Buildings

Editorial

Jump to: Research, Review

6 pages, 173 KB

Open AccessEditorial

Practice and Application of Artificial Intelligence in the Built Environment

by Junming Chen, Yile Chen and Yanyan Liang

Buildings 2026, 16(5), 926; https://doi.org/10.3390/buildings16050926 - 26 Feb 2026

Viewed by 406

Abstract

With the rapid development of science and technology, the integration of artificial intelligence into the entire life cycle of the built environment is accelerating, demonstrating a broad range of potential applications in planning evaluation, design simulation, construction management, and operation and maintenance [...] [...] Read more.

With the rapid development of science and technology, the integration of artificial intelligence into the entire life cycle of the built environment is accelerating, demonstrating a broad range of potential applications in planning evaluation, design simulation, construction management, and operation and maintenance [...] Full article

(This article belongs to the Special Issue Practice and Application of Artificial Intelligence in Built Environment)

Research

Jump to: Editorial, Review

27 pages, 21916 KB

Open AccessArticle

Day–Night and Weekday–Weekend Heterogeneity in Built Environment Impacts on Public Space Vitality: A GWRF Analysis in Yuexiu District

by Yingqian Yang, Xiuhong Lin, Xin Li, Qiufan Chen and Xiaoli Sun

Buildings 2026, 16(3), 523; https://doi.org/10.3390/buildings16030523 - 27 Jan 2026

Viewed by 566

Abstract

Existing studies on urban public space vitality predominantly focus on single temporal scales or macro-urban levels, lacking a systematic understanding of day–night and weekday–weekend differentiation patterns at the meso-scale. This study examines 149 public spaces in the Yuexiu District, Guangzhou, employing Baidu heatmap [...] Read more.

Existing studies on urban public space vitality predominantly focus on single temporal scales or macro-urban levels, lacking a systematic understanding of day–night and weekday–weekend differentiation patterns at the meso-scale. This study examines 149 public spaces in the Yuexiu District, Guangzhou, employing Baidu heatmap data and the geographically weighted random forest (GWRF) model to analyze built environment impacts across four temporal scenarios. The SHAP interaction analysis is incorporated to quantitatively evaluate factor interdependencies and their temporal variations. Findings reveal significant spatiotemporal heterogeneity. Building density shows greater night-time importance while residential density exhibits enhanced daytime importance, particularly on weekend. Weekday–weekend comparison demonstrates contrasting spatial reorganization patterns, with weekday showing divergence and weekend showing convergence in factor importance distributions. The factor interaction analysis highlights stable synergistic relationships between density and diversity, alongside temporal transitions in density–residential density interactions from competitive to synergistic during night-time. Low-vitality public spaces are concentrated in peripheral areas with high building density but insufficient commercial facilities and functional mix. These findings deepen our understanding of the spatiotemporal mechanisms underlying public space vitality generation and the interaction effects among built environment factors, thereby providing an empirical foundation for the formulation of temporally adaptive planning strategies. Full article

(This article belongs to the Special Issue Practice and Application of Artificial Intelligence in Built Environment)

► Show Figures

Figure 1

14 pages, 2030 KB

Open AccessArticle

A Modular AI Workflow for Architectural Facade Style Transfer: A Deep-Style Synergy Approach Based on ComfyUI and Flux Models

by Chong Xu and Chongbao Qu

Buildings 2026, 16(3), 494; https://doi.org/10.3390/buildings16030494 - 25 Jan 2026

Viewed by 1232

Abstract

This study focuses on the transfer of architectural facade styles. Using the node-based visual deep learning platform ComfyUI, the system integrates the Flux Redux and Flux Depth models to establish a modular workflow. This workflow achieved style transfer of building facades guided by [...] Read more.

