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Practice and Application of Artificial Intelligence in Urban Decision-Making
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Practice and Application of Artificial Intelligence in Urban Decision-Making

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

Deadline for manuscript submissions: 30 November 2025 | Viewed by 3612

Special Issue Editors

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
Dr. Yile Chen

E-Mail Website
Guest Editor
Heritage Conservation Laboratory, 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: application of artificial intelligence in design; diffusion model; grasshopper; machine learning; deep learning; image generation; parametric design; computer-aided design

Special Issue Information

Dear Colleagues,

With the rapid development of science and technology, artificial intelligence (AI) is gradually penetrating into all aspects of urban development, especially in the field of urban decision-making. Its application in urban planning decision-making has broad prospects. In particular, new technologies such as big data intelligence, human–machine hybrid enhanced intelligence, Internet group intelligence, and cross-media intelligence will play an important role in expanding decision-making information, improving decision-making technology, enriching decision-making subjects, and strengthening decision-making feedback. The application of AI technology has brought new ideas and methods to urban planning, construction, and management, which will again be optimized by the development and application of a new generation of artificial intelligence. This Special Issue provides a platform for the discussion and exchange of ideas, designs, and technical knowledge that will help promote the practice and application of artificial intelligence in urban decision-making. This Special Issue is open to submissions on any thematic area related to artificial intelligence in urban decision-making. Research papers, analytical reviews, case studies, conceptual frameworks, and policy-related articles are welcome.

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

  • The application of artificial intelligence in urban planning decision-making;
  • The stages and analysis of AI technology intervention in urban decision-making;
  • The use of AI technology to analyze urban spaces;
  • Optimizing the layout of urban or building spaces;
  • The use of AI in architectural design decisions;
  • The practice of optimizing architectural design parameters;
  • Personalized architectural designs using AI support;
  • Innovative practices in smart city construction decision-making;
  • Smart community construction;
  • Innovative AI applications in urban emergency management decision-making.

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

  • decision-making
  • urban or building space
  • construction management practice
  • urban planning and management
  • artificial intelligence technology support
  • layout optimization
  • building parameter optimization

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

Published Papers (6 papers)

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Research

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20 pages, 2255 KiB  
Article
Toward Symmetry in Accessible Restrooms Design: Integrating KE, RST, and SVM for Optimized Emotional-Functional Alignment
by Zimo Chen, Jingwen Tian, Hongtao Zhou and Duan Wu
Buildings 2025, 15(9), 1567; https://doi.org/10.3390/buildings15091567 - 6 May 2025
Viewed by 158
Abstract
Accessible restrooms must reconcile code-based functionality with the affective expectations of disabled users. This study develops an integrated Kansei Engineering (KE)–Rough Set Theory (RST)–Support Vector Machine (SVM) workflow that converts user emotions into verifiable design guidelines. Surveys and semi-structured interviews with 50 disabled [...] Read more.
Accessible restrooms must reconcile code-based functionality with the affective expectations of disabled users. This study develops an integrated Kansei Engineering (KE)–Rough Set Theory (RST)–Support Vector Machine (SVM) workflow that converts user emotions into verifiable design guidelines. Surveys and semi-structured interviews with 50 disabled participants produced nine Kansei words; factor analysis extracted three principal emotional factors—tidiness, utility and care—capturing 75.8% of total variance. The morphological decomposition of 60 restroom samples yielded 41 design attributes, from which RST attribute reduction isolated six critical features. An SVR model with a radial-basis kernel, trained on 90% of the data and validated on the remaining 10%, achieved R2 = 0.931 and RMSE = 0.085. The exhaustive prediction of 15,750 feasible design combinations pinpointed an optimal configuration; follow-up user testing confirmed the improvement in satisfaction (mean 5.1 on a seven-point scale). The KE–RST–SVM workflow thus offers a reproducible, data-driven path for harmonizing emotional and functional objectives in inclusive restroom design, and can be extended to other barrier-free facilities. Full article
(This article belongs to the Special Issue Practice and Application of Artificial Intelligence in Urban Decision-Making)
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24 pages, 5543 KiB  
Article
Study on the Influence Mechanism of Machine-Learning-Based Built Environment on Urban Vitality in Macau Peninsula
by Chen Pan, Jiaming Guo, Haibo Li, Jiawei Wu, Nengjie Qiu and Shengzhen Wu
Buildings 2025, 15(9), 1557; https://doi.org/10.3390/buildings15091557 - 5 May 2025
Viewed by 504
Abstract
Clarifying the mechanisms by which the micro-scale built environment influences urban vitality is an important scientific challenge, to guide precise urban planning in the context of urban renewal. In this study, we quantify the intensity of human activities through Baidu heat maps, analyze [...] Read more.
Clarifying the mechanisms by which the micro-scale built environment influences urban vitality is an important scientific challenge, to guide precise urban planning in the context of urban renewal. In this study, we quantify the intensity of human activities through Baidu heat maps, analyze social interaction patterns using social media check-in data, and integrate built environment elements such as road network topology, 3D building morphology, and the spatial distribution of points of interest (POIs). A machine learning technique combining a real-encoded Accelerated Genetic Algorithm-Projective Pathfinding Model (RAGA-PPM) and Shapley Additive Projection for Interpretability (SHAP) for Interpretability Analysis (IPA) was used to investigate the nonlinear mechanisms of 17 factors affecting urban vitality in Macau Peninsula, China. Firstly, the explanatory power of the built environment for comprehensive vitality was significantly better than the other dimensions. Two factors, population vitality and microblogging check-in vitality, contributed the most to the composite vitality value. Secondly, road network density was the most important built environment factor affecting urban vitality in Macau Peninsula (SHAP = 0.025). Finally, the impacts of built environment factors on urban vitality showed nonlinearities, and the threshold effects of the core factors (road network density, spatial fractal dimension, and openness to the sky) showed a consistent neighborhood-level pattern. This study establishes a framework for micro-vitality mechanisms in high-density cities, addressing the limitations of traditional methods in modeling complex nonlinear relationships. The methodological integration of RAGA-PPM and SHAP advances the innovative paradigm of applying interpretable machine learning to the study of urban form. Full article
(This article belongs to the Special Issue Practice and Application of Artificial Intelligence in Urban Decision-Making)
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19 pages, 3555 KiB  
Article
Research on Park Perception and Understanding Methods Based on Multimodal Text–Image Data and Bidirectional Attention Mechanism
by Kangen Chen, Xiuhong Lin, Tao Xia and Rushan Bai
Buildings 2025, 15(9), 1552; https://doi.org/10.3390/buildings15091552 - 4 May 2025
Viewed by 392
Abstract
Parks are an important component of urban ecosystems, yet traditional research often relies on single-modal data, such as text or images alone, making it difficult to comprehensively and accurately capture the complex emotional experiences of visitors and their relationships with the environment. This [...] Read more.
Parks are an important component of urban ecosystems, yet traditional research often relies on single-modal data, such as text or images alone, making it difficult to comprehensively and accurately capture the complex emotional experiences of visitors and their relationships with the environment. This study proposes a park perception and understanding model based on multimodal text–image data and a bidirectional attention mechanism. By integrating text and image data, the model incorporates a bidirectional encoder representations from transformers (BERT)-based text feature extraction module, a Swin Transformer-based image feature extraction module, and a bidirectional cross-attention fusion module, enabling a more precise assessment of visitors’ emotional experiences in parks. Experimental results show that compared to traditional methods such as residual network (ResNet), recurrent neural network (RNN), and long short-term memory (LSTM), the proposed model achieves significant advantages across multiple evaluation metrics, including mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), and the coefficient of determination (R2). Furthermore, using the SHapley Additive exPlanations (SHAP) method, this study identified the key factors influencing visitors’ emotional experiences, such as “water”, “green”, and “sky”, providing a scientific basis for park management and optimization. Full article
(This article belongs to the Special Issue Practice and Application of Artificial Intelligence in Urban Decision-Making)
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26 pages, 17239 KiB  
Article
Optimizing Urban Block Morphology for Energy Efficiency and Photovoltaic Utilization: Case Study of Wuhan
by Ruoyao Wang, Yanyan Huang, Guoliang Zhang, Yi Yang and Qizhi Dong
Buildings 2025, 15(7), 1118; https://doi.org/10.3390/buildings15071118 - 29 Mar 2025
Viewed by 397
Abstract
With global carbon emissions continuing to rise and urban energy demands growing steadily, understanding how urban block morphology impacts building photovoltaic (PV) efficiency and energy consumption has become crucial for sustainable urban development and climate change mitigation. Current research primarily focuses on individual [...] Read more.
With global carbon emissions continuing to rise and urban energy demands growing steadily, understanding how urban block morphology impacts building photovoltaic (PV) efficiency and energy consumption has become crucial for sustainable urban development and climate change mitigation. Current research primarily focuses on individual building optimization, while block-scale coupling relationships between PV utilization and energy consumption remain underexplored. This study developed an integrated prediction and optimization tool using deep learning and physical simulation to assess how urban block design parameters (building morphology, orientation, and layout) affect PV efficiency and energy performance. Through a methodology combining block modeling, PV potential assessment, and energy consumption simulation, the research quantified relationships between design parameters, PV utilization, and energy consumption. Results demonstrate that appropriate building forms and layouts reduce shadow obstruction, enhance PV system capability, and simultaneously improve PV efficiency while reducing energy consumption. The tool provides improved prediction accuracy, enabling urban planners to scientifically design block layouts that maximize PV generation and minimize energy use. Extensive experimental validation demonstrates that the integrated model and analytical methods proposed in this study will help urban planners break through the limitations of individual building research, making PV-energy consumption optimization analysis at the block scale possible, and providing scientific basis for achieving low-carbon transformation and sustainable energy development in the building sector. Full article
(This article belongs to the Special Issue Practice and Application of Artificial Intelligence in Urban Decision-Making)
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20 pages, 4627 KiB  
Article
Harnessing the Power of Improved Deep Learning for Precise Building Material Price Predictions
by Zhilong Guo, Yayong Luo, Tongqiang Yi, Xiangnan Jing and Jing Ma
Buildings 2025, 15(6), 873; https://doi.org/10.3390/buildings15060873 - 11 Mar 2025
Viewed by 585
Abstract
Accurate forecasting of construction material prices is essential for effective cost control and risk management in construction projects. However, due to the influence of various complex factors, building material prices exhibit high nonlinearity and instability, often making traditional prediction methods inadequate for achieving [...] Read more.
Accurate forecasting of construction material prices is essential for effective cost control and risk management in construction projects. However, due to the influence of various complex factors, building material prices exhibit high nonlinearity and instability, often making traditional prediction methods inadequate for achieving optimal results. This study introduces an innovative prediction model, CEEMDAN-VMD-GRU-ARIMA, specifically designed for forecasting the price of prestressed steel bars. This model uniquely combines CEEMDAN and VMD to address nonlinear characteristics, and it innovatively incorporates sample entropy for the adaptive selection of either GRU or ARIMA for prediction. Additionally, a VMD decomposition mode number K value optimization method, based on a sparse index, is proposed. Experimental results demonstrate that the model performs exceptionally well, achieving an adjusted R-squared value of 81.10%, with various error indicators significantly surpassing the results for the baseline model. This approach offers new insights for short-term price prediction of building materials and contributes to enhancing the economic benefits and management efficiency of construction projects. Full article
(This article belongs to the Special Issue Practice and Application of Artificial Intelligence in Urban Decision-Making)
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Review

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34 pages, 8012 KiB  
Review
Machine Learning for Resilient and Sustainable Cities: A Bibliometric Analysis of Smart Urban Technologies
by Bin Luan and Xinqun Feng
Buildings 2025, 15(7), 1007; https://doi.org/10.3390/buildings15071007 - 21 Mar 2025
Viewed by 410
Abstract
With the acceleration of urbanization, the construction of smart cities has become a global focal point, with machine learning technology playing a crucial role in this process. This study aims to conduct a bibliometric analysis of the published research in the fields of [...] Read more.
With the acceleration of urbanization, the construction of smart cities has become a global focal point, with machine learning technology playing a crucial role in this process. This study aims to conduct a bibliometric analysis of the published research in the fields of smart cities and machine learning, using visualization techniques to reveal the spatiotemporal distribution patterns, research hotspots, and collaborative network structures. The goal is to provide systematic references for academic research and technological innovation in related fields. The results indicate that the development of this field exhibits distinct phases and regional characteristics. From a temporal perspective, research has undergone three stages: initial development, rapid growth, and stable consolidation, with the period from 2017 to 2021 marking a critical phase of rapid expansion. In terms of spatial distribution, countries such as China and the United States are at the forefront of this field, whereas regions like Africa and South America have a relatively low research output due to constraints in research resources and technological infrastructure. A hotspot analysis revealed that research topics are increasingly diverse and dynamically evolving. Issues such as data privacy, cybersecurity, sustainable development, and intelligent transportation have gradually become focal points, reflecting the dual demand of smart city development for technological innovation and green growth. Furthermore, collaboration network analysis indicates that international academic cooperation is becoming increasingly close, with research institutions in China, the United States, and Europe playing a central role in the global collaboration system, thereby promoting technology sharing and interdisciplinary integration. Through a systematic bibliometric analysis, this study identifies key application directions and future development trends in the research on smart cities and machine learning, providing valuable insights for academic research and technological advancements in related fields. Full article
(This article belongs to the Special Issue Practice and Application of Artificial Intelligence in Urban Decision-Making)
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