## 4 Discussion and Conclusions
### Future Research Directions  
Based on the results of the literature review presented in this study, three specific sets of challenges (corresponding to identified application domains) could be summarized. 1. **Challenges of AI-driven design of 3D-printed architectural structures**, including: * time-consuming and costly data acquiring and labeling process, * computational costs of handling large amounts of data, * data interpretability and validation, and * the prediction and control of the material anisotropy and other characteristics. 2. **Challenges of AI-driven optimization of 3D-printed architectural structures**, including: * application of AI in the practical domain, since it is mostly limited to checking printability and modularization for prefabrication techniques, * ML models' non-invertible relationship between the input (targeted extrudate cross-sectional shape) and output (3 DP nozzle shape), * creation of the control systems for effective extruding toolpath, * higher geometric complexity of the optimized topologies, and * inability to utilize common AI models since they give out low accuracy results. 3. **Challenges of AI-driven diagnostics of 3D-printed architectural structures**, including: * effort consuming tasks of manual annotation of data, * short timeframe for 3 DP production which allows for a limited number of diagnostic and inspective methods on-site, * 3 DP material characteristics such as pumpability, yield stress, viscosity, and cement hydration assessment, and * extrudate surface quality issues such as jagged surface finish or the staircase effect. Future research will presumably address some of listed challenges through advancing various aspects of AI and 3 DP technologies and their integration with architectural practice. Some other promising research directions are specified in the following. * **Integration of AI tools in the conceptual design stage of 3D-printed architectural structures. **  
More research on the conceptual design stage of AM in architecture is needed [72]. Also, a more user-centric and inclusive design approach may result from researching human-computer interaction and investigating technologies that promote cooperation between architects and AI models. Future research could focus on developing AI tools that assist designers in the creative design process. However, it is essential to investigate the overall implications of AI-driven architectural 3 DP design for large-scale structures and ensure the technology aligns with generally acceptable ethical values. * **Advancements of AI algorithms and generative design techniques to optimize the performance and functionality of 3D-printed architectural structures. **  
This involves developing AI models that could consider multiple design objectives, constraints, and user preferences to generate optimized and efficient designs. Anotherinteresting area is the advancement of AI-based techniques for structural analysis and verification of 3 DP architectural structures. Developing AI models that can efficiently analyze the structural performance of complex designs will help architects ensure safety and make informed decisions. However, further research is needed in the field of data collection, as the lack of readily available datasets in the construction industry poses challenges for the wider implementation of AI-driven tools [55; 64]. * **Exploration of AI-driven algorithms for multi-scale, multi-material 3 DP process. ** As 3 DP often requires novel and unique material mixes, AI tools have proven to be useful tools for rheology optimization and the design of new printing mixes. To produce complex and customized architectural structures, researchers could investigate how AI can be utilized to control printing processes involving numerous materials with different qualities and scales. Also, the effectiveness and scalability of 3 DP in architectural construction could be increased by studying the integration of AI with construction robotics and automation.