This study focuses on the transfer of architectural facade styles. Using the node-based visual deep learning platform ComfyUI, the system integrates the Flux Redux and Flux Depth models to establish a modular workflow. This workflow achieved style transfer of building facades guided by deep perception, encompassing key stages such as style feature extraction, depth information extraction, positive prompt input, and style image generation. The core innovation of this study lies in two aspects: Methodologically, a modular low-code visual workflow has been established. Through the coordinated operation of different modules, it ensures the visual stability of architectural forms during style conversion. In response to the novel challenges posed by generative AI in altering architectural forms, the evaluation framework innovatively introduces a “semantic inheritance degree” assessment system. This elevates the evaluation perspective beyond traditional “geometric similarity” to a new level of “semantic and imagery inheritance.” It should be clarified that the framework proposed by this research primarily provides innovative tools for architectural education, early design exploration, and visualization analysis. This workflow introduces an efficient “style-space” cognitive and generative tool for teaching architectural design. Students can use this tool to rapidly conduct comparative experiments to generate multiple stylistic facades, intuitively grasping the intrinsic relationships among different styles and architectural volumes/spatial structures. This approach encourages bold formal exploration and deepens understanding of architectural formal language. Full article

(This article belongs to the Special Issue Practice and Application of Artificial Intelligence in Built Environment)

► Show Figures

Figure 1

32 pages, 8317 KB

Open AccessArticle

Research Progress and Frontier Trends in Generative AI in Architectural Design

by Yingli Yang, Yanxi Li, Xuefei Bai, Wei Zhang and Siyu Chen

Buildings 2026, 16(2), 388; https://doi.org/10.3390/buildings16020388 - 17 Jan 2026

Viewed by 1274

Abstract

In recent years, with the rapid advancement of science and technology, generative artificial intelligence has increasingly entered the public eye. Primarily through intelligent algorithms that simulate human logic and integrate vast amounts of network data, it provides designers with solutions that transcend traditional [...] Read more.

In recent years, with the rapid advancement of science and technology, generative artificial intelligence has increasingly entered the public eye. Primarily through intelligent algorithms that simulate human logic and integrate vast amounts of network data, it provides designers with solutions that transcend traditional thinking, enhancing both design efficiency and quality. Compared to traditional design methods reliant on human experience, generative design possesses robust data processing capabilities and the ability to refine design proposals, significantly reducing preliminary design time. This study employs the CiteSpace visualization tool to systematically organize and conduct knowledge map analysis of research literature related to generative AI in architectural design within the Web of Science database from 2005 to 2025. Findings reveal the following: (1) International research exhibits a trend toward interdisciplinary convergence. In recent years, research in this field has grown rapidly across nations, with continuously increasing academic influence; (2) Research primarily focuses on technological applications within architectural design, aiming to drive innovation and development by providing superior, more efficient technical support; (3) Generative AI in architectural design has emerged as a prominent international research focus, reflecting a shift from isolated design to industry-wide integration; (4) Generative AI has become a core global architectural design topic, with future research advancing toward full-process intelligent collaboration. High-quality knowledge graphs tailored for the architecture industry should be constructed to overcome data silos. Concurrently, a multidimensional evaluation system for generative quality must be established to deepen the symbiotic design paradigm of human–machine collaboration. This significantly enhances efficiency while reducing the iterative nature of traditional methods. This study aims to provide empirical support for theoretical and practical advancements, offering crucial references for practitioners to identify business opportunities and policymakers to optimize relevant strategies. Full article

(This article belongs to the Special Issue Practice and Application of Artificial Intelligence in Built Environment)

► Show Figures

Figure 1

21 pages, 83627 KB

Open AccessArticle

Research on Urban Perception of Zhengzhou City Based on Interpretable Machine Learning

by Mengjing Zhang, Chen Pan, Xiaohua Huang, Lujia Zhang and Mengshun Lee

Buildings 2026, 16(2), 314; https://doi.org/10.3390/buildings16020314 - 11 Jan 2026

Viewed by 533

Abstract

Urban perception research has long focused on global metropolises, but has overlooked many cities with complex functions and spatial structures, resulting in insufficient universality of existing theories when facing diverse urban contexts. This study constructed an analytical framework that integrates street scene images [...] Read more.

