Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.6
3.4
Journals
Buildings
Special Issues
Towards Innovative Approaches in Urban Science and Architectural Research Using...
share announcement

Towards Innovative Approaches in Urban Science and Architectural Research Using Artificial Intelligence

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: 31 January 2027 | Viewed by 1329

Editors

Dr. Baohua Wen

E-Mail Website
Guest Editor
School of Architecture and Planning, Hunan University, Changsha 410082, China
Interests: architectural design and theory; vernacular architecture; building sustainability
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ewa Janina Grabska

E-Mail Website
Guest Editor
Department of Design and Computer Graphics, Jagiellonian University, 31-007 Kraków, Poland
Interests: architectural design; visual communication; artificial intelligence; creativity and innovation; design theory
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to present the opportunities and challenges brought by the profound advances in artificial intelligence to the fields of urban science and architecture. From generative design to urban morphology analysis, from architectural heritage conservation to site perception based on computer vision. Artificial intelligence not only redefines the capabilities of design tools but also actively supports the emergence of new research paradigms.

The editors of this Special Issue aim to present the latest research from global scientists, practitioners and technical experts on artificial intelligence methods that support innovative solutions in two complementary interdisciplinary fields: urban planning and architecture.

Original research articles are invited on topics related to urban design, architecture and interdisciplinary fields. Suggested themes include, but are not limited to:

  1. Generative design and artificial intelligence-driven innovative design methods;
  2. Urban space perception and cognition based on computer vision;
  3. Digital pathways for architectural heritage conservation with artificial intelligence interventions;
  4. Performance simulation analysis and intelligent decision support;
  5. Intelligent spatial design and aesthetic evaluation;
  6. Paradigm shifts in human–artificial intelligence collaborative design;
  7. Deep learning–driven research paradigms;
  8. Spatial intelligence and urban data mining;
  9. Intelligent construction and facility management;
  10. Interdisciplinary integration and new methods in urban science;
  11. AI-assisted design solutions for challenges such as climate change.

Dr. Baohua Wen
Prof. Dr. Ewa Janina Grabska
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-anonymized 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

  • design paradigm shifts
  • spatial intelligence
  • urban design
  • architectural heritage conservation
  • computer vision
  • generative design
  • intelligent decision-making
  • deep learning

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • 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 (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

23 pages, 8173 KB  
Article
A Machine-Learning-Supplemented Parametric Framework for Early-Stage Stadium Design Analysis and Optimisation
by Yakim Milev and Sam Jacoby
Buildings 2026, 16(12), 2409; https://doi.org/10.3390/buildings16122409 - 17 Jun 2026
Viewed by 304
Abstract
This paper investigates machine learning (ML)-supplemented workflows integrated within a modular parametric modelling framework derived from a typological analysis of stadiums. The objective of the research is to address a gap between numerous isolated computational studies and the realities of early stadium design [...] Read more.
This paper investigates machine learning (ML)-supplemented workflows integrated within a modular parametric modelling framework derived from a typological analysis of stadiums. The objective of the research is to address a gap between numerous isolated computational studies and the realities of early stadium design within the Royal Institute of British Architects (RIBA) Plan of Work (PoW) Stages 0–3. From a practical perspective, the proposed design framework aims to embed supervised learning, semi-supervised learning, and evolutionary optimisation into stadium design development to support site appraisal, brief preparation, concept development, spatial coordination, and stadium bay or stand optimisation based on quantifiable design characteristics. The framework addresses the inefficiencies and limitations of the traditional stadium design process by allowing rapid design space exploration defined by typological drivers, evaluation of a large set of solutions based on performance metrics such as circulation distances, sightline quality, and layout distribution, and the validation of concepts against benchmarks. Within the applicable design pipelines, and where labels are derived from deterministic performance criteria, the supervised approaches achieved prediction accuracies above 85%, while evolutionary optimisation reduced the number of seats with restricted views by approximately 95%. The value of the study is that it demonstrates that the integration of parametric modelling based on shared typological characteristics and the mapping of ML methods to the RIBA PoW has the potential to support stadium design in a novel way. Full article
(This article belongs to the Special Issue Towards Innovative Approaches in Urban Science and Architectural Research Using Artificial Intelligence)
►▼ Show Figures

Figure 1

31 pages, 7311 KB  
Article
ArchiExplain: Multi-Level Evidence Chains for Precedent-Based Interpretability in Architectural Image Understanding
by Jun Yin, Peilin Li, Tianrui Li, Jing Zhong, Zhanxiang Jin, Tianjing Feng and Peter Russell
Buildings 2026, 16(12), 2394; https://doi.org/10.3390/buildings16122394 - 16 Jun 2026
Viewed by 338
Abstract
Deep neural networks have been widely applied in architectural analysis and design research, supporting tasks such as facade recognition, floor-plan analysis, and architectural visual classification. However, although existing models possess strong predictive capabilities, their decision-making processes remain characterized by a pronounced black-box nature, [...] Read more.
Deep neural networks have been widely applied in architectural analysis and design research, supporting tasks such as facade recognition, floor-plan analysis, and architectural visual classification. However, although existing models possess strong predictive capabilities, their decision-making processes remain characterized by a pronounced black-box nature, making it difficult to provide architects with understandable and traceable grounds for judgment. This limits their practical value in the architectural field, as designers require not only accurate outputs but also interpretable explanatory evidence regarding the basis of decision-making. This issue is particularly critical in architectural interpretation, where judgments are rarely made solely on the basis of isolated visual features, but are instead often formed through comparison and negotiation with precedents, spatial logic, and domain knowledge. To address this challenge, this paper proposes ArchiExplain, a multi-level interpretability framework for architectural image understanding, aiming to enable a deeper understanding of architectural images. The main contributions of this study are threefold: (1) We construct two architectural datasets for interpretability evaluation: a facade dataset composed of streetscape images from Harbin, China, and Greece, and a floor-plan dataset consisting of Real-plan drawings from real design cases and standardized generated R-plan drawings. Unlike existing datasets that primarily serve style recognition, semantic parsing, or image generation tasks, the datasets in this paper focus on evaluating the correspondence among model explanations, precedent associations, visual evidence, and predictive judgments. (2) Based on the above datasets, we propose the ArchiExplain framework. Unlike attribution methods such as Grad-CAM, Saliency Maps, and Integrated Gradients, which mainly reveal local discriminative regions, or influence-based methods that only trace the influence of training samples, this framework integrates training-sample influence tracing, Saliency Maps, and Integrated Gradients. It establishes a unified evidential chain among precedent samples, discriminative image regions, and final predictions, thereby transforming neural network decisions into an interpretable reasoning process with architectural significance. (3) Experimental results show that ArchiExplain performs stably on 100 randomly selected test samples, achieving an accuracy of 98.41% in the facade classification task and 98.34% in the floor-plan classification task. Further deletion/occlusion faithfulness analysis shows that the main attribution methods outperform the random baseline. Meanwhile, a questionnaire study involving 28 architects further verifies the consistency between model explanations and human architectural cognition. These findings indicate that ArchiExplain can enhance the transparency of architectural deep learning models and has practical application potential in architectural design analysis, model diagnosis, and precedent-based learning. Full article
(This article belongs to the Special Issue Towards Innovative Approaches in Urban Science and Architectural Research Using Artificial Intelligence)
►▼ Show Figures

Graphical abstract

28 pages, 84354 KB  
Article
Optimization of Residential Building Design Elements for Energy Efficiency in Hot Summer and Cold Winter Regions Using Energy Simulation and GBDT: A Case Study of Rural Housing in Hangzhou
by Huan Zhang, Yuanzhan Zhu, Yukuan Li, Dian Gu, Yujia Chen and Jie Wang
Buildings 2026, 16(12), 2335; https://doi.org/10.3390/buildings16122335 - 11 Jun 2026
Viewed by 324
Abstract
The escalating energy consumption in China’s rural residences necessitates the adoption of targeted energy-efficient design strategies. However, existing studies have mainly focused on urban buildings or cold-climate rural residences, and insufficient attention has been given to form-based energy optimization for rural housing in [...] Read more.
The escalating energy consumption in China’s rural residences necessitates the adoption of targeted energy-efficient design strategies. However, existing studies have mainly focused on urban buildings or cold-climate rural residences, and insufficient attention has been given to form-based energy optimization for rural housing in hot summer and cold winter regions. Hangzhou was selected because it is a representative city in this climate zone, where rural residences face both summer cooling and winter heating demands. This study systematically investigates passive design pathways for rural residential buildings by optimizing architectural forms. We conducted in-depth field surveys and data analysis on 76 diverse samples, including both self-built and unified construction types, to establish three representative typical residential models (rectangular, L-shaped, U-shaped) for the Hangzhou region. DesignBuilder was employed to simulate the impacts of eight morphological elements—Shape Coefficient, building area, aspect ratio, orientation, number of floors, floor height, floor height ratio, and roof slope—on building energy consumption. The Gradient Boosting Decision Tree (GBDT) method was then used to quantify the nonlinear effects and relative importance of these elements. The results indicate clear nonlinear relationships between elements and the energy-saving rate. Floor height is identified as the most critical factor affecting energy consumption, followed by roof slope, with building area and other elements also showing significant influence. Based on the quantitative analysis, this study proposes energy-efficient design optimization strategies for rural housing in Hangzhou, offering a validated methodological framework and practical design references for the sustainable development of rural residences in hot summer and cold winter regions. Full article
(This article belongs to the Special Issue Towards Innovative Approaches in Urban Science and Architectural Research Using Artificial Intelligence)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop