## 3 Results
### Content Analysis
#### 3.2.2 Applications  
Topics identified through the bibliometric analysis, including AI-driven (1) design, (2) optimization, and (3) diagnostics of architectural 3D-printed structures, are analyzed in depth in this subsection (Figure 7). Within each topic, the following aspects were reviewed:  
1. general uses of different AI algorithms in a specific application domain;
2. challenges of large-scale structures 3 DP overcomes by integration of AI;
3. modifications of AI algorithms made to suit a specific application domain. Figure 6: Overview of AI techniques and main applications in 3 DP in architecture [28; 46; 47; 48; 49; 50; 51; 54; 55; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73]. Figure 7: Overview of the main identified uses for AI in large-scale 3 DP. Topic 1. AI-Driven Design of 3D-Printed Architectural Structures  
In the reviewed articles, AI integration in the 3 DP design process is mainly focused on the issues of material design and design optimization of the 3 DP mixes. The previous is confirmed by the fact that 75% of the represented papers deal with these specific topics. [PERSON] et al. [64] deliver a systematic review of the emerging digital technologies used for off-site construction leading towards Industry 4.0, where among the fifteen reviewed technologies, AI and 3 DP are elaborated on separately, each representing an important factor for the future development of the construction sector. On the other hand, [PERSON] et al. [55] review the development of predictive modeling and design optimization and the current state of the art specifically for concrete 3 DP, where four characteristic steps for the implementation of ML into the 3 DP are given, including the problem definition, development of the model, collection of data, and process settings. Moreover, [PERSON] et al. [28] deliver a review of the latest research on integrating ML into construction 3 DP, with an extensive discussion of current problems and future trends in the field. In another study, [PERSON] [47] proposes a framework for AI and 3 DP combinations in five specific aspects, including 3 DP materials, automation design, digital construction, 3 DP robots, and a 3 DP BIM platform. The overview of the main findings of AI models in 3 DP for designing large-scale structures is given in Table 6.  
The main challenges found in the studies include the time-consuming process and handling of large amounts of data in ML models [55; 64]. Regarding the ML application in \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Applications** & **Techniques** & **Main Conclusions** & **Author(s) References** \\ \hline Design for Additive Manufacturing (DfAM) of prefabricated architectural components. & & \(\bullet\) & Benefits: reduced effort and cost in the design process; components’ segmentation speed is increased. Issues: time-consuming topology analysis preparation process; limitations in the components’ configuration design. & [PERSON] et al. [64] \\ \hline Rapid design method development for product modeling and its structure selection. & ML & \(\bullet\) & Future research: exploring combinations of novel technologies such as BIM and AL & Tan [47] \\ \hline Design problem definition aimed at AM. & ML & \(\bullet\) & Challenge: handling large amounts of data with high computational costs. Digital twins of AM structures could support the ML techniques in attaining better outcomes. ML included in tasks such as topology optimization, bio-inspired concept creation, and geometry creation using Computer-Aided Design. & [PERSON] et al. [55] \\ \hline AM material design with the real-time observation and automatic mixture alterations during the printing process. & ML & \(\bullet\) & ML application in the matching ratios’ selection can improve production efficiency and reduce construction costs. Future studies: the integration of different research areas presenting specific challenges in the construction 3 DP. & [PERSON] et al. [28] \\ \hline \hline \end{tabular}
\end{table}