Urban perception research has long focused on global metropolises, but has overlooked many cities with complex functions and spatial structures, resulting in insufficient universality of existing theories when facing diverse urban contexts. This study constructed an analytical framework that integrates street scene images and interpretable machine learning. Taking Zhengzhou City as the research object, it extracted street visual elements based on deep learning technology and systematically analyzed the formation mechanism of multi-dimensional urban perception by combining the LightGBM model and SHAP method. The main findings of the research are as follows: (1) The urban perception of Zhengzhou City shows a significant east–west difference with Zhongzhou Avenue as the boundary. Positive perceptions such as safety and vitality are concentrated in the central business district and historical districts, while negative perceptions are more common in the urban fringe areas with chaotic built environments and single functions. (2) The visibility of greenery, the openness of the sky and the continuity of the building interface are identified as key visual elements affecting perception, and their directions and intensifies of action show significant differences due to different perception dimensions. (3) The influence of visual elements on perception has a complex mechanism of action. For instance, the promoting effect of greenery visibility on beauty perception tends to level off after reaching a certain threshold. The research results of this study can provide quantitative basis and strategic reference for the improvement in urban space quality and humanized street design. Full article

(This article belongs to the Special Issue Practice and Application of Artificial Intelligence in Built Environment)

► Show Figures

Figure 1

48 pages, 23340 KB

Open AccessArticle

Exploring the Satisfaction of Low-Income Elderly People with Open Space Environment in Tapgol Park of Central Seoul: A Decision Tree Approach to Machine Learning

by Chunhong Wu, Yile Chen, Fenrong Zhang, Liang Zheng, Jingwei Liang, Shuai Yang and Yinqi Wang

Buildings 2026, 16(1), 172; https://doi.org/10.3390/buildings16010172 - 30 Dec 2025

Viewed by 619

Abstract

In urban design, public open spaces (POS) are essential for enhancing health and well-being across the lifetime. High-quality public open spaces facilitate the maintenance of optimal physical and mental health in older individuals by encouraging activities like physical exercise and social engagement. Preserving [...] Read more.

In urban design, public open spaces (POS) are essential for enhancing health and well-being across the lifetime. High-quality public open spaces facilitate the maintenance of optimal physical and mental health in older individuals by encouraging activities like physical exercise and social engagement. Preserving the physical and mental well-being of elderly individuals is a fundamental concern for aging policy. Nevertheless, urbanization presents considerable problems with the provision of public open spaces for activities aimed at the elderly. South Korea has more significant issues than other nations globally. This study, based on data from 477 valid questionnaires collected in and around Tapgol Park in Jung-gu, Seoul, employed a decision tree approach to identify key factors and paths that influence overall satisfaction. The goal was to identify decision paths that improve satisfaction while ensuring interpretability, thereby providing a scientific basis for urban space design and renovation. The results show that: (1) The decision tree of this study presents a hierarchical logic of quietness first, then accessibility and cleanliness, and finally price and vitality, which is consistent with the high frequency of use of Tapgol Park by the elderly and the diverse facilities in the surrounding area. (2) The key to improving the management and satisfaction of Tapgol Park in Seoul is the quietness of the site. (3) When the park is not quiet, users are most sensitive to bottom-line factors, such as commercial supply, evacuation safety, transportation accessibility, price perception, barrier-free, and anti-slips. (4) When the park is quiet, basic comfort factors such as smooth walking, all-day opening, sunlight, and no odor constitute the minimum condition set for entering the comfort zone. (5) Water experience, waterfront accessibility, proximity to cultural resources, and moderate business and community-oriented leisure facilities are key plus points. Methodologically, this study is among the first to apply a decision tree approach to low-income elderly using a small public open space in a historic city center, clarifying the nonlinear and hierarchical relationships among environmental factors within these low-income elderly groups. This provides empirical support and reference for the aging-friendly urban space in world heritage cities and other historical and cultural cities. Full article

(This article belongs to the Special Issue Practice and Application of Artificial Intelligence in Built Environment)

► Show Figures

Figure 1

29 pages, 4226 KB

Open AccessArticle

Interpretable Assessment of Streetscape Quality Using Street-View Imagery and Satellite-Derived Environmental Indicators: Evidence from Tianjin, China

by Yankui Yuan, Fengliang Tang, Shengbei Zhou, Yuqiao Zhang, Xiaojuan Li, Sen Wang, Lin Wang and Qi Wang

Buildings 2026, 16(1), 1; https://doi.org/10.3390/buildings16010001 - 19 Dec 2025

Viewed by 844

Abstract

Amid accelerating climate change, intensifying urban heat island effects, and rising public demand for livable, walkable streets, there is an urgent practical need for interpretable and actionable evidence on streetscape quality. Yet, research on streetscape quality has often relied on single data sources [...] Read more.

