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Artificial Intelligence and Buildings: Design, Analysis, and Construction
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Artificial Intelligence and Buildings: Design, Analysis, and Construction

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Construction Management, and Computers & Digitization".

Deadline for manuscript submissions: closed (31 July 2024) | Viewed by 5227

Special Issue Editors

Dr. Shi-Yu Xu

E-Mail Website
Guest Editor
Department of Civil and Construction Management, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
Interests: structural engineering and mechanics; earthquake engineering; geotechnical engineering; solid and soil mechanics; earth-retaining structures; finite element method; architectural and structural system design; algorithm-aided design; image recognition
Dr. Dave T. F. Kuo

E-Mail Website
Guest Editor
Graduate Institute of Environmental Engineering, National Taiwan University, Taipei 10617, Taiwan
Interests: environmental organic chemistry; ecotoxicology; environmental risk assessment; environmental meta-analysis; carbon capture and sequestration; machine-learning based environmental engineering

Special Issue Information

Dear Colleagues,

The integration of artificial intelligence (AI) into the building sector marks a revolutionary shift, promising to reshape traditional practices and unlock unparalleled opportunities for innovation and efficiency.

In the fields of architectural design and civil engineering, AI has already demonstrated remarkable impacts and potentials. For instance, AI-powered generative design algorithms entail employing evolutionary search or optimization techniques to achieve predefined objectives, enhancing creativity and resource utilization. Furthermore, AI algorithms can analyze data from sensors installed on buildings, predicting potential structural issues and allowing for timely repairs to prevent failures. This Special Issue brings together leading researchers, practitioners, and visionaries to share their cutting-edge research and insights, showcasing the transformative impacts of AI across various aspects of building design, analysis, and construction.

We cordially invite scholars worldwide to contribute to this Special Issue and share their innovative research and practical applications of AI in the building sector. By collaborating and sharing knowledge, we aim to foster a deeper understanding of AI's potential in architecture and civil engineering and propel the industry toward a more sustainable, efficient, and intelligent future. This Special Issue will also spark new ideas and collaborations that will hopefully shape the future of our built environment.

Dr. Shi-Yu Xu
Dr. Dave T. F. Kuo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Buildings is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • architectural and structural design
  • structural analysis
  • construction and management
  • built environment
  • computer-aided design
  • computer-aided engineering
  • artificial intelligence
  • machine learning
  • image recognition

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

33 pages, 37821 KiB  
Article
Communicating AI for Architectural and Interior Design: Reinterpreting Traditional Iznik Tile Compositions through AI Software for Contemporary Spaces
by Miray Gür, Figen Kıvılcım Çorakbaş, İmran Satış Atar, M. Gazihan Çelik, İlayda Maşat and Ceyda Şahin
Buildings 2024, 14(9), 2916; https://doi.org/10.3390/buildings14092916 - 15 Sep 2024
Viewed by 286
Abstract
Artificial intelligence (AI), which has a strong potential to assist architects in conceptual and visualization stages, has been increasingly used in the field of design and architecture. This study, focusing on the AI tools that generate images from texts and offer innovative solutions [...] Read more.
Artificial intelligence (AI), which has a strong potential to assist architects in conceptual and visualization stages, has been increasingly used in the field of design and architecture. This study, focusing on the AI tools that generate images from texts and offer innovative solutions to design problems, aims to evaluate the use of AI for the reinterpretation of traditional Iznik tile patterns and colors in the context of architectural design and modern interiors. The methodology consists of four stages, which are the selection of AI tools (Copilot, DALL-E 2, DALL-E 3, Midjourney), the preparation of textual prompts for testing “çini” (tile) expression, testing of the AI tools’ perception of the concepts related to Iznik tile motifs, and the creation of prompt series. The findings of our study provide evidence that current AI tools exhibit distinct features in terms of variety, conceptualization, artistic visualization, and image production, while they are hardly equipped with the necessary conceptual background to communicate with the designers for the interpretation of the traditional Iznik tiles in contemporary architectural design. Specifically, Midjourney, which could produce historically referenced contemporary designs in response to textual expressions, was more successful than other AI tools. DALL-E 2 could not visualize the expressions concerning the placement of the Iznik tile surfaces in interior spaces but was quite inspiring in terms of the images regarding the tile pattern and color. DALL-E 3 and Copilot tools produced similar images in terms of color palette and patterns, whereas DALL-E 3 was better at visualizing spatial data. Our results reveal that AI tools still need to be developed for analyzing traditional patterns, styles, and forms for contemporary design purposes. On the other hand, AI tools can develop innovative approaches, optimize the tile production procedure, and have the potential to accelerate the design process for designers by generating new and diverse ideas. Full article
(This article belongs to the Special Issue Artificial Intelligence and Buildings: Design, Analysis, and Construction)
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24 pages, 8975 KiB  
Article
Classification and Model Explanation of Traditional Dwellings Based on Improved Swin Transformer
by Shangbo Miao, Chenxi Zhang, Yushun Piao and Yalin Miao
Buildings 2024, 14(6), 1540; https://doi.org/10.3390/buildings14061540 - 25 May 2024
Viewed by 838
Abstract
The extraction of features and classification of traditional dwellings plays significant roles in preserving and ensuring the sustainable development of these structures. Currently, challenges persist in subjective classification and the accuracy of feature extraction. This study focuses on traditional dwellings in Gansu Province, [...] Read more.
The extraction of features and classification of traditional dwellings plays significant roles in preserving and ensuring the sustainable development of these structures. Currently, challenges persist in subjective classification and the accuracy of feature extraction. This study focuses on traditional dwellings in Gansu Province, China, employing a novel model named Improved Swin Transformer. This model, based on the Swin Transformer and parallel grouped Convolutional Neural Networks (CNN) branches, aims to enhance the accuracy of feature extraction and classification precision. Furthermore, to validate the accuracy of feature extraction during the prediction process and foster trust in AI systems, explainability research was conducted using Grad-CAM-generated heatmaps. Initially, the Gansu Province Traditional Dwelling Dataset (GTDD) is established. On the constructed GTDD dataset, the Improved Swin Transformer attains an accuracy of 90.03% and an F1 score of 87.44%. Comparative analysis with ResNet-50, ResNeXt-50, and Swin Transformer highlights the outstanding performance of the improved model. The confusion matrix of the Improved Swin Transformer model reveals the classification results across different regions, indicating that the primary influencing factors are attributed to terrain, climate, and cultural aspects. Finally, using Grad-CAM-generated heatmaps for explaining classifications, it is observed that the Improved Swin Transformer model exhibits more accurate localization and focuses on features compared to the other three models. The model demonstrates exceptional feature extraction ability with minimal influence from the surrounding environment. Simultaneously, through the heatmaps generated by the Improved Swin Transformer for traditional residential areas in five regions of Gansu, it is evident that the model accurately extracts architectural features such as roofs, facades, materials, windows, etc. This validates the consistency of features extracted by the Improved Swin Transformer with traditional methods and enhances trust in the model and decision-making. In summary, the Improved Swin Transformer demonstrates outstanding feature extraction ability and accurate classification, providing valuable insights for the protection and style control of traditional residential areas. Full article
(This article belongs to the Special Issue Artificial Intelligence and Buildings: Design, Analysis, and Construction)
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22 pages, 6419 KiB  
Article
Artificial Intelligence Islamic Architecture (AIIA): What Is Islamic Architecture in the Age of Artificial Intelligence?
by Ahmad W. Sukkar, Mohamed W. Fareed, Moohammed Wasim Yahia, Emad Mushtaha and Sami Luigi De Giosa
Buildings 2024, 14(3), 781; https://doi.org/10.3390/buildings14030781 - 13 Mar 2024
Cited by 1 | Viewed by 2179
Abstract
Revisiting the long-debated question: “What is Islamic architecture?”, this research article aims to explore the identity of “Islamic architecture (IA)” in the context of artificial intelligence (AI) as well as the novel opportunities and cultural challenges associated with applying AI techniques, such as [...] Read more.
Revisiting the long-debated question: “What is Islamic architecture?”, this research article aims to explore the identity of “Islamic architecture (IA)” in the context of artificial intelligence (AI) as well as the novel opportunities and cultural challenges associated with applying AI techniques, such as the machine learning of Midjourney in the context of IA. It investigates the impact factors of AI technologies on the understanding and interpretation of traditional Islamic architectural principles, especially architectural design processes. This article employs a quantitative research methodology, including the observation of works of artists and architectural designers appearing in the mass media in light of a literature review and critical analysis of scholarly debates on Islamic architecture, spanning from historical perspectives to contemporary discussions. The article argues for the emergence of a continuous paradigm shift from what is commonly known as “postmodern Islamic architecture” (PMIA) into “artificial intelligence Islamic architecture” (AIIA), as coined by the authors of this article. It identifies the following impact factors of AI on IA: (1) particular requirements and sensitivities, inaccuracies, and biases, (2) human touch, unique craftsmanship, and a deep understanding of cultural issues, (3) regional variation, (4) translation, (5) biases in sources, (6) previously used terms and expressions, and (7) intangible values. The significance of this research in digital heritage lies in the fact that there are no pre-existing theoretical publications on the topic of “Islamic architecture in the age of artificial intelligence”, although an extensive set of publications interpreting the question of the definition of Islamic architecture, in general, is found. This article is pivotal in analyzing this heritage-inspired design approach in light of former criticism of the definition of “Islamic architecture”, which could benefit both theorists and practitioners. This theoretical article is the first in a series of two sequential articles in the Buildings journal; the second (practical) article is an analytical evaluation of the Midjourney architectural virtual lab, defining major current limits in AI-generated representations of Islamic architectural heritage. Full article
(This article belongs to the Special Issue Artificial Intelligence and Buildings: Design, Analysis, and Construction)
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23 pages, 60771 KiB  
Article
DL-SLICER: Deep Learning for Satellite-Based Identification of Cities with Enhanced Resemblance
by Ulzhan Bissarinova, Aidana Tleuken, Sofiya Alimukhambetova, Huseyin Atakan Varol and Ferhat Karaca
Buildings 2024, 14(2), 551; https://doi.org/10.3390/buildings14020551 - 19 Feb 2024
Cited by 1 | Viewed by 1335
Abstract
This paper introduces a deep learning (DL) tool capable of classifying cities and revealing the features that characterize each city from a visual perspective. The study utilizes city view data captured from satellites and employs a methodology involving DL-based classification for city identification, [...] Read more.
This paper introduces a deep learning (DL) tool capable of classifying cities and revealing the features that characterize each city from a visual perspective. The study utilizes city view data captured from satellites and employs a methodology involving DL-based classification for city identification, along with an Explainable Artificial Intelligence (AI) tool to unveil definitive features of each city considered in this study. The city identification model implemented using the ResNet architecture yielded an overall accuracy of 84%, featuring 45 cities worldwide with varied geographic locations, Human Development Index (HDI), and population sizes. The portraying attributes of urban locations have been investigated using an explanatory visualization tool named Relevance Class Activation Maps (CAM). The methodology and findings presented by the current study enable decision makers, city managers, and policymakers to identify similar cities through satellite data, understand the salient features of the cities, and make decisions based on similarity patterns that can lead to effective solutions in a wide range of objectives such as urban planning, crisis management, and economic policies. Analyzing city similarities is crucial for urban development, transportation strategies, zoning, improvement of living conditions, fostering economic success, shaping social justice policies, and providing data for indices and concepts such as sustainability and smart cities for urban zones sharing similar patterns. Full article
(This article belongs to the Special Issue Artificial Intelligence and Buildings: Design, Analysis, and Construction)
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