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From Theory to Practice: Artificial Intelligence Applications in the Built...
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From Theory to Practice: Artificial Intelligence Applications in the 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 September 2026 | Viewed by 3491

Special Issue Editors

Dr. Yile Chen

E-Mail Website
Guest Editor
Faculty of Humanities and Arts, Macau University of Science and Technology, Taipa, 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, Taipa, Macau 999078, China
Interests: mathematical algorithms in design; optimization theory; diffusion models; machine learning; deep learning; generative design; computational geometry; parametric design; computer-aided design
Special Issues, Collections and Topics in MDPI journals
Dr. Yanyan Liang

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

Special Issue Information

Dear Colleagues,

The built environment encompasses multiple dimensions, including architectural design, construction, operations and maintenance, and urban planning. The application of artificial intelligence (AI) in the built environment has become a core focus of research and innovation in the field. This Special Issue aims to bridge the gap between AI theoretical advancements and real-world built environment scenarios, exploring practical application paths for technologies such as machine learning, big data analytics, and digital twins in architectural design, urban planning, facility operation and maintenance, and sustainability management. It emphasizes transforming abstract algorithms and models into concrete solutions, such as optimizing spatial layout, improving energy efficiency, enhancing disaster resilience, and improving the user experience in built spaces. By integrating interdisciplinary perspectives, this direction is dedicated to showcasing cutting-edge practical achievements, solving practical application challenges, and outlining a development blueprint for AI-driven intelligent, sustainable, and human-centered built environments.

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 digital restoration of historic buildings;
  • Data security and privacy protection in AI applications in the built environment.

Dr. Yile Chen
Dr. Junming Chen
Dr. Yanyan Liang
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 250 words) can be sent to the Editorial Office for assessment.

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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  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (7 papers)

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Research

36 pages, 4222 KB  
Article
Context-Adaptive Image Generation of Intangible Cultural Heritage Furniture for Architectural Interiors: A ComfyUI-Based AIGC Virtual Studio
by Jingting Meng, Jie Chen, Ziqi Zhang and Shaoyu Chen
Buildings 2026, 16(10), 1868; https://doi.org/10.3390/buildings16101868 - 8 May 2026
Viewed by 152
Abstract
To address the challenge of efficiently and cost-effectively generating images of intangible cultural heritage (ICH) furniture that can adapt to diverse modern spatial contexts for visual communication, this paper proposes and constructs an Generative Artificial Intelligence (AIGC) virtual studio system based on ComfyUI. [...] Read more.
To address the challenge of efficiently and cost-effectively generating images of intangible cultural heritage (ICH) furniture that can adapt to diverse modern spatial contexts for visual communication, this paper proposes and constructs an Generative Artificial Intelligence (AIGC) virtual studio system based on ComfyUI. The system is designed for ICH furniture designers, cultural communicators, and digital preservation practitioners, aiming to overcome the bottlenecks of scene switching encountered in traditional photography and 3D modeling. First, furniture images and user scene descriptions are collected, and a dual lexicon consisting of AI prompts and user prompts is constructed. The analytic hierarchy process (AHP) is then applied to weight and filter prompt combinations, forming a quantifiable and integrated prompt system. Second, a visual workflow incorporating ControlNet and IPAdapter nodes is built in ComfyUI to enable the transfer of ICH furniture images to various preset spatial scenes. Finally, a Likert-scale comparison is conducted between the experimental group (using AHP-weighted prompts) and the control group (using unweighted prompts). The results show that the experimental group achieves significant improvements in image realism, style consistency, and cultural communication effectiveness. The images generated by this system can be directly used for digital display, e-commerce product pages, design proposals, and cultural archives of ICH furniture. The method is applicable to the context-aware AIGC generation of traditional furniture and home products, provided that a certain amount of image data and a ComfyUI environment are available. This study provides a reusable technical pathway for the modern visual presentation of ICH furniture and offers methodological support and empirical evidence for the integration of AIGC into environmental design. Full article
(This article belongs to the Special Issue From Theory to Practice: Artificial Intelligence Applications in the Built Environment)
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27 pages, 16537 KB  
Article
Decoding Rent Determinants in Urban Housing Markets: A Multi-Perspective Multimodal Machine Learning Analysis
by Yueyi Tan and Jusheng Song
Buildings 2026, 16(9), 1787; https://doi.org/10.3390/buildings16091787 - 30 Apr 2026
Viewed by 330
Abstract
Urban housing rents are central to socioeconomic dynamics and urban sustainability, shaping affordability and quality of life. Existing research largely relies on linear models and focuses on economic, demographic, and locational factors, often neglecting complex nonlinear interactions and the impact of human perceptions. [...] Read more.
Urban housing rents are central to socioeconomic dynamics and urban sustainability, shaping affordability and quality of life. Existing research largely relies on linear models and focuses on economic, demographic, and locational factors, often neglecting complex nonlinear interactions and the impact of human perceptions. This study introduces a comprehensive, multi-perspective framework that integrates housing attributes, living convenience, competition, location, accessibility, and quantified perceptual metrics using multimodal machine learning. Advanced techniques, including XGBoost, SHAP, Partial Dependence Plots (PDPs), Interpretative Structural Modeling (ISM), and Bayesian Network (BN), capture nonlinearities, interactions, and hierarchical dependencies among rent determinants. Housing attributes and living convenience indicators exert the strongest cumulative influence on rents, while perceptual variables rank third, providing significant, threshold-dependent contributions and explaining up to 21.66% of rent variation. Notable interactions are identified between accessibility, facility density, and perceptual quality. The ISM–BN analysis uncovers multi-level pathways, demonstrating how both environmental features and human perceptions jointly influence rents. This framework offers actionable insights for equitable housing and urban planning policies, supporting data-driven decisions in complex urban rental markets. Full article
(This article belongs to the Special Issue From Theory to Practice: Artificial Intelligence Applications in the Built Environment)
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34 pages, 5296 KB  
Article
An Interpretable Pretrained Tabular Modeling Framework for Predicting IRI Across Multiple Pavement Structural Configurations
by Liang Qin, Tong Liu, Qianhui Sun and Mingxin Tang
Buildings 2026, 16(7), 1358; https://doi.org/10.3390/buildings16071358 - 29 Mar 2026
Viewed by 583
Abstract
With increasing traffic loads and increasingly complex climate conditions, accurate prediction of the International Roughness Index (IRI) of asphalt pavements is crucial for developing effective maintenance plans. However, traditional regression models have limitations in capturing the coupled effects of traffic, structure, and environmental [...] Read more.
With increasing traffic loads and increasingly complex climate conditions, accurate prediction of the International Roughness Index (IRI) of asphalt pavements is crucial for developing effective maintenance plans. However, traditional regression models have limitations in capturing the coupled effects of traffic, structure, and environmental factors. To overcome this limitation, this study constructed a dataset containing 10,836 samples based on the Long-Term Pavement Performance (LTPP) database, integrating traffic load, pavement structure parameters, and climate variables. The variance inflation factor (VIF) and correlation analysis were used to validate the effectiveness of feature selection. We trained nine machine learning models and optimized the hyperparameters using a Bayesian optimization method with five-fold cross-validation to ensure good generalization ability. Results show that the TabPFN model, based on prior information, achieved the best overall performance with a coefficient of determination R2 = 0.9474 and a low prediction error (RMSE = 0.138) on the test set. Paired t-tests based on cross-validation further confirmed that TabPFN’s predictive performance is statistically superior to the baseline model. SHAP and generalized additive model (GAM) analyses indicate that traffic load is the main driver of IRI growth, while structural layer thickness, within a certain range, can mitigate pavement roughness. Climatic factors have indirect long-term effects through cumulative environmental exposure. Although the main drivers differ slightly among different pavement structures, traffic load consistently plays a dominant role. To enhance the model’s practical applicability, we also developed a user-friendly graphical interface (GUI) for fast and accurate IRI prediction. Full article
(This article belongs to the Special Issue From Theory to Practice: Artificial Intelligence Applications in the Built Environment)
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36 pages, 5956 KB  
Article
A Knowledge-Augmented Two-Stage Workflow for Architectural Concept-to-Massing Generation and Evaluation
by Shangci Sun and Yao Fu
Buildings 2026, 16(6), 1265; https://doi.org/10.3390/buildings16061265 - 23 Mar 2026
Viewed by 578
Abstract
Large language models (LLMs) and diffusion-based image generators can rapidly produce architectural ideas and imagery, yet translating conceptual narratives into massing composition is often implicit and difficult to reproduce. In this paper, we present a knowledge-augmented two-stage workflow for architectural concept-to-massing generation and [...] Read more.
Large language models (LLMs) and diffusion-based image generators can rapidly produce architectural ideas and imagery, yet translating conceptual narratives into massing composition is often implicit and difficult to reproduce. In this paper, we present a knowledge-augmented two-stage workflow for architectural concept-to-massing generation and evaluation. The outputs are represented as axonometric massing proxy images, which serve as 2D visual proxies for early-stage massing refinement rather than editable 3D models. The workflow integrates a prototype library and Knowledge Graph (KG) routing to map narrative cues into executable strategy and operation tokens and compile stage-specific prompts. Stage 1 produces structural concept sketches emphasizing legible composition, while Stage 2 generates axonometric massing proxy images conditioned on Stage 1 sketches to stabilize composition across candidates. Under a fixed sampling budget, candidates are ranked using a rubric-based scoring protocol with Top-K selection, and evaluation signals can be written back to update prompt compilation iteratively. Across diverse project briefs, ablation studies demonstrate that knowledge augmentation improves constraint compliance and composition readability while maintaining controlled diversity for early exploration. We report expert ratings together with paired statistical tests to support reproducible comparisons. Full article
(This article belongs to the Special Issue From Theory to Practice: Artificial Intelligence Applications in the Built Environment)
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27 pages, 3038 KB  
Article
FCBD: A New Technology for Generating Bubble Charts Based on Functional Constraints in Indoor Layout Design
by Yingqi Shi, Yunqi Lin, Mingfeng Zhang, Jingran Liu, Zhen Tian and Junming Chen
Buildings 2026, 16(6), 1218; https://doi.org/10.3390/buildings16061218 - 19 Mar 2026
Viewed by 398
Abstract
Despite advances in deep learning for interior layout design, existing bubble diagram methods still rely on manual sketches or dataset retrieval. These methods use a binary functional relationship with simple adjacency logic, limiting end-to-end generation and integration of complex constraints. In contrast, bubble [...] Read more.
Despite advances in deep learning for interior layout design, existing bubble diagram methods still rely on manual sketches or dataset retrieval. These methods use a binary functional relationship with simple adjacency logic, limiting end-to-end generation and integration of complex constraints. In contrast, bubble diagrams can represent more complex, overlapping relationships. This paper proposes a Functional Constraint Bubble Diagram (FCBD) framework for intelligent layout generation. Annotated interior data are encoded into structured representations capturing spatial boundaries, room functions, area constraints, and user preferences. A dual-branch Transformer is employed, where the Node Transformer learns function-aware room representations and the Edge Transformer models adjacency relationships under environment-aware constraints to enhance spatial coherence. Latent-space sampling enables multi-solution generation, while an interactive refinement mechanism supports real-time user adjustments. The generated bubble diagrams drive floor plan synthesis and are evaluated on layout rationality, functional compatibility, visual quality, and diversity. Experimental results show that FCBD achieves a functional accuracy of 92.0%, adjacency accuracy of 88.9%, the lowest room overlap of 0.038, and the highest layout diversity of 1.245. Compared to baselines, FCBD improves functional and adjacency accuracy by up to 10%, reduces room overlap by over 25%, and generates more diverse and well-connected layouts, significantly reducing manual design effort. The end-to-end experimental results verify the validity of the generated topology and the practical value of the FCBD framework in intelligent interior design. Full article
(This article belongs to the Special Issue From Theory to Practice: Artificial Intelligence Applications in the Built Environment)
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32 pages, 6394 KB  
Article
A Machine-Learning Approach for Evaluating Perceived Walking Comfort in Macau’s High-Density Urban Environment
by Zhimu Gong, Junling Zhou, Xuefang Zhang, Lingfeng Xie, Guanxu Luo, Xiping Luo, Jiayi Fu, Yitong Guo and Xiaoyan Zhi
Buildings 2026, 16(6), 1103; https://doi.org/10.3390/buildings16061103 - 10 Mar 2026
Viewed by 478
Abstract
Evaluating pedestrian comfort in high-density cities requires methods integrating subjective experience with urban morphology. This study develops an integrated framework combining pairwise comparison scoring, semantic segmentation (DeepLabv3+), ensemble learning (Random Forest), and SHAP-based interpretability. EfficientNet-B7 is used to expand pairwise datasets and derive [...] Read more.
Evaluating pedestrian comfort in high-density cities requires methods integrating subjective experience with urban morphology. This study develops an integrated framework combining pairwise comparison scoring, semantic segmentation (DeepLabv3+), ensemble learning (Random Forest), and SHAP-based interpretability. EfficientNet-B7 is used to expand pairwise datasets and derive continuous comfort scores across Macau’s street network. Four experiential street types are identified: historical–cultural districts, urban lifestyle areas, natural corridors, and leisure zones. SHAP analysis illustrates stable associations between predicted comfort scores and multi-layered spatial configurations, including cultural legibility and sequencing in historic cores, moderate greenery with functional anchoring in residential areas, and scene coherence in tourism zones. Semantic features serve as effective morphological proxies within the modeling framework. Methodologically, the framework demonstrates how explainable machine learning can be applied to dense Asian cities under observational conditions. Design implications emphasize interface continuity, microclimate adaptation, and functional enrichment, suggesting that pedestrian comfort is closely related to coherent spatial–experiential structures rather than isolated environmental upgrades. Full article
(This article belongs to the Special Issue From Theory to Practice: Artificial Intelligence Applications in the Built Environment)
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35 pages, 4004 KB  
Article
Breaking Rework Chains in Low-Carbon Prefabrication: A Hybrid Evolutionary Scheduling Framework
by Yixuan Tang, Xintong Li and Yingwen Yu
Buildings 2026, 16(5), 968; https://doi.org/10.3390/buildings16050968 - 1 Mar 2026
Viewed by 389
Abstract
Achieving sustainability in prefabricated construction necessitates a balance between operational efficiency and stringent environmental constraints. However, cascading rework chains triggered by assembly defects frequently disrupt this equilibrium. Existing literature predominantly addresses this dynamic through reactive rescheduling, thereby largely overlooking the potential of proactive [...] Read more.
Achieving sustainability in prefabricated construction necessitates a balance between operational efficiency and stringent environmental constraints. However, cascading rework chains triggered by assembly defects frequently disrupt this equilibrium. Existing literature predominantly addresses this dynamic through reactive rescheduling, thereby largely overlooking the potential of proactive topological interception. To bridge this gap, this study proposes a proactive bi-level scheduling framework that mathematically integrates strategic quality inspection planning with operational low-carbon project execution. Specifically, a Generalized Total Cost (GTC) model is formulated to internalize multi-objective trade-offs—including time, cost, and carbon emissions—into a unified financial metric through market-based shadow prices. This framework is operationalized through a novel bi-level Hybrid Evolutionary Algorithm (H-TS-CDBO). By combining the global exploration capabilities of Chaotic Dung Beetle Optimization with the local refinement mechanisms of Tabu Search, the proposed solver is specifically engineered to navigate the topological ruggedness induced by proactive inspection interventions. Empirical benchmarking validates the computational robustness of the solver, while an illustrative case study substantiates a critical managerial paradigm shift from “passive remediation” to “active prevention”: compared to traditional methods, a marginal preventive investment of 5.4% functions as an effective containment mechanism, yielding a 40.8% net reduction in the GTC. Furthermore, a sensitivity analysis regarding varying static carbon tax rates simulates algorithmic adaptation under diverse regulatory intensity thresholds, delineating an actionable pathway for project managers to achieve lean, low-carbon synergy amidst evolving regulatory pressures. Full article
(This article belongs to the Special Issue From Theory to Practice: Artificial Intelligence Applications in the Built Environment)
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