Amid accelerating climate change, intensifying urban heat island effects, and rising public demand for livable, walkable streets, there is an urgent practical need for interpretable and actionable evidence on streetscape quality. Yet, research on streetscape quality has often relied on single data sources and linear models, limiting insight into multidimensional perception; evidence from temperate monsoon cities remains scarce. Using Tianjin’s main urban area as a case study, we integrate street-view imagery with remote sensing imagery to characterize satellite-derived environmental indicators at the point scale and examine the following five perceptual outcomes: comfort, aesthetics, perceived greenness, summer heat perception, and willingness to linger. We develop a three-step interpretable assessment, as follows: Elastic Net logistic regression to establish directional and magnitude baselines; Generalized Additive Models with a logistic link to recover nonlinear patterns and threshold bands with Benjamini–Hochberg false discovery rate control and binned probability calibration; and Shapley additive explanations to provide parallel validation and global and local explanations. The results show that the Green View Index is consistently and positively associated with all five outcomes, whereas Spatial Balance is negative across the observed range. Sky View Factor and the Building Visibility Index display heterogeneous forms, including monotonic, U-shaped, and inverted-U patterns across outcomes; Normalized Difference Vegetation Index and Land Surface Temperature are likewise predominantly nonlinear with peak sensitivity in the midrange. In total, 54 of 55 smoothing terms remain significant after Benjamini–Hochberg false discovery rate correction. The summer heat perception outcome is highly imbalanced: 94.2% of samples are labeled positive. Overall calibration is good. On a standardized scale, we delineate optimal and risk intervals for key indicators and demonstrate the complementary explanatory value of street-view imagery and remote sensing imagery for people-centered perceptions. In Tianjin, a temperate monsoon megacity, the framework provides reproducible, actionable, design-relevant evidence to inform streetscape optimization and offers a template that can be adapted to other cities, subject to local calibration. Full article

(This article belongs to the Special Issue Practice and Application of Artificial Intelligence in Built Environment)

► Show Figures

Figure 1

29 pages, 26801 KB

Open AccessArticle

Renewal Design of Architectural Facade Features in the Shantou Xiaogongyuan Historic District Based on Deep Learning

by Wanying Yan, Tukun Wang and Cuina Zhang

Buildings 2025, 15(24), 4404; https://doi.org/10.3390/buildings15244404 - 5 Dec 2025

Cited by 2 | Viewed by 1242

Abstract

The Shantou Xiaogongyuan Historic District is a significant cultural symbol of the “Century-Old Commercial Port,” embodying the historical memory of the Chaoshan diaspora culture and modern trade. However, amid rapid urbanization, the area faces challenges such as the degradation of architectural façade styles, [...] Read more.

The Shantou Xiaogongyuan Historic District is a significant cultural symbol of the “Century-Old Commercial Port,” embodying the historical memory of the Chaoshan diaspora culture and modern trade. However, amid rapid urbanization, the area faces challenges such as the degradation of architectural façade styles, the erosion of historical features, and inefficiencies in traditional restoration methods, often resulting in renovated façades that exhibit “form resemblance but spirit divergence.” To address these issues, this study proposes a method integrating computer vision and generative design for historical building façade renewal. Focusing on the arcade buildings in the Xiaogongyuan District, an intelligent façade generation system was developed based on the pix2pix model, a type of Conditional Generative Adversarial Network (CGAN). A dataset of 200 annotated images was constructed from 200 field-collected façade samples, including Functional Semantic Labeling (FSL) diagrams and Building Elevation (BE) diagrams. After 800 training epochs, the model achieved stable convergence, with the generated schemes achieving compliance rates of 80% in style consistency, 60% in structural integrity, and 70% in authenticity. Additionally, a WeChat mini-program was developed, capable of generating façade drawings in an average of 3 s, significantly improving design efficiency. The generated elevations are highly compatible and can be directly imported into third-party modeling software for quick 3D visualization. In a practical application at the intersection of Shangping Road and Zhiping Road, the system generated design alternatives that balanced historical authenticity and modern functionality within hours, far surpassing the weeks required by traditional methods. This research establishes a reusable technical framework that quantifies traditional craftsmanship through artificial intelligence, offering a viable pathway for the cultural revitalization of the Xiaogongyuan District and a replicable systematic approach for AI-assisted renewal of historic urban areas. Full article

