity values, which can be further used in reconstructing a 3D model of objects in construction environments. With the development of TLS and modeling approaches, relevant studies have been conducted on the reconstruction of different objects, including buildings [ 49 , 51 , 53 , 54 , 58 , 60 , 61 , 65 , 70 , 72 ], civil infrastructure and its components (bridges [ 55 , 62 , 63 , 64 , 73 , 74 , 75 , 76 , 77 ], tunnels [ 50 ], precast concrete elements [ 71 , 79 ], removable floodwalls [ 56 ], bridge piers [ 66 ], pipe racks [ 52 ]), and construction sites [ 67 , 68 , 69 ]. The process of 3D model reconstruction using TLS can be classified into three main phases: (1) data collection, (2) data pre-processing, in which the critical task is to register multiple scans in a common coordinate system, and (3) modeling.…”

“…Previous researchers have developed different strategies and algorithms for identifying various objects (building components [ 42 , 47 , 70 , 88 , 92 ], bridge components [ 44 , 57 , 75 , 89 , 91 , 95 , 97 ], tunnel components [ 78 , 98 ], construction site [ 96 ], and construction equipment [ 68 ]). The typical approach in object recognition is to use shape descriptors.…”

“…In general, there are two common methods for surface flatness inspection that can be found in previous studies: (1) following quantitative indexes defined in relevant standards such as the F-numbers method [ 163 , 172 ]; or (2) setting up a reference plane and calculating the deviations between surface points and the reference plane [ 162 , 170 ]. On the other hand, the majority of papers on structural damage identification are focused on surface cracks [ 89 , 98 , 99 , 104 , 144 , 171 , 174 , 175 ], spalling [ 75 , 89 ], corrosion [ 161 ], water leakage [ 173 ], and concrete loss [ 160 , 166 ].…”

Digital Survey Ai and Semantics for Railway Masonry Bridges Health Assessment

“…In the context of structural analysis requirements, (Park et al, 2020) propose an extended IFC-based bridge information modeling method using a process to apply the meshfree structural analysis method to the IFCbased model. (Isailović et al, 2020) presented an approach for point cloud-based detection of spalling damage joined with a method for semantic enrichment of IFC model with damage semantics. (Ismail et al, 2017) improved the semantic quality of BIM models and link specific domain information from various domains with focus on bridge models based on the IFC standards.…”

A Deep Learning Approach Towards Railway Safety Risk Assessment

“…An image-based crack detection method was suggested to automate crack detection for safety and cost-effective bridge maintenance [113]. Additionally, the authors proposed automating the processes of bridge monitoring and maintenance for safe transportation infrastructure and compared the effectiveness of four crack-detection algorithms (wavelet, Fourier transforms, Sobel, and Canny [114]) for detecting healthy concrete surfaces [115], bridge damage [116] and corrosion detection [117]. Moreover, with the goal of automating concrete bridge decks inspections, a principal component analysis (PCA) algorithm was applied to mitigate the dimensionality problem of feature vectors to extract significant crack features from a database of bridge images [118].…”