# Simplified Scan-vs-BIM Frameworks for Automated Structural Inspection of Steel Structures
## 6 Conclusions  
This study presents and evaluates Distance-DNN, a deep learning model developed for structural integrity evaluation by identifying features in As-Built scan data and comparing them with As-Planned BIM data. The Distance-DNN-based Scan-vs-BIM methodology proposed here diverges from traditional Scan-vs-BIM approaches, which typically involve constructing comparative models and processing them through complex transformations. Instead, the proposed framework integrates two distinct neural networks that perform integrity evaluation and error type analysis, offering a comprehensive assessment of structural integrity. Specifically, this study constructed a total of 26,500 real project datasets through augmentation of existing project data and generated an additional 65,000 virtual datasets within a 3D virtual environment featuring clearly defined structural errors. During the training process, the structural integrity evaluation (SIE) network achieved an accuracy of 95.77%, while the structural error type analysis (SETA) network attained an accuracy of 68.97%. The respective loss rates of the two deep neural networks were 0.03 and 0.04, reflecting minimal prediction errors. When applied to an actual steel structure, the trained Distance-DNN demonstrated a verified accuracy of 94.2% in structural integrity evaluation. Traditional Scan-vs-BIM approaches have posed challenges for real-time analysis due to their substantial computational demands [11]. In contrast, the Distance-DNN-based Scan-vs-BIM framework facilitates analysis by directly comparing As-Built scans with As-Planned models, obviating the need for complex data transformations. Consequently, this approach is computationally efficient, achieving a processing speed of 2.1 milliseconds per object, thereby enabling real-time structural integrity analysis. Additionally, the Distance-DNN framework is highly versatile and capable of evaluating the integrity of an object based on its As-Planned geometry, even in the absence of detailed BIM data. Given its robust computational speed and high accuracy, this framework holds significant potential for application in construction scenarios where structural integrity evaluation is critical. Despite these promising results, the study has several limitations that warrant further investigation. First, the current framework has been primarily validated on linear structural components, such as columns and beams. While it can generalize across various linear structural members, its applicability to non-linear or complex structural components--such as curved beams, slabs, free-form structures, and elements with variable cross-sections--remains unverified. The framework relies on consistent geometric properties for feature extraction, which may not be present in complex geometries. This limitation suggests that additional research is necessary to adapt the methodology for a broader range of structural elements. Second, the SETA network achieved a lower accuracy of 68.97% compared to the SIE network, indicating limitations in accurately classifying specific types of structural errors. This reduced accuracy could impact the effectiveness of targeted maintenance and repair strategies, as misclassification of error types may lead to inappropriate remedial actions. Enhancing the classification performance of the SETA network is therefore essential for the framework to provide more reliable diagnostic information. Third, although the study utilized a substantial amount of augmented and virtual datasets, the reliance on simulated data may not capture all the nuances of real-world structural variations and environmental factors. Factors such as sensor noise, environ mental conditions, and unforeseen structural anomalies present in actual construction sites might affect the model's performance. This potential gap could affect the model's generalizability when deployed in diverse construction scenarios, highlighting the need for more extensive real-world data collection and validation.