The proliferation of digital technologies considerably changed the field of architecture. Digital fabrication pushes architecture into an unexpected new domain of previously unachievable complexity, detail, and materiality. Understanding these technologies’ impact can help direct future research, innovate design and construction processes, and improve the education of future professionals. However, comprehensive reviews offering a holistic perspective on the effects of 3D printing technologies on architecture are limited. Therefore, this study aims to provide a systematic review of state-of-the-art research on 3D printing technologies in architectural design and construction. The review was performed using three major databases, and selected peer-reviewed journal articles published in the last ten-year period were included in quantitative and qualitative analyses. Using bibliometric analysis, the research progress is summarized through the identified trend of the annual number of articles, prominent authors and co-authorship network, and key topics in the literature organized in three clusters. Further, content analysis of selected articles enabled coding cluster themes. Moreover, the analysis differentiated two categories of 3D printing technologies based on the scale of the system, elaborating their peculiarities in terms of materials, methods, and applications. Finally, challenges and promising directions for future work and research challenges are discussed.

Artificial Intelligence (AI) and 3D printing (3DP) play considerable roles in what is known as the Fourth Industrial Revolution, by developing data- and machine-intelligence-based integrated production technologies. In architecture, this shift was induced by increasingly complex design requirements, posing important challenges for real-world design implementation, large-scale structure fabrication, and production quality standardization. The study systematically reviews the application of AI techniques in all stages of creating 3D-printed architectural structures and provides a comprehensive image of the development in the field. The research goals are to (1) offer a comprehensive critical analysis of the body of literature; (2) identify and categorize approaches to integrating AI in the production of 3D-printed structures; (3) identify and discuss challenges and opportunities of AI integration in architectural production of 3D-printed structures; and (4) identify research gaps and provide recommendations for future research. The findings indicate that AI is an emerging addition to the 3DP process, mainly transforming it through the real-time adjustment of the design or printing parameters, enhanced printing quality control, or prediction and optimization of key design features. However, the potential of the application of AI in large-scale architectural 3D printing still needs to be explored. Lastly, the study emphasizes the necessity of redefining traditional field boundaries, opening new opportunities for intelligent architectural production.

Artificial intelligence (AI) and 3D printing (3DP) play critical roles in what is known as the 4th Industrial Revolution by developing data- and machine-intelligence-based integrated production technologies. This shift has led to increasingly complex design requirements and fabrication methods, posing several challenges for implementing design, fabrication process, and architectural quality standardization. This paper systematically reviews AI techniques applied in diverse stages of 3DP production in architecture. The research goals are to (1) offer a comprehensive critical analysis of the body of literature; (2) identify and categorize approaches to integrating AI in the production of 3D-printed structures; (3) identify and discuss challenges and opportunities of AI integration in architectural production of 3DP structures; and (4) identify gap and provide recommendations for future research. The findings indicate that topics related to AI applications are current and trending, receiving increasing attention from researchers in general and particularly those working in machine and deep learning, artificial neural networks, and pattern recognition. However, the potential of the application of AI in architectural production using additive manufacturing still needs to be explored. Considering this, the paper emphasizes the necessity of redefining traditional field boundaries and opening new opportunities in the sector.

This paper investigates the use of parametric inverted chain models (ICMs) as pedagogical tools for teaching structural design to architecture students. A mixed-method action research study was conducted at the University of Belgrade – Faculty of Architecture through an educational intervention that integrated analog and digital form-finding within a learning-by-doing framework. The intervention combined physical hanging-chain models with computational simulations using Spider3D, a custom Grasshopper plugin developed for ICM simulation. Data collected through surveys, knowledge assessments, focus-group discussions, reflective journals, and instructor observations indicate that the approach improved students’ understanding of structural behavior, equilibrium forms, and the relationship between form and force. Participants reported increased confidence in structural reasoning, greater engagement with structural design concepts, and a stronger appreciation of structure as a generator of architectural form. The combination of physical and computational workflows enabled students to develop both intuitive and analytical perspectives on structural performance. The study demonstrates that parametric ICMs can function not only as form-finding tools but also as effective pedagogical instruments that support experiential learning, structural intuition, and creative exploration. The findings highlight the potential of integrating research-led digital tools into architectural education to strengthen the connection between structural design, computation, and creative practice.

The interest in pattern geometry and its application to architecture may be seen throughout history. While some authors were fascinated by pattern aesthetics, others were focused on their effectiveness and underlying principles of pattern formation. In continuing with the work of the second group of authors, this paper reviews opportunities for efficient ways of implementing patterns in the design of architectural elements, supported by recent developments in parametric design and digital fabrication techniques. This paper aims to analyze pattern configurations found in nature in order to determine the underlying generation principles and the potential of their application for 3D printed slab systems. Using case study methodology, selected patterns will be applied in developing a generative parametric design system, which will further be tested in creating and (small-scale) fabricating ribbed slab elements. The result of the research is the generalization of a design approach based on principles of natural pattern formation to produce sustainable design solutions that rely on the transposition of the inherent efficiency of natural systems, such as low energy or material consumption.

This paper investigates the use of parametric inverted chain models (ICMs) as pedagogical tools for teaching structural design to architecture students. A mixed-methods action research study was conducted at the University of Belgrade Faculty of Architecture through a five-week educational intervention with 30 undergraduate students, integrating analog and digital form-finding within a learning-by-doing framework. The intervention combined physical hanging-chain models with computational simulations using Spider3D, a custom Grasshopper plugin developed for ICM simulation. Data collected through surveys, knowledge assessments, focus-group discussions, reflective journals, and instructor observations indicate that the approach improved students’ understanding of structural behavior, equilibrium forms, and the relationship between form and force. Participants reported increased confidence in structural reasoning, greater engagement with structural design concepts, and a stronger appreciation of structure as a generator of architectural form. Combining physical and computational workflows enabled students to develop both intuitive and analytical perspectives on structural performance. The study suggests that parametric ICMs can function not only as form-finding tools but also as effective pedagogical instruments that support experiential learning, structural intuition, and creative exploration. The findings highlight the potential of integrating research-led digital tools into architectural education to strengthen connections among structural design, computation, and creative practice.