## 2 Methodology
### Forming Literature Sample
#### 2.1.1 Literature Search  
The initial search and paper collection were carried out in the WoS database, which includes leading peer-reviewed publications with bibliometric data. Other databases, such as Scopus, were also considered for this research, since they also offer a comprehensive sample pool. However, this review required screening a topic that is growing across multiple research disciplines and needed to be narrowed down to the ones relevant to the construction industry. Further, WoS was chosen as the primary literature source for its built-in categorization of papers by research categories. Meanwhile, the Scopus search engine is more keyword-oriented with fewer in-depth categorization filters, with Engineering being the closest category, which proved to be too wide a literature pool for efficient retrieval of results for this study.  
The search was conducted using topic-relevant keywords. As this paper focuses on the application of AI tools in AM, keywords were divided into two sets, one addressing the most used AI tools and the other referring to the terms commonly used for AM in architecture. The AM and design sets mostly consist of synonyms often used to describe 3 DP technologies and several architectural design terms to cover the whole design and fabrication process. On the other hand, the topic of AI tools covers a wide range of different technologies that need to be included, as they are not used synonymously.  
The keyword search string \"artificial intelligence\" OR \"AI\" OR \"augmented intelligence\" OR \"machine learning\" OR \"ML\" OR \"simulated annealing\" OR \"computer vision\"  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Source** & **Search Method** & & **Search Criteria** \\ \hline Web of Science & Keyword method & (a) & Research category: Engineering, Civil, Construction and Building Technology, or Architecture \\ Online repositories & Reference list search & (b) & Paper type: Journal article, proceeding papers \\ Google Scholar & Internet search & (c) & Years published: 2013–present \\  & & (d) & Language: English \\ \hline \hline \end{tabular}
\end{table}
Table 1: Search criteria.  
Figure 1: Layout of the research process.  
OR \"pattern recognition\" OR \"genetic algorithm\"\" OR \"evolutionary algorithm\"\" OR \"neural network\" OR \"deep learning\" OR \"reinforced learning\" OR \"fuzzy logic\" OR \"adversarial network\" OR \"convolutional network\" OR \"supervised learning\" OR \"un-supervised learning\" AND \"material extrusion\" OR \"large-scale 3d print\" OR \"additive manufacturing\" OR \"concrete 3D print\" OR \"3D print\" OR \"generative design\" OR \"structural design\" OR \"computational design\" was used to search paper titles, abstracts, and keywords. Based on the established search criteria, the search was limited to the relevant categories and publication date range, as shown in Figure 2. The asterisk sign at the end of several phrases in Figure 2 is written to include wildcard search results and can represent any group of characters.  
The initial search yielded 394 results matching the search criteria, which were then subjected to the literature selection process. Simultaneously, an unstructured search using the same keywords was performed in the Scopus and Google Scholar databases and multiple online repositories to uncover additional potentially relevant papers. This method contributed to an additional 44 papers.