(This article belongs to the Special Issue Practice and Application of Artificial Intelligence in Built Environment)

► Show Figures

Figure 1

32 pages, 23468 KB

Open AccessArticle

AI-Based Pre-Renewal Design for Historic Building Facades: An AIGC–LoRA Framework with Collaborative Assessment

by Wen Duan, Jiacheng Rao, Jiarong Zhao, Nan Tao and Jiangpeng Chen

Buildings 2025, 15(23), 4212; https://doi.org/10.3390/buildings15234212 - 21 Nov 2025

Cited by 2 | Viewed by 1629

Abstract

Historic conservation areas face the challenge of balancing heritage preservation with modern adaptation, often resulting in irreversible risks. AIGC technology offers an effective solution to mitigate these renewal risks. Current methods struggle with three bottlenecks: a lack of high-quality datasets, difficulty integrating expert [...] Read more.

Historic conservation areas face the challenge of balancing heritage preservation with modern adaptation, often resulting in irreversible risks. AIGC technology offers an effective solution to mitigate these renewal risks. Current methods struggle with three bottlenecks: a lack of high-quality datasets, difficulty integrating expert and public preferences, and generating diverse proposals under complex preservation rules. This study proposes an AI-driven pre-renewal framework for building facades, which involves (1) virtual pre-renewal design using a large language model (LLM) to generate facade proposals based on “non-change,” “permissible,” and “prohibited” rules; (2) a multi-stakeholder evaluation system integrating expert and public judgments via the Bradley–Terry model; and (3) LoRA fine-tuning of Stable Diffusion XL to optimize facade generation. In the case study of the Shangxijie and Xiaxijie Historic Conservation Area of Jincheng Ancient Town, the framework was implemented in three stages. First, LLM-generated facades addressed data scarcity by adhering to preservation constraints. Second, an online platform integrated expert and public evaluations to refine the training dataset. Finally, LoRA fine-tuning improved the model’s contextual fidelity and stylistic coherence. Quantitative analysis showed that LoRA models outperformed the base model in authenticity and fidelity. Historic models achieved the highest fidelity (FID = 23.4, SSIM = 0.918, CLIPScore = 0.842), Style-Coordinated models performed stably (composite score = 0.82 ± 0.05, SSIM = 0.884), and Style-Incompatible models showed greater variability (mean = 0.78, SD = 0.09). The expert–public collaborative mechanism validated the iterative “generate–evaluate–refine” workflow as a sustainable approach for heritage facade renewal. Full article

(This article belongs to the Special Issue Practice and Application of Artificial Intelligence in Built Environment)

► Show Figures

Figure 1

37 pages, 5876 KB

Open AccessArticle

YOLOv11-Safe: An Explainable AI Framework for Data-Driven Building Safety Evaluation and Design Optimization in University Campuses

by Jing Hou, Yanfeng Hu, Bingchun Jiang, Zhoulin Chang, Mingjie Cao and Beili Wang

Buildings 2025, 15(22), 4125; https://doi.org/10.3390/buildings15224125 - 16 Nov 2025

Viewed by 1228

Abstract

Campus buildings often present hidden safety risks such as falls and wheelchair instabilities, which are closely related to architectural layout, material selection, and accessibility design. This study develops YOLOv11-Safe, an attention-enhanced and geometry-aware framework that functions as both a detection model and a [...] Read more.

Campus buildings often present hidden safety risks such as falls and wheelchair instabilities, which are closely related to architectural layout, material selection, and accessibility design. This study develops YOLOv11-Safe, an attention-enhanced and geometry-aware framework that functions as both a detection model and a spatial diagnostic tool for building safety assessment. The framework integrates a modified SimAM attention mechanism and a normalized Wasserstein distance (NWD) loss to improve detection accuracy in complex indoor environments, trained on a dataset of 1000 annotated images covering fall and wheelchair accident scenarios. To interpret spatial risk patterns, a Random Forest classifier combined with SHAP analysis was applied to quantify the contribution of five architectural–behavioral variables: body–ground contact ratio (BGCR), accessibility index (AI), event duration (D), body posture angle (PA), and spatial density (SD). Results show that BGCR and AI dominate the risk-level prediction, while D, PA, and SD refine boundary conditions. Scene-based verification further demonstrated that the framework accurately localized unsafe features—such as uneven drainage edges and discontinuous handrails—and translated them into actionable design feedback. The proposed approach thus links deep-learning detection with interpretable spatial analysis, offering a quantitative foundation for evidence-based architectural safety optimization in university campuses. Full article

