## 3 Results
### Bibliometric Analysis
#### 3.1.2 Keywords Analysis  
A network analysis was performed based on the keyword co-occurrence rates, visualizing keyword relations and co-occurrence rates in the analyzed data sample. There are two possible methods of obtaining data for network visualization: automatically extracting the most common words by scanning article titles or abstracts or generating a data map based on the bibliographic metadata keywords [43]. For this study, the second method was used, since it provides a clear picture of the principal themes discussed in the studies, as predefined keywords often correspond with the main themes presented in the research. As the literature sample was collected from multiple sources, bibliographic metadata for this analysis were obtained from the reference manager software and subsequently imported into VOSviewer for the analysis. The two options for counting keyword occurrence rates are full counting and fractional counting [53]. For this study, the network was generated by the full counting method, where each link for one co-occurrence has the same strength. Since the collected literature sample is relatively small, the minimal number of co-occurrences in the network was set to 2, resulting in 25 keywords repeating more than twice. These words were then used to generate a visualization network based on the number of occurrences and their link strength. Table 4 systematizes the top 15 included keywords based on the number of occurrences and total link strength. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Journal** & **No. of Articles** & **IF (2022)** \\ \hline _Construction and Building Materials_ & 4 & 7.4 \\ _Cement and Concrete Research_ & 2 & 11.4 \\ _Buildings_ & 2 & 3.8 \\ _Automation in Construction_ & 2 & 10.3 \\ _Additive Manufacturing_ & 1 & 11.63 \\ _Virtual and Physical Prototyping_ & 1 & 10.96 \\ _Journal of Intelligent Manufacturing_ & 1 & 8.3 \\ _Case Studies in Construction Materials_ & 1 & 6.2 \\ _Structures_ & 1 & 4.1 \\ _Materials and Structures_ & 1 & 3.8 \\ _Applied Sciences_ & 1 & 2.7 \\ _International Journal of Architectural Computing_ & 1 & 1.7 \\ _Construction Innovation_ & 1 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Source journals for the analyzed literature sample. The identified keywords are strongly related to the main topic, with most of them referring to AI tools, such as \"machine learning\" or \"artificial neural networks\". In contrast, others refer to additive manufacturing processes like \"3D printing,\" \"concrete\", or \"mix design\". Two terms with the highest link strength and number of incidences are \"compressive strength\" and \"concrete\", which indicate a research focus on the material and structural design optimization for 3 DP, with multiple papers focusing on the application of AI tools in this field [51; 54; 55]. Another highly present term was \"machine learning\", indicating increased use of ML tools in the field. The previous can be better observed in Figure 5, which visually presents a keyword network grouping related keywords into four clusters. The keywords are divided into clusters based on their interconnections. These agglomerations can help better understand the principal themes in the existing research. The largest green cluster includes the most reoccurring words and focuses on the design and predictions in construction using ML tools. The red cluster is more focused on 3D concrete printing through performance and behavior analysis. The main AI tool in this cluster is computer vision, which is closely related to the rest of the terms. The blue cluster is formed around 3 DP optimization and digital fabrication processes. This cluster includes artificial neural networks (ANNs) as a representative AI tool. The smallest yellow cluster is centered around material design for AM without including the specific AI tools. The keyword cluster analysis identified three principal categories representing the central research topics (Table 5).