A systematic review on artificial intelligence applications in architecture

Bölek, Buse; Tutal, Osman; Özbaşaran, Hakan

doi:10.47818/drarch.2023.v4i1085

Cited by 13 publications

(8 citation statements)

References 96 publications

(65 reference statements)

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“…However, a vast scope remains for further research to comprehensively discern the long-term implications and to formulate frameworks guiding the effective assimilation of AI within architectural pedagogy as well as practice. The narrative suggests a paradigm shift where architectural design and education are becoming more accessible and instantaneous, thanks to AI technologies (Bölek, Tutal, and Özbaşaran 2023). This narrative underscores decades of advancements that have enticed both artistic and architectural communities, framing a vivid tableau of AI's role in reshaping creative expression and design paradigms .…”

Section: Historical Context and Current Directionsmentioning

confidence: 99%

FabriCity-XR: A Phygital Lattice Structure Mapping Spatial Justice – Integrated Design to AR-Enabled Assembly Workflow

Mehan,

Howell,

Montejano

et al. 2024

112th ACSA Annual Meeting Proceedings, Disruptors on the Edge

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This paper examines the integration of Midjourney AI in architectural education, aimed at navigating digital pedagogy. By reviewing a case study from an Architecture studio, the paper seeks to understand the benefits, challenges, and ethical implications of embedding AI technologies in architectural learning. Traditional architectural education hasprimarily focused on design principles, technical skills, and historical context. However, introducing AI presents an opportunityto expand the pedagogical scope, enabling students to engage with emerging concepts and techniques. A Design/ Research studio, titled “The Sacred in Between: Architectural Explorations of Sacred Spaces,” enabled students in exploringthe concept of “the sacred” through digital and physical artifacts, employing AI as a novel tool. Utilizing Midjourney AI,students synthesized relevant readings and discussions to create imagined scenes, with an extensive array of imagesaiding the initial design process. Additionally, reflections on encounters with AI spurred a class debate on AI’s influence on architectural education and the profession.A core discussion emerged around the potential decrease in human agency and creativity due to an overreliance onAI, which may lead to a standardized design approach, thus inhibiting individual expression and innovation. The ethical dimension of integrating AI into the curriculum was also explored, with concerns regarding authorship. Advocacy fora critical understanding of AI’s social and ethical implications was emphasized. The incorporation of AI in architectural education opens avenues for curriculum diversification, design exploration, and interdisciplinary collaboration,especially in a digital-centric society propelled towards the Metaverse. However, preserving human agency, addressingethical issues, and equipping students for a dynamic professional landscape are imperative. This integration encourages architectural institutions to empower students in navigating complex design challenges amid rapid technological changes, urging a re-evaluation of architecture’s manifestation in digital realms as discourse increases in this domain.

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Section: Historical Context and Current Directionsmentioning

confidence: 99%

FabriCity-XR: A Phygital Lattice Structure Mapping Spatial Justice – Integrated Design to AR-Enabled Assembly Workflow

Mehan,

Howell,

Montejano

et al. 2024

112th ACSA Annual Meeting Proceedings, Disruptors on the Edge

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“…In fact, the growing interest in the utilization of AI tools within these fields is well documented in the existing literature. For example, Huang et al [11], Lagaros and Plevris [12], Manzoor et al [13], Dede et al [14], Pan and Zhang [15], and Lu et al [16] reviewed the application of AI tools in civil engineering, while Momade et al [17] and Bölek et al [18] documented the recent applications of these tools in architecture.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Accuracy of Low-Cost Inclinometers with Artificial Intelligence

Lozano,

Emadi,

Komarizadehasl

et al. 2024

Buildings

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The development of low-cost structural and environmental sensors has sparked a transformation across numerous fields, offering cost-effective solutions for monitoring infrastructures and buildings. However, the affordability of these solutions often comes at the expense of accuracy. To enhance precision, the LARA (Low-cost Adaptable Reliable Anglemeter) system averaged the measurements of a set of five different accelerometers working as inclinometers. However, it is worth noting that LARA’s sensitivity still falls considerably short of that achieved by other high-accuracy commercial solutions. There are no works presented in the literature to enhance the accuracy, precision, and resolution of low-cost inclinometers using artificial intelligence (AI) tools for measuring structural deformation. To fill these gaps, artificial intelligence (AI) techniques are used to elevate the precision of the LARA system working as an inclinometer. The proposed AI-driven tool uses Multilayer Perceptron (MLP) to glean insight from high-accuracy devices’ responses. The efficacy and practicality of the proposed tools are substantiated through the structural and environmental monitoring of a real steel frame located in Cuenca, Spain.

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“…In the application of artificial intelligence methods in the field of architecture, a significant amount of research has been focused on integrating technical solutions into architectural issues. The application of artificial intelligence in architectural design ranges from intelligent material design to architectural planning solutions [4]. This includes computer vision aspects such as image processing and image generation, which can offer more efficient and effective solutions for architectural design.…”

Section: Introductionmentioning

confidence: 99%

Architectural facade design with style and structural features through stable diffusion model

Wen,

Liang,

et al. 2024

Journal of Intelligent Construction

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Keywords architectural design digital facade generation style transfer stable diffusion T2I-AdapterWith the advancements in digital technology, the field of architectural design has increasingly embraced data and algorithms to enhance design efficiency and quality. Recent advancements in text-to-image generation models have enabled the creation of images corresponding to textual descriptions. However, textual descriptions struggle to capture essential style characteristics in style images. In this paper, we propose a method for architectural facade design based on the Stable Diffusion Model, which combines stylistic images or stylistic keywords as input with the structural conditions of content images to generate images with both stylistic and architectural features. By employing the CLIP image encoder to convert the style image into its initial image embedding, utilizing feature extraction from multi-layer cross-attention, and training optimization to obtain a pre-trained image embedding, this method extracts stylistic features from style images and converts them into corresponding embeddings. This process enables the generated images to embody both stylistic features and artistic semantic information. Furthermore, the T2I-Adapter model is employed to use the architectural structure of content images as conditional guidance, ensuring that the generated images exhibit the corresponding structural features. By leveraging these two aspects, this method can decorate architecture with stylistic features from stylistic images while preserving the architectural structure features of content images, resulting in images that reflect the content images after style transformation. Our method is primarily applied in architectural design applications. It is capable of generating facade images from flat design drawings, 3D architectural models, and handdrawn sketches, and has achieved commendable results.

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A systematic review on artificial intelligence applications in architecture

Cited by 13 publications

References 96 publications

FabriCity-XR: A Phygital Lattice Structure Mapping Spatial Justice – Integrated Design to AR-Enabled Assembly Workflow

FabriCity-XR: A Phygital Lattice Structure Mapping Spatial Justice – Integrated Design to AR-Enabled Assembly Workflow

Enhancing the Accuracy of Low-Cost Inclinometers with Artificial Intelligence

Architectural facade design with style and structural features through stable diffusion model

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