# Simplified Scan-vs-BIM Frameworks for Automated Structural Inspection of Steel Structures
## 3 Methodology
### Scan-vs-BIM  
Scan-vs-BIM collects geometric comparison data of point clouds and shapes. The distance between the sampled point cloud and the BIM shape is calculated to build the dataset. The traditional Scan-vs-BIM framework requires high computational performance due to the complex process of converting point cloud data into mesh models and performing detailed geometric comparisons with BIM models. This mesh generation and alignment process can take several hours per project, especially for large and complex datasets, and demands significant memory and processing power. In some cases, the calculations can take several days, depending on the scope of the structure. However, the proposed framework can conduct integrity evaluation using only 3D distance and index data. Index data refer to the normalized positional values of the sampled points along the structural object's length, derived from the BIM model's parameters. Specifically, for each point in the point cloud within the bounding space, we calculate its relative position along the centerline of the structural component. This index value provides a standardized measure of where each point lies along the length of the object. By combining distance data with these index values, the model can understand the spatial distribution of deviations along the component. Based on this method of transforming data structures, the algorithm can simplify the dimensional structure it recognizes, providing fast and predictable computational speed with fewer computational resources than existing frameworks [43].  
Figure 4 shows the geometric comparison method of Scan-vs-BIM based on Distance-DNN. First, the shortest distance was calculated in the 3D space of the point cloud data of As-Built scans using coordinates and As-Planned BIM data. The method of calculating the shortest distance between the point cloud data and the BIM shape uses the Closest Point Method (CPM) to find the target point on the shape closest to the origin point [44]. Then, the target-origin distance is calculated through the 3D coordinate system and stored in the 3D distance dataset.  
Figure 4: Scan-vs-BIM based on Distance-DNN.  
A deep learning model is trained by extracting features from data. If the data input to the model is scattered without criteria, it is difficult to achieve integrity evaluation. In general, features are maintained by adding baseline data [26]. In this study, the parameter index was collected by aligning the relative positions of the points with the model centerline of the BIM parameters. Through the collected index data, the tendency of point cloud and shape comparison data was identified.  
Finally, the process performs data normalization. Normalization is an operation in which all data are transformed to the same degree of scale. In this study, data were adjusted based on a predefined error range scale. By adding the 3D scanner's mechanical error range of \(\pm\)51 mm and the structure's error range of \(\pm\)13 mm, 64 mm was set as the normalization bound. The collected distance values were divided by the normalization bound and converted into float data between 0 and 1. Due to the nature of DNNs, float data between 0 and 1 must be input; thus, data larger than the range value are fixed to 1 (i.e., the maximum value of the input format).