## 3 Results
### Content Analysis
#### 3.2.1 Techniques  
ML is a computer science field that relates to the algorithms that learn how to solve complex real-world problems from given datasets. The most common problems met by ML include classification, clustering, and prediction [56]. The generally accepted types of ML, distinguished by significant functional differences, include (1) Supervised Learning (SL), which uses input data for identification and fulfillment of certain tasks, among which classification and regression are the most well-known; (2) Unsupervised Learning (UL), which is mostly concerned with identifying groups and organization patterns within unlabeled datasets, with the common task of clustering; (3) Semi-Supervised Learning (SSL), which learns from both labeled and unlabeled data, commonly used for classification and clustering purposes; and (4) Reinforcement Learning (RL), which functionality lies within a reward-punish system where the algorithm automatically evaluates the best behavior patterns and takes further actions to optimize the system [57]. Deep learning (DL) is a subset of ML with key features represented in numerous layers or stages of nonlinear information processing and supervised (SDL) or unsupervised (UDL) feature representation at progressively higher, more abstract layers [58]. Defining ANN in the scientific and practical domains remains challenging [59]. The architecture and functionality of artificial neurons forming ANNs are based on biological neurons. These networks function by processing information in their fundamental constituents--artificial neurons--in a non-linear, distributed, parallel, and local manner [60]. Lastly, computer vision represents a technical system whose primary goal is to mimic the functional modules of human vision, which is achieved through tasks including visualization, image formation, control of irradiance, focusing, irradiance resolution, tracking, and processing and analysis [61].  
Out of the twenty-one reviewed studies, ML is found to be the main topic of six studies, with applications ranging from design, optimization, and diagnostic purposes for 3 DP structures [62; 63; 28; 64; 65; 66; 29; 28]. ANN as a category is found to be dominantly present in nine studies overall, with applications belonging to the diagnostic domain [65; 66; 67; 68; 69; 50; 51; 54; 65; 69].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Topic** & **Representative Keywords** \\ \hline \multirow{4}{*}{1} & & Design \\  & AI-driven design & Construction \\  & of 3D-printed architectural structures & Machine learning \\  & & Neural networks \\  & & Deep learning \\  & & Optimization \\  & & Digital fabrication \\
2 & AI-driven optimization & 3D printing \\  & of 3D-printed architectural structures & Artificial neural networks \\  & & Concrete \\  & & Performance \\  & & Computer vision \\
3 & AI-driven diagnostics & Quality monitoring \\  & of 3D-printed architectural structures & Automation \\  & & Prediction \\  & & Behavior \\ \hline \hline \end{tabular}
\end{table}
Table 5: Principal research themes.  
The combined appearance of ML and ANN is dominantly found in a total of four studies, where the use is focused around optimization and diagnostics [49; 70; 71; 72]. Lastly, computer vision algorithms are present solely in the diagnostic domain and cover a total of four papers [47; 48; 54; 73]. Figure 6 displays the identified AI techniques, their domains of application, and the related references.