# A Network Analysis-Based Approach for As-Built BIM Generation and Inspection
## 1 Introduction  
The Building and Construction (B&C) industry is often cited as inefficient and unproductive due to the limited integration of Internet and Communication Technologies (ICT) [1], particularly the documentation and inspection of building structures, which was a tedious and laborious task during the 2D drawing era. The advancement of Computer-Aided Design (CAD) technology facilitates data interoperability among designers, constructors, and other stakeholders throughout the entire building lifecycle and also lays the foundation of Building Information Modelling (BIM), which has received a considerable amount of attention in the B&C industry and emerged as a core enabling technology for Construction 4.0 deployment [2]. As with the traditional as-designed BIM developed during the design phase, as-built BIM has also attracted increasing attention from both industry and academia, owing to the significance of efficient BIM-enabled maintenance or facility management for building assets that were built before the existence of BIM. Many researchers have invested in as-built BIM reconstruction because of its broad potential application prospects, such as the automatic reconstruction of structures [3] and the piping system [4] from photogrammetric information or point clouds.  
The increasing prevalence of as-built BIM studies necessitates the development of effective management and inspection methods to ensure a comprehensive management system for completed buildings. Efficient BIM-enabled functions for building assets thatwere built before the existence of BIM are highly dependent on the accuracy of as-built BIM. Such research aims to facilitate the analysis and management of as-built BIM, which is a fundamental problem arising in many BIM-based applications, such as facility management [5] and predictive maintenance [6]. Moreover, the advanced function of comparing as-designed and as-built BIMs also benefits from the improved accuracy and comprehensive analysis. Prior studies often relied on visual inspection, manual management, and a check of selective attributes [7; 8; 9; 10; 11; 12]. However, such methods are time-consuming and error-prone due to the large file sizes, complex inheritance, and referencing relationships of BIM models. Although some recent studies have been developed for automatic analysis and management methods based on machine learning, their methods are sensitive to parameter tuning and exert demands on computing resources, resulting in low scalability and robustness.  
To address these issues, an innovative approach based on network science is proposed for the analysis and management of as-built BIM. The proposed method starts with network component identification by extracting information from Industry Foundation Classes (IFC) files, followed by the establishment of networks for as-built or as-designed BIM. Next, topological analyses are conducted to provide more insight and reference parameters. Last, different networks are compared to identify any discrepancies or changes. Our method fully considers the correlation and relationships among building elements and effectively utilises the information from IFC files. As a result, the approach can improve comparison efficiency and reduce computational resource consumption, while its comparison process depends on network-related properties and is not sensitive to parameter tuning. Furthermore, the network analysis results are leveraged to derive management and modified recommendations for as-built BIM. Lastly, we also demonstrate potential applications of our approach in practical implementation.  
The rest of the paper is organised as follows: Section 2 presents the literature review of related works on BIM inspection, while the proposed solution is detailed in Section 3. Section 4 validates the solution using practical scanning data, followed by a discussion. Finally, Section 5 summarises the contributions and suggests future work.