Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.4
3.1
Journals
Buildings
Special Issues
Artificial Intelligence in Architecture and Interior Design
share announcement

Artificial Intelligence in Architecture and Interior Design

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 March 2026 | Viewed by 4834

Special Issue Editors

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
Dr. Baohua Wen

E-Mail Website
Guest Editor
School of Architecture and Planning, Hunan University, Changsha 410082, China
Interests: architectural design; green buildings; vernacular architecture; sustainable development of urban and rural areas; computer vision; convolutional neural networks

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to present developments in architecture and interior design from the perspective of both opportunities and challenges resulting from the far-reaching advances in the field of artificial intelligence (AI). From generative design to intelligent spatial management, from sustainability analysis to user experiences and preferences, AI is not only redefining the possibilities of creative tools but also proposing its own research paradigms.

In addition, this Special Issue aims to gather cutting-edge research from global scholars, practitioners, and technical experts to explore how AI empowers design innovation and spatial creation, driving both industry practices and scientific research in architecture and interior design toward an intelligent future.

The following topics are suggested for papers related to architecture and/or interior design, but authors do not have to limit themselves to them:

  1. Generative and AI-Driven Innovation;
  2. Performance Analysis and Intelligent Decision-Making;
  3. Evolutionary Design and Aesthetic Evaluation;
  4. Human–AI Collaborative Design Paradigm Shifts;
  5. Deep Learning-Driven Research Paradigms;
  6. User Experience and Spatial Intelligence;
  7. Intelligent Construction and Facility Management;
  8. AI Interventions in Cultural Heritage;
  9. Cross-Disciplinary Integration;
  10. Future Directions in the AI Era;
  11. AI-Powered Design Solutions for Climate Change and Related Challenges.

Prof. Dr. Ewa Janina Grabska
Dr. Baohua Wen
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

  • AI-driven innovation
  • design paradigm shifts
  • spatial intelligence
  • architecture
  • interior design
  • 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 (4 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

33 pages, 9178 KB  
Article
Automated Image-to-BIM Using Neural Radiance Fields and Vision-Language Semantic Modeling
by Mohammad H. Mehraban, Shayan Mirzabeigi, Mudan Wang, Rui Liu and Samad M. E. Sepasgozar
Buildings 2025, 15(24), 4549; https://doi.org/10.3390/buildings15244549 - 16 Dec 2025
Viewed by 308
Abstract
This study introduces a novel, automated image-to-BIM (Building Information Modeling) workflow designed to generate semantically rich and geometrically useful BIM models directly from RGB images. Conventional scan-to-BIM often relies on specialized, costly, and time-intensive equipment, specifically if LiDAR is used to generate point [...] Read more.
This study introduces a novel, automated image-to-BIM (Building Information Modeling) workflow designed to generate semantically rich and geometrically useful BIM models directly from RGB images. Conventional scan-to-BIM often relies on specialized, costly, and time-intensive equipment, specifically if LiDAR is used to generate point clouds (PCs). Typical workflows are followed by a separate post-processing step for semantic segmentation recently performed by deep learning models on the generated PCs. Instead, the proposed method integrates vision language object detection (YOLOv8x-World v2) and vision based segmentation (SAM 2.1) with Neural Radiance Fields (NeRF) 3D reconstruction to generate segmented, color-labeled PCs directly from images. The key novelty lies in bypassing post-processing on PCs by embedding semantic information at the pixel level in images, preserving it through reconstruction, and encoding it into the resulting color labeled PC, which allows building elements to be directly identified and geometrically extracted based on color labels. Extracted geometry is serialized into a JSON format and imported into Revit to automate BIM creation for walls, windows, and doors. Experimental validation on BIM models generated from Unmanned Aerial Vehicle (UAV)-based exterior datasets and standard camera-based interior datasets demonstrated high accuracy in detecting windows and doors. Spatial evaluations yielded up to 0.994 precision and 0.992 Intersection over Union (IoU). NeRF and Gaussian Splatting models, Nerfacto, Instant-NGP, and Splatfacto, were assessed. Nerfacto produced the most structured PCs suitable for geometry extraction and Splatfacto achieved the highest image reconstruction quality. The proposed method removes dependency on terrestrial surveying tools and separate segmentation processes on PCs. It provides a low-cost and scalable solution for generating BIM models in aging or undocumented buildings and supports practical applications such as renovation, digital twin, and facility management. Full article
(This article belongs to the Special Issue Artificial Intelligence in Architecture and Interior Design)
►▼ Show Figures

Graphical abstract

21 pages, 10117 KB  
Article
Screen Façade Pattern Design Driven by Generative Adversarial Networks and Machine Learning Classification for the Evaluation of a Daylight Environment
by Hyunjae Nam and Dong Yoon Park
Buildings 2025, 15(22), 4056; https://doi.org/10.3390/buildings15224056 - 11 Nov 2025
Viewed by 748
Abstract
This research seeks to identify optimised screen façade patterns and ratios for the effective management of daylight ingress and glare effects. It employs generative adversarial networks (GANs) to generate pattern variations and further evaluates the resultant variations through daylight simulations for application in [...] Read more.
This research seeks to identify optimised screen façade patterns and ratios for the effective management of daylight ingress and glare effects. It employs generative adversarial networks (GANs) to generate pattern variations and further evaluates the resultant variations through daylight simulations for application in screen façades. The generated pattern data were classified by hierarchical clustering to distinguish distinct feature groups, and they were subsequently utilised as façade configurations. The pattern data were assessed through daylight performance metrics: spatial daylight autonomy (sDA), annual sunlight exposure (ASE), and daylight glare probability (DGP). The results of the annual-based simulations indicate that façade patterns with frame ratios in the range of 50–65% are useful in reducing the areas exposed to intensive glare on the façade side while maintaining the minimum required lighting conditions. The overall influence of screen façades on interior daylighting in a large space (e.g., 10 m × 10 m) was found to be limited. Their performance is notable in reducing glare discomfort areas within approximately 2.5 m of south-facing façades. This study supports an application strategy in which screen façades are used to manage the extent of areas exposed to daylight ingress within an interior space. Full article
(This article belongs to the Special Issue Artificial Intelligence in Architecture and Interior Design)
►▼ Show Figures

Figure 1

42 pages, 8656 KB  
Article
Artificial Intelligence-Based Architectural Design (AIAD): An Influence Mechanism Analysis for the New Technology Using the Hybrid Multi-Criteria Decision-Making Framework
by Xinliang Wang, Yafei Zhao, Wenlong Zhang, Yang Li, Xuepeng Shi, Rong Xia, Yanjun Su, Xiaoju Li and Xiang Xu
Buildings 2025, 15(21), 3898; https://doi.org/10.3390/buildings15213898 - 28 Oct 2025
Viewed by 1324
Abstract
Artificial Intelligence (AI) has emerged as a transformative force in the field of architectural design. This study aims to systematically analyze the influence mechanisms of Artificial Intelligence-based Architectural Design (AIAD) by constructing a comprehensive hybrid model that integrates the Analytic Hierarchy Process (AHP), [...] Read more.
Artificial Intelligence (AI) has emerged as a transformative force in the field of architectural design. This study aims to systematically analyze the influence mechanisms of Artificial Intelligence-based Architectural Design (AIAD) by constructing a comprehensive hybrid model that integrates the Analytic Hierarchy Process (AHP), Decision-Making Trial and Evaluation Laboratory (DEMATEL), Interpretive Structural Modeling (ISM), and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC). Based on the previous quantitative literature review, 6 primary categories and 18 secondary influencing factors were identified. Data were collected from a panel of fifteen experts representing the architecture industry, academia, and computer science. Through weighting analysis, causal mapping, hierarchical structuring, and driving–dependence classification, the study clarifies the complex interrelationships among influencing factors and reveals the underlying drivers that accelerate or constrain AI adoption in architectural design. By quantifying the hierarchical and causal influence of factors, this research provides theoretical findings and practical insights for design firms undergoing digital transformation. The results extend previous meta-analytical studies, offering a decision-support system that bridges academic research and real-world applications, thereby guiding stakeholders toward informed adoption of artificial intelligence for future cultural tourism development and regional spatial innovation. Full article
(This article belongs to the Special Issue Artificial Intelligence in Architecture and Interior Design)
►▼ Show Figures

Figure 1

28 pages, 20784 KB  
Article
Systematic Parameter Optimization for LoRA-Based Architectural Massing Generation Using Diffusion Models
by Soon Min Hong and Seungyeon Choo
Buildings 2025, 15(19), 3477; https://doi.org/10.3390/buildings15193477 - 26 Sep 2025
Viewed by 1239
Abstract
This study addresses the systematic optimization of Low-Rank Adaptation (LoRA) parameters for architectural knowledge integration in diffusion models, where existing AI research has provided limited guidance for establishing plausible parameter ranges in architectural massing applications. While diffusion models show increasing utilization in architectural [...] Read more.
This study addresses the systematic optimization of Low-Rank Adaptation (LoRA) parameters for architectural knowledge integration in diffusion models, where existing AI research has provided limited guidance for establishing plausible parameter ranges in architectural massing applications. While diffusion models show increasing utilization in architectural design, general models lack domain-specific architectural knowledge, and previous studies have offered insufficient hyperparameter optimization frameworks for architectural massing studies—fundamental components for expressing architectural knowledge. This research establishes a comprehensive LoRA training framework specifically for architectural mass generation, systematically evaluating caption detail levels, optimizers, learning rates, schedulers, batch sizes, and training steps. Through analysis of 220 architectural mass images representing spatial transformation operations, the study recommends the following parameter settings: detailed captions, Adafactor optimizer, learning rate 0.0003, constant scheduler, and batch size 4, achieving significant improvements in prompt-to-output fidelity compared to baseline approaches. The contribution of this study is not in introducing a new algorithm, but in providing a systematic application of LoRA in the architectural domain, serving as a bridging milestone for both emerging architectural-AI researchers and advanced scholars. The findings provide practical guidelines for integrating AI technologies into architectural design workflows, while demonstrating how systematic parameter optimization can enhance the learning of architectural knowledge and support architects in early-stage massing and design decision-making. Full article
(This article belongs to the Special Issue Artificial Intelligence in Architecture and Interior Design)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop