# Simplified Scan-vs-BIM Frameworks for Automated Structural Inspection of Steel Structures
## 3 Methodology  
The Distance-DNN-based Scan-vs-BIM framework proposed in this study aims to evaluate the integrity of the target structure based on direct comparison data between the As-Built 3D scan and As-Planned BIM. Evaluating the integrity of individual structural components is crucial for pinpointing specific areas that may require maintenance or repair. This level of granularity enables targeted interventions, reduces overall maintenance costs, and enhances the safety and reliability of the structure [12]. Traditionally, generating BIM models from point cloud data involves a Scan-to-BIM process where the point cloud is manually or semi-manually converted into a mesh model. This involves identifying and modeling structural elements by fitting geometric primitives (nodes, edges, or planes) to the point cloud data. However, the traditional process that relies on Scan-to-BIM in the Scan-vs-BIM framework causes the following problems: First, individual integrity evaluation of structures is not possible, and there is a lack of reliability in the generated structural models. Second, in the process of creating a point cloud-based mesh model through Scan-to-BIM, it shows slow speed and low efficiency due to large memory allocation. Third, the error type of the structure cannot be automatically determined. The Scan-vs-BIM framework uses As-Planned BIM and As-Built scan data. Comparing these two types of data in one format (mesh) reduces the efficiency of the Scan-vs-BIM framework. However, Distance-DNN, which analyzes the features through a direct comparison of the two types of data in an unmodified state, resolves the shortcomings of the traditional framework and can show strong performance and computational speed. Figure 1 shows the flowchart of the proposed Distance-DNN-based Scan-vs-BIM framework that performs structural integrity evaluation based on direct comparison data. The proposed framework consists of the following steps: (1) preprocessing, (2) Scan-vs-BIM, and (3) Distance-DNN. The framework evaluates the structural integrity of one single structural object per process and repeats the same process for all structural objects to review the integrity of the entire structure. The piecewise operation improves computational efficiency by breaking down the complex shape into smaller, more manageable sections, allowing for localized computations that require less memory and processing power. While our current implementation executes these operations sequentially, they can be parallelized to further enhance efficiency. Figure 1: Flowchart of simplified Scan-vs-BIM. In the preprocessing step, a parameter-based bounding space of a BIM object is created for single-structure evaluation. The bounding space becomes the reference for a single operation. It performs the function of separating the BIM shape model and sampling the points inside the bounding space. In the Scan-vs-BIM process, the geometric relationship data of the point cloud and BIM are collected. The method of constructing and comparing meshes through the Scan-to-BIM process of the traditional Scan-vs-BIM process requires a lot of computation; thus, the processing speed is slow. However, the framework of this study performs integrity evaluation by collecting only distance and index data, which are direct comparison data. In the Distance-DNN process, the tendency of distance and index data to perform a structural integrity evaluation function and a function to predict structural error types is analyzed. A DNN model was constructed to perform two functions. The proposed framework was implemented using Python programming language (version 3.8) due to its extensive libraries and tools for scientific computing. For the development and training of the Distance-DNN model, we utilized the TensorFlow deep learning library (version 2.4), which provides robust support for constructing and training neural networks.