(This article belongs to the Special Issue Practice and Application of Artificial Intelligence in Built Environment)

► Show Figures

Figure 1

22 pages, 16290 KB

Open AccessArticle

Identification and Configuration Optimization of Key Campus Landscape Features Using Augmentation-Based Machine Learning and Configuration Analysis

by Xiaowen Zhuang, Yi Cai, Zhenpeng Tang, Zheng Ding and Christopher Gan

Buildings 2025, 15(21), 3868; https://doi.org/10.3390/buildings15213868 - 26 Oct 2025

Viewed by 915

Abstract

A university campus is a composite built environment integrating research, daily life, culture, and ecological green space. Its landscape elements shape environmental perception and overall spatial quality. This study assesses spatial quality by identifying key features and optimizing their joint effects across three [...] Read more.

A university campus is a composite built environment integrating research, daily life, culture, and ecological green space. Its landscape elements shape environmental perception and overall spatial quality. This study assesses spatial quality by identifying key features and optimizing their joint effects across three perceptions: safety, comfort, and belonging. Using a Chinese campus, we captured street-view images, applied semantic segmentation to quantify elements (grass, trees, buildings, roads, sidewalks), and used explainable machine learning with data augmentation to identify the features most relevant to these perceptions. This study then employed fuzzy-set Qualitative Comparative Analysis (fsQCA) to reveal configuration pathways that enhance spatial quality. Results show that data augmentation mitigates class imbalance and improves prediction accuracy. Key features include sky, river, bridge, people, grass, and sidewalks, and path analysis indicates that greater sky openness and higher densities of people, roads, sidewalks, and grass, together with fewer buildings, cars, and bare earth, enhance safety, comfort, and belonging. This study delivers globally transferable design rules and a replicable, policy-ready workflow that enables evidence-based campus upgrades across diverse regions. Full article

(This article belongs to the Special Issue Practice and Application of Artificial Intelligence in Built Environment)

► Show Figures

Figure 1

20 pages, 12576 KB

Open AccessArticle

A ConvLSTM-Based Hybrid Approach Integrating DyT and CBAM(T) for Residential Heating Load Forecast

by Haibo Zhang, Xiaoxing Gao, Xuan Liu and Zhibin Liu

Buildings 2025, 15(20), 3781; https://doi.org/10.3390/buildings15203781 - 20 Oct 2025

Cited by 1 | Viewed by 635

Abstract

Accurate forecasting of residential heating loads is crucial for guiding heating system control strategies and improving energy efficiency. In recent years, research on heating load forecasting has primarily focused on continuous district heating systems, and it often struggles to cope with the abrupt [...] Read more.

Accurate forecasting of residential heating loads is crucial for guiding heating system control strategies and improving energy efficiency. In recent years, research on heating load forecasting has primarily focused on continuous district heating systems, and it often struggles to cope with the abrupt load fluctuations and irregular on/off schedules encountered in intermittent heating scenarios. To address these challenges, this study proposes a hybrid convolutional long short-term memory (ConvLSTM) model that replaces the conventional batch normalization layer with a Dynamic Tanh (DyT) activation function, enabling dynamic feature scaling and enhancing responsiveness to sudden load spikes. An improved channel–temporal attention mechanism, CBAM(T), is further incorporated to deeply capture the spatiotemporal relationships in multidimensional data and effectively handle the uncertainty of heating start–stop events. Using data from two heating seasons for households in a residential community in Dalian, China, we validate the performance of ConvLSTM-DyT-CBAM(T). The results show that the proposed model achieves the best predictive accuracy and strong generalization, confirming its effectiveness for intermittent heating load forecasting and highlighting its significance for guiding demand-responsive heating control strategies and for energy saving and emissions reduction. Full article

