# BIM-Based Model Checking: A Scientometric Analysis and Critical Review
## 3 Scientometric Analysis
### Cluster Analysis  
Cluster analysis can organize domain knowledge through statistics of data, which helps to classify topics and analyze the interrelationships between research. First, a reference network was generated in Citespace using a g-index of 50 and pruning options, and then a title-content (T) clustering method based on the LLR algorithm was used [167]. The references of the 145 articles were identified as 15 cluster labels, each representing a focus topic of the cluster. The clustering results unrelated to BMC were removed, and the final 10 cluster labels were retained, as shown in Figure 13 (by Citespace). The node represents the literature, and the size of the node indicates its frequency of citation. The most representative three articles in each cluster are marked, and the two linked articles have been co-cited by other articles. Different colors represent different clustering results, with blue indicating earlier clusters and yellow indicating later clusters. The red arrows indicate dependencies between the clusters, and the cluster at the tail of the arrow has developed from the cluster which the arrow points towards. For example, cluster 10 developed from clusters 1 and 4, indicating that clusters 1 and 4 provide a foundation of knowledge for the research of cluster 10. Figure 12: The co-citation network of literatures. (Refs. [1; 5; 8; 9; 17; 19; 20; 21; 24; 25; 27; 32; 34; 44; 50; 51; 52; 57; 59; 62; 64; 68; 76; 78; 119]). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
The two most cited articles in each cluster based on their co-citation frequency are listed. The silhouette score reflects the average homogeneity of the cluster [170]. Clustering results are considered reliable when the silhouette score exceeds 0.7. The largest cluster is \"Automatic rule-based checking\" with 92 references, while the smallest is \"Natural language generation\" with 48 references. \begin{table}
\begin{tabular}{p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt}} \hline
**Cluster ID** & **Size** & **Silhouette Score** & **Cluster Label** & **Time Span** & **Most Cited Article** & **Journal** & **DOI** \\ \hline
1 & 92 & 0.946 & Automatic rule-based checking & 1966–2020 & [5] & J. Struct. Div. & doi:10.1061/JSDEAG.0001567 \\ \hline
2 & 78 & 0.934 & Transformational rule & 1992–2022 & [40] & J. Comput. Civ. Eng. & doi:10.1061/(ASCE)CP.1943-5487.0000536 \\ \hline
3 & 77 & 0.926 &
\begin{tabular}{} \end{tabular} & 1956–2022 & [78] & Autom. Constr. & doi:10.1016/j.autcon.2020.103285 \\ \hline
4 & 73 & 0.917 &
\begin{tabular}{} \end{tabular} & 1988–2021 & [9] & Autom. Constr. & doi:10.1016/j.autcon.2009.07.002 \\ \hline
5 & 72 & 0.961 & Infrastructure project & 1987–2020 & [168] & J. Constr. Eng. & doi: 10.1061/(ASCE)CO.1943-7862.0000131 \\ \hline
6 & 62 & 0.935 &
\begin{tabular}{} \end{tabular} & 1960–2022 & [24] & Autom. Constr. & doi:10.1016/j.autcon.2012.05.006 \\ \hline
7 & 61 & 0.905 &
\begin{tabular}{} \end{tabular} & 1992–2020 & [36] & Autom. Constr. & doi:10.1016/j.autcon.2015.07.010 \\ \hline
8 & 57 & 0.972 &
\begin{tabular}{} \end{tabular} & 1985–2018 & [169] & J. Comput. Civ. Eng. & doi: 10.1061/(ASCE)CP.1943-5487.0000705 \\ \hline
9 & 53 & 0.984 &
\begin{tabular}{} \end{tabular} & 1999–2022 & [62] & Buildings & doi:10.3390/buildings9040086 \\ \hline
10 & 48 & 0.914 &
\begin{tabular}{} \end{tabular} & 1969–2022 & [50] & Autom. Constr. & doi:10.1016/j.autcon.2017.06.018 \\ \hline \end{tabular}
\end{table} 
Table 4: The detailed information on the clustering results. Figure 13: The network of clusters. (Refs. [5, 9, 18, 19, 21, 24, 36, 48, 50, 52, 62, 78]). As shown in Figure 14 (by Citespace), the influence of time on the cluster analysis was considered. This is another form of cluster analysis for literature co-citation, which can display the development process of each cluster.