# BIM-Based Model Checking: A Scientometric Analysis and Critical Review
## 4 Critical Review
### Application Cases of Platforms and Projects
#### 4.4.3 Practical Cases  
BMC has established a foundational theoretical framework and demonstrated significant value and potential across various subfields in the AEC domain. It has been applied to diverse project types, such as residential buildings, infrastructure, and energy engineering, to improve the efficiency and accuracy of BIM model checking. Most studies focus on the design phase of projects, where errors in BIM model creation are more likely to emerge. During this stage, BMC addresses conflicts between regulatory rules and models created based on the intentions of designers. Many studies have shown that the practical application and value of BMC currently lie in its feasibility and efficiency. On one hand, it can extract machine-readable rules and knowledge from extensive regulatory texts, replacing manual efforts. For example, [PERSON] and [PERSON] [130] used knowledge graphs to represent relationships in construction safety regulations concerning fall protection, including general fall protection, fall protection systems, guardrail systems, and positioning device systems. Tested across 20 sections, their method proved effective in automatically identifying and classifying domain-specific relationships in unstructured texts, minimizing the need for manual intervention. Similarly, [PERSON] and [PERSON] [83] developed a more accurate POS tagger for building rules, enabling automated compliance checking systems to handle more regulatory requirements with a precision of 95.11%. Additionally, [PERSON] et al. [98] proposed an automated rule interpretation method based on Chinese building codes for fire protection, achieving accuracies of 99.6% for simple sentences and 91.0% for complex sentences. Overall, most studies indicate that, by integrating NLP techniques and text analysis methods, BMC has been successfully employed for rule interpretation. Specifically, for extracting and translating large volumes of complex textual information, BMC significantly outperforms manual methods in efficiency. On the other hand, automated BIM model checking has been adopted by some researchers and governments to improve workflows in digital engineering reviews. For instance, [PERSON] et al. [46] tested compliance checking in GIS using four synthetic scenarios, including the compliance of underground utility burial depths, pipeline spacing, and staff permissions. [PERSON] and [PERSON] [55] developed a system to check wall compliance in residential buildings based on the International Building Code (IBC), achieving a recall rate of 98.7%. Additionally, [PERSON] et al. [57] used the International Exhibition Center as a case study to assess its compliance with building environmental data, such as air pollutant sensors, temperature ranges, and light values. Many findings of the studies suggest that, compared to manual methods for reviewing regulatory documents, NLP-based approaches are more intelligent. By leveraging prior knowledge, such as syntactic and semantic features, these methods extract critical information from unstructured data and formalize it for spatial reasoning, significantly enhancing compliance review processes. The applications of BMC are not yet widespread, and many specific rules and project requirements in the AEC domain cannot be met by current software or platforms. Therefore, some developers offer customized or user-defined modules to meet diverse needs. The updating and optimization of these systems are still ongoing. Unfortunately, some BMC systems have ceased development, including SMARTcodes and UpCodes AI. In addition, although some new systems have emerged in recent years, such as Solar Automated Permit Processing Plus (SolarAPP+) [184] in 2021, the focus of most systems is narrow. In recent years, no more powerful system has been found to comprehensively replace the previous ones. Developers and users are more focused on efforts to extend and customize the capabilities of existing systems.