(This article belongs to the Special Issue Practice and Application of Artificial Intelligence in Built Environment)

► Show Figures

Figure 1

19 pages, 3807 KB

Open AccessArticle

Graph-RWGAN: A Method for Generating House Layouts Based on Multi-Relation Graph Attention Mechanism

by Ziqi Ye, Sirui Liu, Zhen Tian, Yile Chen, Liang Zheng and Junming Chen

Buildings 2025, 15(19), 3623; https://doi.org/10.3390/buildings15193623 - 9 Oct 2025

Cited by 4 | Viewed by 1867

Abstract

We address issues in existing house layout generation methods, including chaotic room layouts, limited iterative refinement, and restricted style diversity. We propose Graph-RWGAN, a generative adversarial network based on a multi-relational graph attention mechanism, to automatically generate reasonable and globally consistent house layouts [...] Read more.

We address issues in existing house layout generation methods, including chaotic room layouts, limited iterative refinement, and restricted style diversity. We propose Graph-RWGAN, a generative adversarial network based on a multi-relational graph attention mechanism, to automatically generate reasonable and globally consistent house layouts under weak constraints. In our framework, rooms are represented as graph nodes with semantic attributes. Their spatial relationships are modeled as edges. Optional room-level objects can be added by augmenting node attributes. This allows for object-aware layout generation when needed. The multi-relational graph attention mechanism captures complex inter-room relationships. Iterative generation enables stepwise layout optimization. Fusion of node features with building boundaries ensures spatial accuracy and structural coherence. A conditional graph discriminator with Wasserstein loss constrains global consistency. Experiments on the RPLAN dataset show strong performance. FID is 92.73, SSIM is 0.828, and layout accuracy is 85.96%. Room topology accuracy reaches 95%, layout quality 90%, and structural coherence 95%, outperforming House-GAN, LayoutGAN, and MR-GAT. Ablation studies confirm the effectiveness of each key component. Graph-RWGAN shows strong adaptability, flexible generation under weak constraints, and multi-style layouts. It provides an efficient and controllable scheme for intelligent building design and automated planning. Full article

(This article belongs to the Special Issue Practice and Application of Artificial Intelligence in Built Environment)

► Show Figures

Figure 1

30 pages, 21831 KB

Open AccessArticle

Optimizing University Campus Functional Zones Using Landscape Feature Recognition and Enhanced Decision Tree Algorithms: A Study on Spatial Response Differences Between Students and Visitors

by Xiaowen Zhuang, Yi Cai, Zhenpeng Tang, Zheng Ding and Christopher Gan

Buildings 2025, 15(19), 3622; https://doi.org/10.3390/buildings15193622 - 9 Oct 2025

Cited by 1 | Viewed by 1723

Abstract

As universities become increasingly open, campuses are no longer only places for study and daily life for students and faculty, but also essential spaces for public visits and cultural identity. Traditional perception evaluation methods that rely on manual surveys are limited by sample [...] Read more.

As universities become increasingly open, campuses are no longer only places for study and daily life for students and faculty, but also essential spaces for public visits and cultural identity. Traditional perception evaluation methods that rely on manual surveys are limited by sample size and subjective bias, making it challenging to reveal differences in experiences between groups (students/visitors) and the complex relationships between spatial elements and perceptions. This study uses a comprehensive open university in China as a case study to address this. It proposes a research framework that combines street-view image semantic segmentation, perception survey scores, and interpretable machine learning with sample augmentation. First, full-sample modeling is used to identify key image semantic features influencing perception indicators (nature, culture, aesthetics), and then to compare how students and visitors differ in their perceptions and preferences across campus spaces. To overcome the imbalance in survey data caused by group–space interactions, the study applies the CTGAN method, which expands minority samples through conditional generation while preserving distribution authenticity, thereby improving the robustness and interpretability of the model. Based on this, attribution analysis with an interpretable decision tree algorithm further quantifies semantic features’ contribution, direction, and thresholds to perceptions, uncovering heterogeneity in perception mechanisms across groups. The results provide methodological support for perception evaluation of campus functional zones and offer data-driven, human-centered references for campus planning and design optimization. Full article

(This article belongs to the Special Issue Practice and Application of Artificial Intelligence in Built Environment)

