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: 30 November 2025 | Viewed by 1173

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 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

  • 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 (1 paper)

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

Research

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 423
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 1-1
Back to TopTop