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Practice and Application of Artificial Intelligence in Built Environment
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Practice and Application of Artificial Intelligence in Built Environment

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Architectural Design, Urban Science, and Real Estate".

Deadline for manuscript submissions: 30 March 2026 | Viewed by 633

Special Issue Editors

Dr. Yanyan Liang

E-Mail Website
Guest Editor
Faculty of Innovation Engineering, Macau University of Science and Technology, Macau 999078, China
Interests: artificial intelligence; machine learning; deep learning; computer vision; image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Yile Chen

E-Mail Website
Guest Editor
Faculty of Humanities and Arts, Macau University of Science and Technology, Macau 999078, China
Interests: urban design and renewal; Lingnan historical buildings; urban morphology; machine learning (CGAN and YOLO)
Special Issues, Collections and Topics in MDPI journals
Dr. Junming Chen

E-Mail Website
Guest Editor
Faculty of Humanities and Arts, Macau University of Science and Technology, Macau 999078, China
Interests: application of artificial intelligence in design; diffusion model; grasshopper; machine learning; deep learning; image generation; parametric design; computer-aided design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid advancement of science and technology, artificial intelligence (AI) is gradually permeating all aspects of urban development, with the built environment being a key branch. This Special Issue focuses on the deep integration of artificial intelligence (AI) technology with the entire life cycle of human settlements, including buildings, cities, and infrastructure, exploring the paths and value of technology implementation. The built environment encompasses multiple dimensions, including architectural design, construction, operations and maintenance, and urban planning. The introduction of AI technologies (such as machine learning, computer vision, natural language processing, and digital twins) is driving its transformation from traditional experience-driven to data-driven, intelligent decision-making. This theme focuses on both the practical effectiveness of technology applications (such as improving design efficiency, optimizing construction safety, and reducing energy consumption) and their adaptability to complex real-world scenarios (such as AI applications in the preservation of historic buildings and the intelligent governance of high-density cities). It also balances ethical standards and sustainable development goals, making it a crucial frontier for interdisciplinary integration.

Topics covered in this Special Issue include, but are not limited to, the following:

  • Applications for AI in the built environment;
  • Stages and analysis of AI technology in the built environment;
  • Using AI to analyze urban space;
  • Optimizing urban or architectural space layout;
  • Applications for AI in interior design;
  • Practical practices in optimizing architectural design parameters;
  • Applications of AI in architectural design solutions, performance optimization (e.g., daylighting and energy consumption simulation), and the digital restoration of historic buildings;
  • Data security and privacy protection in AI applications in the built environment.

Dr. Yanyan Liang
Dr. Yile Chen
Dr. Junming Chen
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 semimonthly 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

  • built environment
  • urban or architectural space
  • construction management practices
  • architecture and management
  • artificial intelligence technology support
  • layout optimization

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

Published Papers (3 papers)

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Research

19 pages, 3807 KB  
Article
Graph-RWGAN: A Method for Generating House Layouts Based on Multi-Relation Graph Attention Mechanism
by Ziqi Ye, Sirui Liu, Zhen Tian, Yile Chen, Liang Zheng and Junming Chen
Buildings 2025, 15(19), 3623; https://doi.org/10.3390/buildings15193623 - 9 Oct 2025
Abstract
We address issues in existing house layout generation methods, including chaotic room layouts, limited iterative refinement, and restricted style diversity. We propose Graph-RWGAN, a generative adversarial network based on a multi-relational graph attention mechanism, to automatically generate reasonable and globally consistent house layouts [...] Read more.
We address issues in existing house layout generation methods, including chaotic room layouts, limited iterative refinement, and restricted style diversity. We propose Graph-RWGAN, a generative adversarial network based on a multi-relational graph attention mechanism, to automatically generate reasonable and globally consistent house layouts under weak constraints. In our framework, rooms are represented as graph nodes with semantic attributes. Their spatial relationships are modeled as edges. Optional room-level objects can be added by augmenting node attributes. This allows for object-aware layout generation when needed. The multi-relational graph attention mechanism captures complex inter-room relationships. Iterative generation enables stepwise layout optimization. Fusion of node features with building boundaries ensures spatial accuracy and structural coherence. A conditional graph discriminator with Wasserstein loss constrains global consistency. Experiments on the RPLAN dataset show strong performance. FID is 92.73, SSIM is 0.828, and layout accuracy is 85.96%. Room topology accuracy reaches 95%, layout quality 90%, and structural coherence 95%, outperforming House-GAN, LayoutGAN, and MR-GAT. Ablation studies confirm the effectiveness of each key component. Graph-RWGAN shows strong adaptability, flexible generation under weak constraints, and multi-style layouts. It provides an efficient and controllable scheme for intelligent building design and automated planning. Full article
(This article belongs to the Special Issue Practice and Application of Artificial Intelligence in Built Environment)
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32 pages, 1936 KB  
Article
Optimizing University Campus Functional Zones Using Landscape Feature Recognition and Enhanced Decision Tree Algorithms: A Study on Spatial Response Differences Between Students and Visitors
by Xiaowen Zhuang, Yi Cai, Zhenpeng Tang, Zheng Ding and Christopher Gan
Buildings 2025, 15(19), 3622; https://doi.org/10.3390/buildings15193622 - 9 Oct 2025
Abstract
As universities become increasingly open, campuses are no longer only places for study and daily life for students and faculty, but also essential spaces for public visits and cultural identity. Traditional perception evaluation methods that rely on manual surveys are limited by sample [...] Read more.
As universities become increasingly open, campuses are no longer only places for study and daily life for students and faculty, but also essential spaces for public visits and cultural identity. Traditional perception evaluation methods that rely on manual surveys are limited by sample size and subjective bias, making it challenging to reveal differences in experiences between groups (students/visitors) and the complex relationships between spatial elements and perceptions. This study uses a comprehensive open university in China as a case study to address this. It proposes a research framework that combines street-view image semantic segmentation, perception survey scores, and interpretable machine learning with sample augmentation. First, full-sample modeling is used to identify key image semantic features influencing perception indicators (nature, culture, aesthetics), and then to compare how students and visitors differ in their perceptions and preferences across campus spaces. To overcome the imbalance in survey data caused by group–space interactions, the study applies the CTGAN method, which expands minority samples through conditional generation while preserving distribution authenticity, thereby improving the robustness and interpretability of the model. Based on this, attribution analysis with an interpretable decision tree algorithm further quantifies semantic features’ contribution, direction, and thresholds to perceptions, uncovering heterogeneity in perception mechanisms across groups. The results provide methodological support for perception evaluation of campus functional zones and offer data-driven, human-centered references for campus planning and design optimization. Full article
(This article belongs to the Special Issue Practice and Application of Artificial Intelligence in Built Environment)
20 pages, 3219 KB  
Article
An Interpretable Machine Learning Approach to Studying Environmental Safety Perception Among Elderly Residents in Pocket Parks
by Shengzhen Wu, Sichao Wu, Jingru Chen and Chen Pan
Buildings 2025, 15(18), 3411; https://doi.org/10.3390/buildings15183411 - 20 Sep 2025
Viewed by 353
Abstract
This research explores the environmental safety challenges faced by pocket parks in the context of urban aging within Chinese cities. It systematically analyzes visual elements that influence the elderly’s perception of environmental safety by applying interpretable machine learning techniques. By integrating panoramic image [...] Read more.
This research explores the environmental safety challenges faced by pocket parks in the context of urban aging within Chinese cities. It systematically analyzes visual elements that influence the elderly’s perception of environmental safety by applying interpretable machine learning techniques. By integrating panoramic image semantic segmentation and explainable AI models (e.g., SHAP and PDP), the study transforms subjective environmental perception into measurable indicators and constructs an environmental safety perception model using the LightGBM algorithm. Results indicate that sufficient pedestrian areas and moderate crowd activities significantly enhance safety perception among the elderly. Conversely, the presence of cars emerges as the most substantial adverse factor. Natural elements, such as vegetation and grass, exhibit nonlinear effects on safety perception, with an optimal threshold range identified. The research further elucidates the intricate synergies and constraints among visual elements, underscoring that the highest perceived safety arises from the synergistic combination of positive factors. This study deepens the understanding of environmental perception among the elderly and offers a data-driven framework and practical guidelines for urban planners and designers. It holds significant theoretical and practical implications for advancing the refined and human-centered renewal of urban public spaces. Full article
(This article belongs to the Special Issue Practice and Application of Artificial Intelligence in Built Environment)
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