► Show Figures

Figure 1

20 pages, 3219 KB

Open AccessArticle

An Interpretable Machine Learning Approach to Studying Environmental Safety Perception Among Elderly Residents in Pocket Parks

by Shengzhen Wu, Sichao Wu, Jingru Chen and Chen Pan

Buildings 2025, 15(18), 3411; https://doi.org/10.3390/buildings15183411 - 20 Sep 2025

Cited by 2 | Viewed by 1156

Abstract

This research explores the environmental safety challenges faced by pocket parks in the context of urban aging within Chinese cities. It systematically analyzes visual elements that influence the elderly’s perception of environmental safety by applying interpretable machine learning techniques. By integrating panoramic image [...] Read more.

This research explores the environmental safety challenges faced by pocket parks in the context of urban aging within Chinese cities. It systematically analyzes visual elements that influence the elderly’s perception of environmental safety by applying interpretable machine learning techniques. By integrating panoramic image semantic segmentation and explainable AI models (e.g., SHAP and PDP), the study transforms subjective environmental perception into measurable indicators and constructs an environmental safety perception model using the LightGBM algorithm. Results indicate that sufficient pedestrian areas and moderate crowd activities significantly enhance safety perception among the elderly. Conversely, the presence of cars emerges as the most substantial adverse factor. Natural elements, such as vegetation and grass, exhibit nonlinear effects on safety perception, with an optimal threshold range identified. The research further elucidates the intricate synergies and constraints among visual elements, underscoring that the highest perceived safety arises from the synergistic combination of positive factors. This study deepens the understanding of environmental perception among the elderly and offers a data-driven framework and practical guidelines for urban planners and designers. It holds significant theoretical and practical implications for advancing the refined and human-centered renewal of urban public spaces. Full article

(This article belongs to the Special Issue Practice and Application of Artificial Intelligence in Built Environment)

► Show Figures

Figure 1

Review

Jump to: Editorial, Research

32 pages, 4310 KB

Open AccessReview

Artificial Intelligence Empowering the Transformation of Building Maintenance: Current State of Research and Knowledge

by Yaqi Zheng, Boyuan Sun, Yiming Guan and Yufeng Yang

Buildings 2025, 15(22), 4118; https://doi.org/10.3390/buildings15224118 - 15 Nov 2025

Viewed by 2533

Abstract

With the acceleration of urbanization and the continuous expansion of building stock, building maintenance plays a critical role in ensuring structural safety, extending service life, and promoting sustainable development. In recent years, the application of artificial intelligence (AI) in building maintenance has expanded [...] Read more.

With the acceleration of urbanization and the continuous expansion of building stock, building maintenance plays a critical role in ensuring structural safety, extending service life, and promoting sustainable development. In recent years, the application of artificial intelligence (AI) in building maintenance has expanded significantly, markedly improving detection accuracy and decision-making efficiency through predictive maintenance, automated defect recognition, and multi-source data integration. Although existing studies have made progress in predictive maintenance, defect identification, and data fusion, systematic quantitative analyses of the overall knowledge structure, research hotspots, and technological evolution in this field remain limited. To address this gap, this study retrieved 423 relevant publications from the Web of Science Core Collection covering the period 2000–2025 and conducted a systematic bibliometric and scientometric analysis using tools such as bibliometrix and VOSviewer. The results indicate that the field has entered a phase of rapid growth since 2017, forming four major thematic clusters: (1) intelligent construction and digital twin integration; (2) predictive maintenance and health management; (3) algorithmic innovation and performance evaluation; and (4) deep learning-driven structural inspection and automated operation and maintenance. Research hotspots are evolving from passive monitoring to proactive prediction, and further toward system-level intelligent decision-making and multi-technology integration. Emerging directions include digital twins, energy efficiency management, green buildings, cultural heritage preservation, and climate-adaptive architecture. This study constructs, for the first time, a systematic knowledge framework for AI-enabled building maintenance, revealing the research frontiers and future trends, thereby providing both data-driven support and theoretical reference for interdisciplinary collaboration and the practical implementation of intelligent maintenance. Full article

(This article belongs to the Special Issue Practice and Application of Artificial Intelligence in Built Environment)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Practice and Application of Artificial Intelligence in Built Environment

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (16 papers)

Editorial

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI