# Simplified Scan-vs-BIM Frameworks for Automated Structural Inspection of Steel Structures
## 1 Introduction  
Integrity evaluation is fundamental for assessing the production quality and safety performance of construction projects, particularly in areas such as new construction, facility management, safety inspections, remodeling, and repairs [1]. Traditionally, integrity evaluation within the construction industry has been a manual process, reliant on the subjective judgment, observation, and expertise of skilled workers, which often results in incomplete and inaccurate reporting [2]. To mitigate these issues, recent research has focused on developing more efficient integrity evaluation methodologies through the application of information and communication technologies (ICTs) to transcend manual limitations [3]. Laser scanning has emerged as a widely adopted method for data collection in integrity evaluations for construction projects [4]. Typically, it is employed to reconstruct As-Built data, which is then used in \"As-Planned vs. As-Built\" comparison procedures [5]. Point cloud-based As-Built verification methods, in general, depend on manually designed models of real-world structures [6]. Consequently, there is an increasing demand for tools capable of automating the processing of As-Built data and enhancing \"As-Planned vs. As-Built\" comparison efficiency. In response, recent research efforts have concentrated on automating construction progress monitoring, leading to rapid advancements in associated theories and technologies [7]. A recent study has demonstrated that the \"Scan-vs-BIM\" framework, which compares As-Built scan data with As-Planned Building Information Modeling (BIM) models,performs effectively in terms of geometric comparison [8]. Both As-Built scan data and As-Planned BIM models used in the Scan-vs-BIM framework continue to evolve, while the associated input costs decrease [9]. These processes facilitate the assessment of the As-Built status of construction projects in a detailed, timely, and semantically accurate manner [10]. Furthermore, converting As-Built scan-based point cloud data into mesh models using the Scan-to-BIM framework, followed by comparison with As-Planned BIM models, has significantly improved the accuracy and automation of \"As-Planned vs. As-Built\" comparisons [11]. However, conventional Scan-to-BIM processes within the Scan-vs-BIM framework are associated with several challenges: (1) individual structural integrity evaluation is restricted, resulting in diminished reliability of structural models, and (2) the efficiency of the process is hindered by the slow speed and significant memory requirements associated with the creation of point cloud-based mesh models and the automatic determination of structural error types [12]. Additionally, comparing As-Planned BIM models with As-Built point clouds within the Scan-vs-BIM framework requires converting both data types into a unified format (mesh models), thereby reducing the overall efficiency of the framework [13]. Therefore, a solution that can directly compare these data types in their original states would greatly enhance the integrity evaluation process and address the shortcomings of the current framework. Recently, the application of deep learning to the construction industry has gained considerable traction, yielding substantial improvements over traditional frameworks [14]. Deep learning has been increasingly applied to address complex challenges in the construction industry, offering improvements in both speed and accuracy over traditional methods. [PERSON] et al. highlighted that deep learning models automate complex tasks such as structural health monitoring, construction site safety assessment, building occupancy modeling, and energy demand prediction [15]. Their analysis shows that deep learning automates complex tasks and efficiently processes large datasets, reducing the need for manual intervention and speeding up data processing. The model's ability to learn complex patterns and representations from data improves accuracy, providing better predictive performance than traditional methods.