# A BIM Based Framework for Damage Segmentation, Modeling, and Visualization Using IFC
## 2 Background
### Damage Data Acquisition  
[PERSON] et al. have proposed an image-based method to automatically detect spalled regions and related properties at reinforced concrete columns [26]. The region of spalling was first isolated by way of a local entropy-based thresholding algorithm, then the exposure of longitudinal reinforcement (i.e., depth of spalling in column) and length of spalling along the column were measured using a global adaptive thresholding algorithm, in conjunction with image processing methods for template matching and morphological operations. That method was tested on a set of images for damaged RC columns, indicating its validity against manual measurements.  
They later improved upon their work by adapting the aforementioned algorithms for spalling segmentation and property retrieval to sufficiently detect the absence of spalled regions on concrete surfaces, detecting transverse reinforcement and distinguishing it from longitudinal reinforcement [27]. Those enhancements enabled a better classification based on contextual information from depth retrieval pertaining to the amount and type of reinforcement, which falls into one of five respective categories, namely: no spalling, spalling of concrete cover, no exposure of reinforcement, spalling which exposes transverse reinforcement, spalling which exposes longitudinal reinforcement, and spalling of concrete, which exposes both transverse and longitudinal reinforcement.  
[PERSON] et al. developed an integrated model based on image processing techniques and machine learning to automate consistent spalling segmentation and numerical representation of distress in subway networks [28]. A hybrid algorithm including the support of regression analysis allows a prediction of spalling depth. A spalling processor detects distress attributes from noise-reduced images and creates 3D visualisation models of the defect. Subsequently, the regression analysis model and image processing techniques measure depth and severity of the spalling distress. The overall process and implementation were able to quantify the spalling depth in 75 images with an average validity of 93%.  
[PERSON] et al. proposed spalling segmentation method by analysing surface roughness reconstructed from point clouds acquired by laser scanning [29]. In the proposed method, points on ancillary facilities are filtered via circular scan-line fitting and large residual error filtering. A roughness descriptor was used to identify high rough patches. Next, high rough areas on the tunnel surface, such as bolt holes, or segment seams were filtered as well after classifying them using Hough transformation and similarity analysis to verify its classification. The remaining patches were presumed to be concrete spalling.  
[PERSON] et al. proposed another approach to identify image texture for features extraction [30]. Image textures obtained from statistical properties of colour channels, grey-level cooccurrence matrix, and grey-level run lengths were used as features to characterize surface condition of a concrete wall. These extracted features were classified into spalling and nonspalling classes.  
A Mask R-CNN architecture developed by Facebook AI Research [31] was utilised by [PERSON] and [PERSON] [32] to segment spalling images and automatically back-project the segmented spalling patches from image plane into 3D space using the available information about the pose of the camera for each captured image and its EXIF metadata as a 2D texture laid over host elements in the 3D information model.  
Besides manual and optical acquisition methods, Structural Health Monitoring (SHM) provides additional concepts and methods to gather damage data. SHM provides continuous measurements from structures and buildings, allowing inferences about the structural condition [33]. Problematic is the influence of environmental and operational variations to measurements [34].