# A BIM Based Framework for Damage Segmentation, Modeling, and Visualization Using IFC
## 7 Discussion and Outlook  
With novel technologies, numerous photos may be taken for bridge inspections. This study developed an open framework to acquire bridge data and facilitate inspection data for planning, analysis, and assessment. In the first step, the defect geometry is generated. A prior study with a small example used a point cloud of a single component to calculate and align defect geometry [64]. The presented case study contained more photos of a bigger structure. This led to a dense point cloud with too many vertices for further processing, thus, splitting up this point cloud was necessary. Generally speaking, the density of the point cloud is an important factor in processing and storing damage data.  
The proposed open framework is able to handle the geometric, semantic, and graphical data of defects. Furthermore, the DIM can accommodate various damage types effectively, like spalling, corrosion, divergences from specifications, and moisture penetration. Problematic defects are plant covering, missing components, and rock pockets. Due to missing concepts for modeling joints, the framework is not able to visualize damaged joints properly. Especially, joint defects are important in case of inspections [13]. Modeling joints and joint defects should be addressed in future research.  
With the information of planned reinforcement and existing defects, such as spalling, cracks, and moisture penetration, most of the planning of ultrasonic or impact-echo surveys can be done in office. However, survey results are included as reports only until now. The aim for future work is to incorporate the results of non-destructive testing in machine-readable formats.  
Mathematical simulations, such as probabilistic deterioration simulations [54], are written using MATLAB. The model provided in this paper is based on the IFC standard. Based on that, integrating a library to read IFC files would allow us to automatically use bridge condition ratings for probabilistic simulations. One remaining problem is that less inspection data is available in form of IFC files.  
Up until now, structural analyses are set up manually based on plans and inspection. However, incorporating damage data into IFC models allows automatic workflows for structural analysis. The damaged component geometry may be meshed automatically for subsequent calculations or is transferred into slabs, shells, and bars [52]. Similarly, an automation of analytical damage propagation would be possible.  
Finally, the visualization for assessment provides the engineer with available semantic, geometric, and graphical data. The engineer may observe component groups or the entire structure to gain an overview of problematic defects or components. However, decisions about the condition rating of the bridge lie within the responsibility of the engineer. This final decision requires deep knowledge of the existing norms, guidelines, and experience in impacts of defects. The framework aims to digitize the workflow of data acquisition and exchange, but leaves decisions to the engineer.  
Conceptualization: M.A.; methodology: M.A., M.S.H.A.; software: M.A., M.S.H.A.; validation, M.A., M.S.H.A.; writing--original draft preparation, M.A.; writing--review and editing: M.A., M.S.H.A., C.K.; visualization: M.A. and M.S.H.A.; supervision: C.K.; project administration: M.A.; funding acquisition: M.A. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by German Research Foundation (DFG, Bonn) and Bauhaus-Universitat Weimar within the program of Open Access Publishing.