# BIM-Based Model Checking: A Scientometric Analysis and Critical Review
## 5 Challenges and Future Directions
### Future Directions
#### 5.2.4 Artificial Intelligence  
Artificial intelligence, particularly machine learning methods, has been demonstrated to effectively extract key information from complex texts and models for use in BMC [28; 84]. This technology relies on data and network architectures, requiring users to accumulate raw data and develop customized models based on specific demands. Legislative bodies and regulatory agencies related to BMC, rather than enterprises or individuals, are better positioned to access resources due to their higher authority and requirements. To implement rigorous workflows, improving data quality and designing robust network architectures are critical for enhancing the credibility of AI methods, thereby facilitating their broader adoption in the BMC field. The rules in the AEC field are primarily written in natural language, providing conditions for the application of NLP technologies in this domain. Many studies have demonstrated the feasibility and efficiency of using NLP technology for the rule extraction, translation, and expression. Over the years, research on language models has gone through the development of one-hot encoding, Word2 vec (Word to Vector), GPTs (generative pre-trained transformers), BERT (bidirectional encoder representations from transformers), and LLMs (large language models). Currently, the concept of deep learning is widely applied in the language processing. New language models have gradually improved their abilities to understand general texts. For example, ChatGPT 4.0 [186] can engage in multi-turn conversations and text generation. However, understanding and generating texts in specialized domains still require more specialized training and adjustments. Many excellent models and algorithms are open source, allowing BMC researchers to explore and extend them further to create more powerful language processing tools tailored for BMC applications. Natural Language Processing (NLP)  
To enhance the automation of BMC, machine learning methods such as NLP and graph neural networks (GNN) have been introduced. Most of these studies aimed to automatically extract information from rules and convert it into computer language. Existing NLP methods in the AEC industry can be divided into three aspects: text mining, feature extraction, and knowledge extraction. For example, [PERSON] et al. [187] conducted text mining on BIM-related texts from job advertisements, extracting keywords and co-occurrences to capture the required abilities and skills of BIM workers. [PERSON] et al. [96] extracted semantic information from safety rules to generate classes and entities of ontology, providing a corpus foundation for rule classification based on NLP. Such research expands the knowledge base of the AEC domain, demonstrating the potential of NLP methods in insight discovery. In addition, [PERSON] [28] introduced NLP to capture syntactic text features and automatically transform information from the \"International Building Code 2009\" (USA) into logical clauses. Similarly, [PERSON] [129] applied deep learning to automatically identify named entities about fall protection requirements from the \"Occupational Safety and Health Act (OSHA)\" (USA). These studies trained the models to learn features of the rules and then attempted to interpret the new rules. Additionally, some research has begun to adopt more complex frameworks and models, attempting to directly extract and construct knowledge from the rule libraries. For example, [PERSON] et al. [143] proposed using GNN to develop representation methods for encapsulating design and regulatory rules, expanding machine learning capabilities to address relationships between the building elements for checking the residential accessibility. [PERSON] [137] utilized knowledge graphs and NLP to convert rules from drawing checking into structured language, forming human-readable knowledge graphs to achieve BMC. Such research maps extracted information into structured knowledge designs.