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Artificial Intelligence in Architecture and Interior Design

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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: closed (31 March 2026) | Viewed by 12017

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Prof. Dr. Ewa Janina Grabska

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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

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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 Issues, Collections and Topics in MDPI journals

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:

Generative and AI-Driven Innovation;
Performance Analysis and Intelligent Decision-Making;
Evolutionary Design and Aesthetic Evaluation;
Human–AI Collaborative Design Paradigm Shifts;
Deep Learning-Driven Research Paradigms;
User Experience and Spatial Intelligence;
Intelligent Construction and Facility Management;
AI Interventions in Cultural Heritage;
Cross-Disciplinary Integration;
Future Directions in the AI Era;
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

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Published Papers (10 papers)

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Research

20 pages, 3195 KB

Open AccessArticle

Leveraging Generative AI for High-Fidelity 360° Spatial Images: Methodological Validation for Use as Experimental Stimuli

by Yoojin Han and Joowon Jeong

Buildings 2026, 16(9), 1679; https://doi.org/10.3390/buildings16091679 - 24 Apr 2026

Viewed by 118

Abstract

Despite its efficiency, the structural integrity and geometric accuracy of artificial intelligence (AI)-generated imagery used in environmental psychology experiments have not been sufficiently validated. This study investigated the methodological validity and substitutability of generative AI-generated 360° images as experimental stimuli for indoor environmental research. Using a three-stage framework, we generated base panoramas with controlled structural parameters, integrated greenery via AI-based inpainting, and conducted multifaceted validation through objective quality metrics and expert assessments. Quantitative results confirmed high technical integrity, indicating that structural distortions at panoramic stitching points were effectively minimized. Furthermore, the AI-generated stimuli maintained stable visual quality across varying greenery densities. Expert evaluations confirmed that the AI-driven approach significantly outperforms conventional 3D modeling, particularly in terms of presence and realism. By achieving high usability and spatial integrity scores, we established a novel standard for employing generative AI to create high-fidelity virtual environments for architectural and psychological research. Full article

(This article belongs to the Special Issue Artificial Intelligence in Architecture and Interior Design)

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22 pages, 2984 KB

Open AccessArticle

Human–AI Collaborative Design in Architectural Studios: Evaluating Paradigm Shifts Across the Six Stages of the Design Process

by Hend Alana, Mohamed Fikry and Asmaa Hasan

Buildings 2026, 16(7), 1445; https://doi.org/10.3390/buildings16071445 - 5 Apr 2026

Viewed by 773

Abstract

Artificial intelligence (AI) is increasingly transforming architectural education, shifting design studios toward human–AI collaborative workflows. This study investigates the impact of AI integration across the six stages of the architectural design process: pre-design, conceptual design, schematic design, design development, documentation, and presentation. A mixed-methods approach was adopted, combining survey data from 17 master’s degree students with reflective insights from eight faculty members involved in hybrid AI-supported studio environments. AI’s influence was evaluated using six indicators: efficiency, creativity enhancement, accuracy, interdisciplinary integration, adoptability, and environmental or architectural impact. The findings indicate that AI is most effective during early design stages, where it supports idea generation, visualization, and rapid iteration. Its impact becomes less pronounced in later technical phases, where human expertise and critical reasoning remain essential. Students perceived AI as a creative catalyst and productivity enhancer, while faculty emphasized its analytical and evaluative potential in supporting informed decision-making. Overall, AI functions most effectively as a complementary partner rather than a replacement for human agency. The study proposes a structured framework to guide ethical and pedagogically sound AI integration within architectural design studios. Full article

(This article belongs to the Special Issue Artificial Intelligence in Architecture and Interior Design)

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21 pages, 4869 KB

Open AccessArticle

Integrating Computer Vision and GIS for Large-Scale Morphological Mapping and Driving Force Analysis of Vernacular Courtyard Dwellings

by Lihua Liang, Xianda Li, Shutong Liu, Zhenhao Guo, Shuo Tang and Baohua Wen

Buildings 2026, 16(6), 1118; https://doi.org/10.3390/buildings16061118 - 11 Mar 2026

Cited by 3 | Viewed by 356

Abstract

This study develops and applies an integrated methodology that combines deep learning-based computer vision and spatial statistics to automate the large-scale identification and analysis of morphological features in vernacular courtyard dwellings. Focusing on Liangshuaixiu dwellings in Wu’an, southern Hebei, we trained an HRNetV2 semantic segmentation model on high-resolution satellite imagery to identify and extract contours for 134,280 courtyard spaces. Core morphological parameters (area, orientation) were calculated and analyzed using GIS spatial statistics and the geographic detector model. The results show that (1) the computer vision pipeline achieved efficient recognition with satisfactory accuracy (~10% mean error); (2) spatial autocorrelation and hotspot analysis revealed distinct regional patterns, including a west–east increase in average courtyard area; and (3) geographic detector analysis demonstrated that courtyard morphology is shaped by complex interactions between natural and socio-economic factors. While average area and orientation were primarily governed by climate (air pressure, wind, temperature) and topography (elevation), diversity and internal variation were strongly influenced by nonlinear interactions, particularly between natural factors (e.g., wind–aspect) and between natural and human factors (e.g., population–climate). This work provides a scalable, data-driven framework for the quantitative spatial analysis of vernacular architectural heritage, advancing the understanding of building morphology as an outcome of coupled human–environment systems. Full article

(This article belongs to the Special Issue Artificial Intelligence in Architecture and Interior Design)

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21 pages, 15260 KB

Open AccessArticle

Intelligent HBIM Framework for Group-Oriented Preventive Protection: A Case Study of the Suopo Ancient Watchtower Complex in Danba

by Li Zhang, Chen Tang, Yaofan Ye, Jinzi Yang and Feng Xu

Buildings 2026, 16(5), 995; https://doi.org/10.3390/buildings16050995 - 3 Mar 2026

Viewed by 311

Abstract

Heritage Building Information Modeling (HBIM) is accelerating the transition from reactive restoration to preventive conservation in architectural heritage management. Nevertheless, research at the heritage-cluster scale remains limited, particularly in terms of multi-source data integration, dynamic value–risk coupling, and lifecycle-oriented decision support. This study proposes an intelligent HBIM-based framework designed to support integrated data processing, automated value–risk assessment, and preventive intervention planning for masonry heritage clusters. The framework is validated through its application to the Suopo Ancient Watchtower Complex in Danba, Sichuan, consisting of 84 polygonal stepped-in stone towers. By integrating 3D laser scanning, unmanned aerial vehicle (UAV) oblique photogrammetry, and historical archival data, a closed-loop workflow is established, spanning data acquisition, parametric semantic modeling, and intervention prioritization. A dedicated parametric component library and hierarchical semantic database tailored to irregular polygonal masonry significantly enhance modeling consistency, semantic coherence, and cross-building reusability. Leveraging the Revit Application Programming Interface (API) and Dynamo, the framework embeds a value–risk model (P = V × R), enabling automated component-level evaluation, real-time visualization of conservation priorities, and one-click generation of intervention lists. Results demonstrate improved modeling accuracy, efficiency, and decision reliability compared with conventional manual workflows. The framework offers a scalable and replicable pathway for sustainable conservation of masonry heritage clusters in high-seismic regions and provides a foundation for future integration with IoT-enabled digital twin systems. Full article

(This article belongs to the Special Issue Artificial Intelligence in Architecture and Interior Design)

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19 pages, 1274 KB

Open AccessArticle

Post-Pandemic Trends in Residential Space Design: An Analysis Using Deep Learning and Expert Evaluation

by Haewon Lim and Hye-Jin Yoon

Buildings 2026, 16(3), 589; https://doi.org/10.3390/buildings16030589 - 31 Jan 2026

Viewed by 716

Abstract

The COVID-19 pandemic has fundamentally transformed residential spaces, yet traditional survey-based approaches face limitations in objectively capturing these changes. This study investigates residential design trends in the Post-pandemic era, defined as the period in which pandemic-induced lifestyle changes have become institutionalized in everyday living environments. Residential interior images were collected from Pinterest and Instagram and analyzed using an image-based deep learning approach combined with expert evaluation. A pretrained convolutional neural network (ResNet50) was employed as a visual feature extractor to quantify three spatial attributes—openness and comfort, flexibility and diversity, and nature-friendliness—across four residential space types: balconies, living rooms, entrances, and bedrooms. The model-generated proportional scores were validated by experts and compared between pre-pandemic and post-pandemic periods. The results reveal dual transformation patterns of functional specialization and increased multifunctionality. Balconies evolved into well-being-oriented spaces with enhanced nature-related features, while living rooms emerged as multifunctional hubs with a substantial increase in spatial flexibility. In contrast, entrances exhibited reduced openness, functioning as hygienic buffer zones. These findings indicate a reconfiguration of spatial hierarchy in post-pandemic housing, where auxiliary spaces gain prominence and traditional primary spaces adopt flexible roles. This study demonstrates the value of image-based deep learning for objectively identifying residential design trends and provides practical implications for resilient housing design in the post-pandemic era. Full article

(This article belongs to the Special Issue Artificial Intelligence in Architecture and Interior Design)

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26 pages, 4166 KB

Open AccessArticle

FP-MAE: A Self-Supervised Model for Floorplan Generation with Incomplete Inputs

by Jing Zhong, Ran Luo, Peilin Li, Tianrui Li, Pengyu Zeng, Zhifeng Lei, Tianjing Feng and Jun Yin

Buildings 2026, 16(3), 558; https://doi.org/10.3390/buildings16030558 - 29 Jan 2026

Viewed by 789

Abstract

Floor plans are a central representational component of architectural design, operating in close relation to sections, elevations, and three-dimensional reasoning to support the production and understanding of architectural space. In this context, we address the bounded computational task of completing incomplete floor plan representations as a form of early-stage design assistance, rather than treating the floor plan as an isolated architectural object. Within this workflow, being able to automatically complete a floor plan from an unfinished draft is highly valuable because it allows architects to generate preliminary schemes more quickly, streamline early discussions, and reduce the repetitive workload involved in revisions. To meet this need, we present FP-MAE, a self-supervised learning framework designed for floor plan completion. This study proposes three core contributions: (1) We developed FloorplanNet, a dedicated dataset that includes 8000 floorplans consisting of both schematic line drawings and color-coded plans, providing diverse yet consistent examples of residential layouts. (2) On top of this dataset, FP-MAE applies the Masked Autoencoder (MAE) strategy. By deliberately masking sections of a plan and using a lightweight Vision Transformer (ViT) to reconstruct the missing regions, the model learns to capture the global structural patterns of floor plans from limited local information. (3) We evaluated FP-MAE across multiple masking scenarios and compared its performance with state-of-the-art baselines. Beyond controlled experiments, we also tested the model on real sketches produced during the early stages of design projects, which demonstrated its robustness under practical conditions. The results show that FP-MAE can produce complete plans that are both accurate and functionally coherent, even when starting from highly incomplete inputs. FP-MAE is a practical and scalable solution for automated floor plan generation. It can be integrated into design software as a supportive tool to speed up concept development and option exploration, and it also points toward broader opportunities for applying AI in architectural automation. While the current framework operates on two-dimensional plan representations, future extensions may integrate multi-view information such as sections or three-dimensional models to better reflect the relational nature of architectural design representations. Full article

(This article belongs to the Special Issue Artificial Intelligence in Architecture and Interior Design)

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33 pages, 9178 KB

Open AccessArticle

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

Cited by 2 | Viewed by 1306

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 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)

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21 pages, 10117 KB

Open AccessArticle

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 1185

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 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)

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42 pages, 8656 KB

Open AccessArticle

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

Cited by 1 | Viewed by 2748

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), 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)

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28 pages, 20784 KB

Open AccessArticle

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 2010

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 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)

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