# BIM-Based Model Checking: A Scientometric Analysis and Critical Review
## 3 Scientometric Analysis
### Keyword Co-Occurrence Analysis  
The g-index of 100 and pruning options were selected in Citespace to generate a keyword co-occurrence network (Figure 8, by Citespace) and a keyword evolution network (Figure 9, by Citespace). The co-occurrence network consists of 217 nodes and 463 edges, and the node size is determined by the frequency of keywords in the articles. The linked nodes appeared in the same article. Due to the possibility of different expressions of the same keyword in different articles, the keywords in 145 articles were moderately integrated before the statistics. For example, \"building information modeling\" and \"BIM\" are merged into \"BIM\". The top ten most commonly used keywords are (frequency in parentheses) \"BIM (67)\", \"ACC (41)\", \"IFC (20)\", \"compliance checking (19)\", \"NLP (18)\", \"rule checking (17)\", \"building code (13)\", \"design (11)\", \"deep learning (11)\", and \"automated construction management systems (10)\". In addition, the line width of the purple ring represents the betweenness centrality of the keyword. There are 15 keywords with a betweenness centrality greater than 0.1, including (centrality in parentheses) \"design (0.46)\", \"compliance checking (0.40)\", \"BIM (0.39)\", \"building code (0.39)\", \"IFC (0.27)\", \"code checking (0.25)\", \"ACC (0.18)\", \"ARC (0.18)\", \"knowledge representation (0.17)\", \"CFG (0.15)\", \"Machine learning (0.14)\", \"Semantic web (0.13)\", \"NLP (0.12)\", and \"Design checking (0.12)\". Among them, \"BIM\", \"ACC\", and \"compliance checking\" are popular because they are relatively broad, and the BMC field is based on BIM and ACC. The high frequency of \"NLP\" and \"deep learning\" reflects the attention of artificial intelligence technology in the BMC field. In addition, the \"IFC\" has become the most common data standard in the BMC and even AEC fields. The frequency and centrality of \"design\" are both high, indicating that researchers in this field are more focused on the design phase, hoping that errors can be resolved as soon as possible rather than being discovered during or after the projects.  
Figure 8: The co-occurrence network of keywords.  
The co-occurrence network is static representation of the specific field, while the evolution network considers time factors to analyze changes in the research trends. Figure 9 shows the development of the keywords from 2008 to 2024. The year in which the keywords were first involved in model checking and the linked keywords indicate that they appeared in the same article. The top five keywords with the highest burst intensity are \"semantic systems (intensity = 1.92)\", \"automated construction management systems (intensity = 3.13)\", \"ARC (intensity = 2.77)\", \"NLP (intensity = 2.39)\", and \"design (intensity = 2.29)\". With the widespread application of BIM and the development of IFC, the keywords have evolved from \"design\" and \"compliance checking\" to \"BIM\", \"ACC\", and \"IFC\", etc. In recent years, keywords have evolved into \"NLP\", \"deep learning\", and \"ARC\", etc. With the development of artificial intelligence technology, the focus on BMC has shifted from traditional engineering management to new directions about intelligent technology.