Buildings

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Open AccessArticle

Study on the Association Between Generative Artificial Intelligence and the Reshaping of Learning Among Undergraduate Architecture Students—A Case Study of Eight Universities in Wuhan, China

by Ran Peng, Xu Zhou, Ding Duan and Haixu Guo

Buildings 2026, 16(14), 2800; https://doi.org/10.3390/buildings16142800 - 14 Jul 2026

Abstract

In an era characterized by the deep integration of digitalization and intelligent technologies, generative artificial intelligence (GAI) is reshaping the ecology of higher education in unprecedented ways. Owing to its inherent complexity, practice-oriented nature, and interdisciplinary characteristics, undergraduate architectural education can no longer [...] Read more.

In an era characterized by the deep integration of digitalization and intelligent technologies, generative artificial intelligence (GAI) is reshaping the ecology of higher education in unprecedented ways. Owing to its inherent complexity, practice-oriented nature, and interdisciplinary characteristics, undergraduate architectural education can no longer be fully supported by traditional pedagogical models in response to emerging demands such as sustainable design, digital twins, and intelligent construction. Based on cross-sectional survey data from 1121 architecture undergraduates across eight universities in Wuhan, Hubei Province, this study proposes the Generative AI-enabled Learning Reshaping Association Model (GAI-LRM) and employs partial least squares structural equation modeling (PLS-SEM) to examine the statistical relationships between the variables. The generative AI tools investigated include text-generation tools such as Kimi AI, Doubao and Seedance, as well as image and design generation tools like Midjourney, Stable Diffusion, Forma AI, and ArkoAI. The results indicate that system-generated content quality, system quality, and task–technology fit are all significantly and positively associated with learning reshaping. Learning relationship reshaping and cognitive flexibility demonstrate positive indirect associations within the relevant pathways, whereas technology dependence shows a negative indirect association. Furthermore, there is a significant association between students’ foundational knowledge in the subject and certain variables. These findings reveal the multifaceted connections between the application characteristics of generative AI and changes in the learning processes of architecture undergraduates; they provide empirical insights for optimizing human–AI collaborative learning, critical design reviews, and tiered instruction in design studios at universities in Wuhan, while also establishing a theoretical framework for future cross-regional, longitudinal, and experimental studies. We situate these findings within a core framework of contemporary architectural scholarship, where mainstream architectural education continues to privilege image-driven representation and adherence to established stylistic paradigms, even as a parallel scientific research movement harnesses artificial intelligence to reshape fundamental design principles. Viewed from this perspective, our results reveal not only the current state of technology adoption but also the underlying mechanisms at play. Specifically, technological reliance diminishes cognitive flexibility, while deep disciplinary literacy constitutes the critical differentiator between uncritical replication and deliberate application. Consequently, we argue that architectural education should not merely incorporate GAI within existing visual paradigms but should instead steer it toward science-based, human-centric design principles. Full article

(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)

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Open AccessArticle

A Heritage DNA (HDNA) Framework for Quantitative Classification of Historic Facades

by Onur Kılıç and Harun Diler

Buildings 2026, 16(14), 2799; https://doi.org/10.3390/buildings16142799 - 14 Jul 2026

Abstract

Historic facade classification is generally based on qualitative assessments, limiting reproducibility and quantitative comparison. This study introduces the Heritage DNA (HDNA) framework, a descriptor-based approach integrating Morphological DNA (MDNA) and Material DNA (MaDNA) for the quantitative characterization of historic facades. Thirty-one facades from [...] Read more.

Historic facade classification is generally based on qualitative assessments, limiting reproducibility and quantitative comparison. This study introduces the Heritage DNA (HDNA) framework, a descriptor-based approach integrating Morphological DNA (MDNA) and Material DNA (MaDNA) for the quantitative characterization of historic facades. Thirty-one facades from the Tepebağ Historic District (Adana, Türkiye) were documented using close-range photogrammetry and represented through a nine-dimensional HDNA vector. Principal Component Analysis (PCA) and hierarchical clustering were employed to identify facade patterns and typological relationships. The first two principal components explained 50.38% of the total variance, while the four-cluster solution produced a silhouette coefficient of 0.256. Hierarchical clustering identified four facade clusters, which were subsequently interpreted as four facade families characterized by distinct morphological and material compositions. The results demonstrate that facade identity can be represented through measurable descriptor combinations, providing a reproducible framework for facade classification. The proposed HDNA approach offers a potentially transferable descriptor-based methodology for comparative heritage studies and data-driven heritage management, subject to further validation across different historic contexts. Full article

(This article belongs to the Special Issue Advanced Research on Cultural Heritage—2nd Edition)

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Open AccessArticle

Shrinkage-Mitigation Mechanism and Prediction Model of Slag/Fly Ash-Based Alkali-Activated Concrete Internally Cured with Superabsorbent Polymers

by Jin Yang, Zilong Tan, Nana Song and Biao Li

Buildings 2026, 16(14), 2798; https://doi.org/10.3390/buildings16142798 - 14 Jul 2026

Abstract

Alkali-activated concrete (AAC) offers the advantages of high mechanical strength and excellent corrosion resistance, making it a promising low-carbon material. However, its widespread application in practical engineering is severely limited by the serious issues of high shrinkage and susceptibility to cracking. To address [...] Read more.

Alkali-activated concrete (AAC) offers the advantages of high mechanical strength and excellent corrosion resistance, making it a promising low-carbon material. However, its widespread application in practical engineering is severely limited by the serious issues of high shrinkage and susceptibility to cracking. To address the challenges, this work proposes incorporating superabsorbent polymers (SAPs) into AAC to mitigate its shrinkage problems. A comprehensive investigation is conducted on fresh, mechanical and shrinkage properties of SAP-modified AAC. The underlying shrinkage-mitigating mechanism is revealed through various tests including hydration heat, X-ray diffraction, scanning electron microscope (SEM), and nuclear magnetic resonance (NMR) analysis. Results indicate that SAP prolongs concrete setting times. With SAP addition, the overall porosity and weak interfacial transition zones are increased, leading to decreases in the compressive strength by approximately 6.9–20.2% at 28 d. Both splitting tensile and flexural strengths show varying degrees of improvement. Adding 0.1–0.3% SAP reduces the 3 d autogenous shrinkage by about 33.08–41.14%, and 56 d drying shrinkage by 12.4–32.7%. SAPs act as internal water reservoirs, regulating humidity and reducing AAC’s sensitivity to internal relative humidity. Furthermore, SAPs optimize AAC pore structure distributions, as revealed by SEM and NMR analyses. Given the impact of SAP dosage (0–0.3%), autogenous and drying shrinkage prediction models are respectively established based on CEB-FIP 90 and GL 2000 models, showing superior agreement between predicted results and test data, with all the fitting coefficients over 0.9. Full article

(This article belongs to the Section Building Materials, and Repair & Renovation)

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Open AccessArticle

Neuroarchitecture and Learning in Children with ASD: Empirical Evidence from Therapeutic Centers in Lima

by Yadira M. Contreras-Montalvo and Emilio J. Medrano-Sanchez

Buildings 2026, 16(14), 2797; https://doi.org/10.3390/buildings16142797 - 14 Jul 2026

Abstract

Growing evidence positions the built environment as an active component of the developmental experience of children with Autism Spectrum Disorder (ASD). In this population, architectural elements such as lighting, acoustics, spatial configuration, and sensory stimuli are consistently associated with learning processes; however, empirical [...] Read more.

Growing evidence positions the built environment as an active component of the developmental experience of children with Autism Spectrum Disorder (ASD). In this population, architectural elements such as lighting, acoustics, spatial configuration, and sensory stimuli are consistently associated with learning processes; however, empirical evidence quantifying these relationships through proxy informant instruments remains scarce, particularly in Latin American urban contexts with infrastructure gaps. This study addresses that gap by examining the association between neuroarchitecture, understood as evidence-based sensory design, and learning in children with ASD attending therapeutic centers in the district of Comas, Metropolitan Lima. A quantitative, non-experimental, cross-sectional, and correlational design was adopted. The final sample comprised 98 proxy informants, family members with daily and sustained contact with children with ASD, recruited from four therapeutic centers in Comas (zones 3, 5, 6, and 8), following an instrument validation process that included a pilot test with 15 additional participants. Data were collected through a structured questionnaire of 24 Likert-scale items with five response options, organized around two variables and six dimensions. Content validity was established through expert judgment by five architecture specialists. Construct validity was assessed using Exploratory Factor Analysis (KMO = 0.744; Bartlett’s sphericity test:

χ^{2}

= 665.96, df = 276, p < 0.001). Instrument reliability was confirmed through Cronbach’s alpha coefficient (

α

= 0.844). Given the non-normal distribution of the data across all constructs (Shapiro–Wilk, p < 0.05), Spearman’s rho coefficient was used for inferential analysis. A positive and statistically significant association was identified between neuroarchitecture and learning (

ρ

= 0.599, p < 0.001). Dimensional analysis revealed a hierarchical pattern: the strongest association corresponded to cognitive processing (

ρ

= 0.492, p < 0.001), followed by social interaction and communication (

ρ

= 0.460, p < 0.001) and sensory regulation and adaptive behavior (

ρ

= 0.460, p < 0.001). A total of 99.0% of proxy informants perceived adequate neuroarchitectural conditions and associated adequate learning outcomes. The findings confirm that neuroarchitectural design is significantly associated with the learning of children with ASD, with cognitive processing emerging as the dimension most sensitive to spatial conditions. The evidence supports the formulation of preliminary design orientations that prioritize sensory stimuli management, spatial legibility, and programmatic differentiation of therapeutic environments, in alignment with Sustainable Development Goals (SDGs) 4 and 11. Full article

(This article belongs to the Special Issue Human-Centric Architectural Design: Neuroarchitecture as a New Tool to Shape Futureproof Inclusive Buildings)

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Open AccessArticle

A Multi-Scale Urban Physical Examination Framework for Spatial Diagnosis for Renewal Prioritization: A Case Study of Wu’an, China

by Runhao Zhang, Qin Li, Chong Liu, Yijun Liu and Lixin Jia

Buildings 2026, 16(14), 2796; https://doi.org/10.3390/buildings16142796 - 14 Jul 2026

Abstract

As China shifts from expansion-oriented urbanization to stock-based renewal, cities need diagnostic tools that can identify built-environment deficiencies and translate them into spatially targeted renewal priorities. Building on China’s official urban physical examination system, this study develops a land-renewal-oriented diagnostic workflow across four [...] Read more.

As China shifts from expansion-oriented urbanization to stock-based renewal, cities need diagnostic tools that can identify built-environment deficiencies and translate them into spatially targeted renewal priorities. Building on China’s official urban physical examination system, this study develops a land-renewal-oriented diagnostic workflow across four spatial scales: housing, community, block, and urban area. The framework integrates top-down objective assessment of land, facilities, infrastructure, environment, and safety conditions with bottom-up resident satisfaction evaluation. It then converts composite health scores, problem concentration, and safety relevance into three renewal priority categories: Priority Level III (critical renewal), Priority Level II (general renewal), and Priority Level I (long-term optimization). Using Wu’an, a resource-dependent county-level city in Hebei Province, as a case study, the results show that Priority Level III problems are concentrated mainly at the housing scale, especially corridor safety hazards, while widespread pipeline aging and age-friendly retrofitting needs are classified as Priority Level II medium-term renewal issues. Community and block scales mainly show facility, governance, and functional mismatches. The contribution of this study is not the four-tier scale structure itself, which is derived from existing policy, but the operational translation of urban physical examination results into spatial diagnosis for land renewal, renewal priority classification, and action-plan formulation. The workflow offers a transferable methodological reference for county-level stock renewal, while local indicators, thresholds, and implementation pathways require contextual adaptation. Full article

(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)

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Open AccessArticle

Evaluation and Analysis of the Spatial Performance and Spatial Legibility of Wind–Rain Bridges in Hunan Under the Influence of Urbanization

by Boyu Pang, Jun Yan, Jiacheng Liu, Sumin Li, Sheng Song, Jichi Guo and Xuchuan Zhou

Buildings 2026, 16(14), 2795; https://doi.org/10.3390/buildings16142795 - 14 Jul 2026

Abstract

Wind–Rain Bridge architecture is an important type of ancient bridge architecture. To reveal the factors contributing to the decline in the spatial performance of Wind-Rain Bridges in Hunan under the influence of urbanization and to assess their spatial legibility across different urbanization gradients, [...] Read more.

Wind–Rain Bridge architecture is an important type of ancient bridge architecture. To reveal the factors contributing to the decline in the spatial performance of Wind-Rain Bridges in Hunan under the influence of urbanization and to assess their spatial legibility across different urbanization gradients, this study addresses the limitations of traditional research, which has largely relied on static observations and lacked quantitative analysis. The findings provide a scientific basis for the refined conservation and adaptive revitalization of traditional architecture. This study examines 535 Wind–Rain Bridges in Hunan. Using Space Syntax, core indicators such as Integration and Choice were quantitatively calculated for both Wind–Rain Bridges and modern bridges. A controlled variable experiment was further employed to analyze the disturbance effects of modern road networks. In addition, an XGBoost multi-classification model was constructed to assess spatial legibility levels and identify key spatial-topological predictors. The results indicate that the decline in the spatial performance of Wind–Rain Bridges in Hunan is not linearly correlated with urbanization. Urban-type Wind–Rain Bridges are most significantly affected by the replacement effect of modern road networks, whereas Semi-Village-type Wind–Rain Bridges exhibit the strongest resilience. Among the variables included in this study, Integration and Mean Depth are the strongest predictors of spatial legibility. This study establishes an analytical framework of “type classification–factor identification–differentiated strategy formulation” and confirms that the primary driver of the spatial performance decline of Wind–Rain Bridges in Hunan is the fragmentation of traditional pedestrian road networks caused by modern transportation systems under urbanization, rather than the aging of the bridge structures themselves. Full article

(This article belongs to the Special Issue Architectural History, Modern Built Heritage, Conservation Repair and Renovation)

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Open AccessArticle

Research on Seasonal Heat Exchange in Underground Ventilation Tunnels Based on Field Measurement and CFD Simulation

by Tong Ren, De Wang, Mengzhuo Li, Long He and Lingbo Kong

Buildings 2026, 16(14), 2794; https://doi.org/10.3390/buildings16142794 - 14 Jul 2026

Abstract

Amidst global carbon neutrality goals, China’s building energy consumption gains prominence, with heating and cooling exceeding 50% of the total. Underground structures leverage inherent geological thermal inertia to significantly reduce ventilation energy demands. This study employs combined field measurements and numerical simulations to [...] Read more.

Amidst global carbon neutrality goals, China’s building energy consumption gains prominence, with heating and cooling exceeding 50% of the total. Underground structures leverage inherent geological thermal inertia to significantly reduce ventilation energy demands. This study employs combined field measurements and numerical simulations to investigate heat exchange mechanisms and performance in underground hydropower station air intake tunnels. Four representative tunnels (Sichuan, Fujian, Hebei, Yunnan) served as case studies, monitoring air temperature, humidity, velocity, and wall temperature. Field-monitored parameters informed a computational fluid dynamics (CFD) model, enabling quantitative analysis of rock thermal conductivity, inlet air velocity, and wall temperature effect on heat exchange efficiency. Research shows that: (1) significant seasonal adaptive characteristics exist, achieving peak cooling efficiency (69.04%, summer) and heating efficiency (78.86%, winter); (2) rock thermal conductivity is the primary efficiency determinant—quartzite tunnels exhibited 11.8% higher average efficiency than tuff tunnels; and (3) inlet air velocity negatively correlates with efficiency, exceeding 90% at 0.1 m/s but declining to 69% at 1.5 m/s. This work provides a theoretical basis for optimizing energy-efficient ventilation in underground engineering and validates the pivotal role of rock thermal inertia in reducing operational building energy consumption. Full article

(This article belongs to the Section Building Energy, Physics, Environment, and Systems)

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Open AccessArticle

Cooking Fume Particulate Matter as an Indoor Air Pollution Source: Comparative Measurement Methods and Correction Factors

by Pan Wang, Linghui Kong, Muhammad Azher Hassan, Fei Wang, Jinyu He and Xin Wang

Buildings 2026, 16(14), 2793; https://doi.org/10.3390/buildings16142793 - 14 Jul 2026

Abstract

Cooking fumes are an important source of indoor and outdoor air pollution. Containing potentially carcinogenic particles and toxic chemical components, they pose significant health threats, making precise detection essential for risk prevention and the formulation of emission standards. This study used the manual [...] Read more.

Cooking fumes are an important source of indoor and outdoor air pollution. Containing potentially carcinogenic particles and toxic chemical components, they pose significant health threats, making precise detection essential for risk prevention and the formulation of emission standards. This study used the manual gravimetric analysis as the benchmark reference method to systematically evaluate the performance of three alternative measurement techniques: (1) an optical particle counter (Promo 3000, Palas GmbH, Karlsruhe, Germany), (2) a photometer (DustTrak 8533, TSI, Shoreview, MN, USA), and (3) infrared spectrophotometry. A data correction model was constructed by simulating typical cooking conditions. Results indicate that the manual gravimetric analysis yielded the highest particulate concentrations. Infrared spectrophotometry measured only 75.6% of the benchmark value, mainly because of its selectivity toward oil-derived organic components and possible sampling losses. The optical particle counter, influenced by particle light-scattering properties and density differences, underestimated the mass concentration of particles larger than 0.3 μm, capturing only 46.9% of the benchmark value. Conversely, the photometer exhibited the smallest deviation, with readings closest to the gravimetric reference under the tested cooking fumes conditions. Theoretical derivation and data fitting determined correction factors of 1.32 for infrared spectrophotometry, 2.13 for the optical particle counter for particles larger than 0.3 μm, and 0.97 for the photometer. This correction system enables the standardization of data across different detection methods, allowing on-site monitoring data to be calibrated against the gravimetric reference method. Full article

(This article belongs to the Special Issue Building Ventilation and Air Quality: Integrated Approaches for Human Health, Energy Efficiency and Sustainable Environment)

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Open AccessSystematic Review

Low-Carbon Retrofitting for Existing Urban Residential Buildings in China: A Systematic Review of Policies, Measures and Performance

by Qunfeng Ji, Xinyue Shu, Pengju Zhang and Chuancheng Li

Buildings 2026, 16(14), 2792; https://doi.org/10.3390/buildings16142792 - 14 Jul 2026

Abstract

Existing urban residential buildings contribute substantially to operational energy use and carbon emissions in the building sector, making low-carbon retrofitting a key approach to improving the performance of the existing housing stock. This study conducts a bibliometric and systematic review of research on [...] Read more.

Existing urban residential buildings contribute substantially to operational energy use and carbon emissions in the building sector, making low-carbon retrofitting a key approach to improving the performance of the existing housing stock. This study conducts a bibliometric and systematic review of research on low-carbon retrofitting of existing urban residential buildings in China. Journal articles published between 2015 and 2025 were retrieved from Web of Science and Scopus, and 91 studies were retained after screening. Bibliometric analysis was used to examine annual publication trends, source distribution, keyword co-occurrence, thematic evolution, and organizational collaboration. The systematic review further synthesised evidence on retrofit policies, technical measures, and performance evaluation methods. The results indicate a clear increase in publications in recent years, with research attention shifting from basic energy-saving measures towards multi-objective optimization, carbon reduction, and thermal comfort improvement. The review suggests that China’s residential retrofit policies can be understood as a multi-level framework supporting retrofit implementation. Retrofit strategies have gradually shifted from individual measures towards integrated retrofit packages, while performance evaluation has expanded from energy-saving assessment to broader considerations of carbon emissions, occupant comfort, and economic feasibility. The review highlights the need for more consistent evaluation boundaries, stronger integration of lifecycle carbon accounting and occupant behaviour, and climate-responsive retrofit strategies. These findings provide a structured basis for comparing retrofit approaches, strengthening the connection between policy and technology, and supporting decision-making in large-scale residential retrofit programmes. Full article

(This article belongs to the Special Issue Low-Carbon Transformation of Existing Built Environments: From Retrofit to Regeneration)

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Open AccessArticle

NERF2BIM: AI-Driven Detailing-on-Demand Through Sustainable Point Cloud Surveys and Semantic 3D Understanding for Advanced Modeling of Existing Buildings

by Ivan Bratoev, Omar Faig Orujlu, Ziyang Xu, Ziya Erkoc, Matthias Nießner, Christoph Holst and Frank Petzold

Buildings 2026, 16(14), 2791; https://doi.org/10.3390/buildings16142791 - 14 Jul 2026

Abstract

The refurbishment and energy-efficient renovation of existing buildings, specifically those before 1945, with complex architectural building elements, require a level of building information that is often unavailable, incomplete or imprecise. As they represent a large percentage of current building stock (up to 25% [...] Read more.

The refurbishment and energy-efficient renovation of existing buildings, specifically those before 1945, with complex architectural building elements, require a level of building information that is often unavailable, incomplete or imprecise. As they represent a large percentage of current building stock (up to 25% of all buildings), it is crucial to address such an issue, as such buildings are ideal targets for renovation and energy retrofitting projects. This paper presents a conceptual pipeline, developed through the NERF2BIM research project, focusing on an Artificial Intelligence (AI)-supported holistic pipeline for the creation of as-is Building Information Modeling (BIM) models of existing buildings, expanding upon existing methodologies with the embedding of knowledge-driven semi-automatic detailing-on-demand task. The proposed pipeline integrates uncertainty-aware spatial capture, semantic interpretation and reconstruction, and knowledge-based reasoning within a BIM-oriented workflow. The paper provides an overview of current advancements in the respective aspects of the pipeline, highlighting current gaps. The proposed conceptual pipeline aims at addressing these issues through novel applications of AI and knowledge-driven solutions. Key contributions include: (1) a conceptual approach in addressing the imprecision of more sustainable data gathering approaches; (2) a context-aware BIM reconstruction process, providing multiple data output types; and (3) a formalization of architectural and construction knowledge and its utilization in a detailing-on-demand approach of the reconstructed BIM models. Through the integration of uncertainty-aware data gathering, context-aware reconstructions and domain expertise into existing reconstruction pipelines, the proposed pipeline bridges the data gap for existing buildings, enabling more efficient and knowledge-driven renovation processes. Full article

(This article belongs to the Special Issue Construction 5.0 in Early Architectural Design Phases)

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Open AccessArticle

Calculation of Stability Capacity for Elastically Restrained Sway Reinforced Concrete Slender Columns Based on Elastoplastic Stiffness

by Shuwei Lan, Peng Zhou, Difei Zhao, Wei Zhang, Jiansheng Zhang and Hongyu Chen

Buildings 2026, 16(14), 2790; https://doi.org/10.3390/buildings16142790 - 14 Jul 2026

Abstract

With the continuous advancement of urban renewal and the renovation and utilization of existing buildings, a large number of existing reinforced concrete slender columns face challenges in capacity evaluation. Quick and accurate calculation of their stability capacity, which represents the upper limit of [...] Read more.

With the continuous advancement of urban renewal and the renovation and utilization of existing buildings, a large number of existing reinforced concrete slender columns face challenges in capacity evaluation. Quick and accurate calculation of their stability capacity, which represents the upper limit of member capacity, holds significant importance. These columns often exhibit plastic characteristics such as concrete cracking and steel yielding. Moreover, the bracing restraint provided by adjacent columns typically falls between that of a sway frame and a non-sway frame, classifying them as elastically restrained sway frame columns. Current design codes lack appropriate effective length factor tables for such columns, while the stiffness degradation induced by material nonlinearity is difficult to quantify accurately. To address these issues, the frame column is isolated from the overall structure and modeled as a rigid compression member system with three springs. The influence of bracing stiffness on the column’s critical load is revealed, leading to a formula for the elastic critical load of elastically restrained sway frame columns. Based on tests of reinforced concrete columns under compression, the influence mechanisms of eccentricity ratio and longitudinal reinforcement ratio on flexural stiffness degradation are elucidated. The obtained elastoplastic stiffness is then integrated into the stability calculation framework for elastically restrained sway frame columns, resulting in a method for determining the elastoplastic stability capacity of reinforced concrete columns that accounts for both geometric and material nonlinearities. This method avoids solving complex transcendental equations and offers a straightforward calculation process, providing a simple and practical hand-calculation tool for evaluating the stability capacity of reinforced concrete columns in existing buildings. Full article

(This article belongs to the Section Building Materials, and Repair & Renovation)

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Open AccessArticle

Prioritising the Adaptive Reuse of Closed Schools in Depopulating Regions: Reconciling Urgency and Potential Through a Multi-Criteria Framework

by Jinju Jung, Inkwan Paik, Junhyuk Lim and Seunguk Na

Buildings 2026, 16(14), 2789; https://doi.org/10.3390/buildings16142789 - 14 Jul 2026

Abstract

The accelerating closure of schools in depopulating regions is leaving a growing surplus of public assets whose reuse must be prioritised, yet systematic and transferable tools for supporting such decisions in advance remain scarce. This study proposes an artificial-intelligence-augmented multi-criteria framework that prioritises [...] Read more.

The accelerating closure of schools in depopulating regions is leaving a growing surplus of public assets whose reuse must be prioritised, yet systematic and transferable tools for supporting such decisions in advance remain scarce. This study proposes an artificial-intelligence-augmented multi-criteria framework that prioritises the adaptive reuse of closed schools using only openly available demographic and spatial data. Four criteria—regional ageing, building floor area, and proximity to administrative and transport infrastructure—were evaluated for 121 closed schools in Chungbuk Province, South Korea, under two weighting schemes, the subjective analytic hierarchy process and the objective entropy method, with a large-language-model agent added as an explanatory layer to interpret context and recommend reuse types. The data-driven weights proved liable to a structural distortion, elevating the single largest building to first place in urgency on the strength of its size alone, a misjudgement the agent corrected through contextual reasoning over the same data. Examining the schools from the opposed standpoints of intervention urgency and reuse potential further revealed a near-perfect inversion between them (Spearman ρ = −0.998), indicating that the most urgent schools are systematically those least able to sustain a market-led conversion. The framework addresses this dilemma not through a single optimal ranking but through spatially differentiated, agent-generated recommendations and is formulated for transfer to other middle-income economies approaching the same demographic transition. Full article

(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)

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Open AccessArticle

Rheological Regulation and Printability Enhancement of 3D-Printed Recycled Concrete Incorporating Calcined Oyster Shell Powder

by Ze Chen, Yuncheng Wang, Chaolang Zheng, Xuelin Liu and Jinyang Jiang

Buildings 2026, 16(14), 2788; https://doi.org/10.3390/buildings16142788 - 14 Jul 2026

Abstract

The relationship between rheological properties and printability in 3D-printed recycled concrete incorporating calcined oyster shell powder (COSP) remains insufficiently understood. The unstable rheological behavior and insufficient buildability of 3D-printed recycled concrete limit its application in digital construction. In this study, the COSP, a [...] Read more.

The relationship between rheological properties and printability in 3D-printed recycled concrete incorporating calcined oyster shell powder (COSP) remains insufficiently understood. The unstable rheological behavior and insufficient buildability of 3D-printed recycled concrete limit its application in digital construction. In this study, the COSP, a marine solid waste-derived functional powder, was incorporated into 3D-printed recycled concrete to improve rheological behavior and printability. The effects of different COSP contents on rheological properties and structure build-up were assessed. In addition, MIP, XRD, and SEM analyses were used to clarify the underlying regulation mechanism. The results showed that COSP significantly improved the rheological properties of fresh paste. As the COSP content increased from 0% to 5%, the static yield stress, dynamic yield stress, and thixotropic recovery degree increased from 1703 Pa to 4561 Pa, from 92 Pa to 375 Pa, and from 56% to 87%, respectively. Meanwhile, the structural deformation rate decreased from 12.3% to 6.37%, corresponding to a reduction of approximately 48%. An appropriate COSP content also improved the mechanical properties, with the flexural strengths in the X and Y directions increasing to 5.17 MPa and 4.99 MPa, respectively, and the compressive strengths increasing to 26.04 MPa and 25.48 MPa, respectively. The microscopic performance results indicated that COSP refined the pore structure, promoted the formation of hydration products, and improved compactness. This study offers preliminary evidence for improving the printability of 3D-printed recycled concrete, while addressing the urgent environmental challenge of marine solid waste utilization and enhancing the economic feasibility and production efficiency of 3D-printed concrete. Full article

(This article belongs to the Special Issue Advanced Materials for Modern Methods of Construction: Innovations, Challenges, and Sustainable Building Applications)

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23 pages, 10041 KB

Open AccessArticle

A Component-Based Joint Model for Nonlinear Analysis of Bolted Extended End-Plate Connections

by Xiao Liu, Yilun Li, Haiwei Yao, Chaoyang Liu and Liangyin Huang

Buildings 2026, 16(14), 2787; https://doi.org/10.3390/buildings16142787 - 14 Jul 2026

Abstract

Semi-rigid connections play a critical role in steel structures; however, most existing component-based approaches do not explicitly account for stiffness degradation and post-yield residual stiffness, which may reduce the accuracy of moment–rotation predictions. To address this limitation, direct numerical simulations (DNSs) of representative [...] Read more.

Semi-rigid connections play a critical role in steel structures; however, most existing component-based approaches do not explicitly account for stiffness degradation and post-yield residual stiffness, which may reduce the accuracy of moment–rotation predictions. To address this limitation, direct numerical simulations (DNSs) of representative T-stub beam-to-column joints were conducted to investigate their nonlinear rotational behavior. Based on the observed joint response, a Joint Component Model (JCM) capable of representing sequential yielding, stiffness evolution, and residual rotational stiffness was developed. Constitutive relationships were derived, and a parameter identification procedure directly relating joint geometry and component mechanical properties to the model parameters was established. The proposed model was subsequently implemented in ANSYS and validated through analyses of T-stub joints and steel frames subjected to static and dynamic loading. The results showed good agreement between the JCM and DNS in terms of moment–rotation relationships, force–displacement responses, and dynamic time-history responses. Compared with DNS, the proposed model significantly reduced computational time while maintaining satisfactory prediction accuracy. The proposed JCM therefore provides an efficient and reliable component-based modeling framework for modeling semi-rigid steel connections and capturing stiffness evolution throughout the entire joint rotation process. Full article

(This article belongs to the Special Issue Nonlinear Behaviour of Steel and Composite Structures)

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24 pages, 4294 KB

Open AccessArticle

AI Agent Framework Research for Dynamic BIM Query via Retrieval-Augmented Code Generation for IFC

by Taewook Kang

Buildings 2026, 16(14), 2786; https://doi.org/10.3390/buildings16142786 - 13 Jul 2026

Abstract

Accessing and retrieving specific information from complex Building Information Modeling (BIM) data, particularly within the Industry Foundation Classes (IFC) schema, remains a challenge for Architecture, Engineering, and Construction (AEC) professionals. Existing methods, including manual parsing, database conversion, and early-stage AI applications, often suffer [...] Read more.

Accessing and retrieving specific information from complex Building Information Modeling (BIM) data, particularly within the Industry Foundation Classes (IFC) schema, remains a challenge for Architecture, Engineering, and Construction (AEC) professionals. Existing methods, including manual parsing, database conversion, and early-stage AI applications, often suffer from high overhead, inflexibility, or reliance on pre-processed, static data views. This study introduces a novel AI agentic framework that leverages a Large Language Model (LLM) to dynamically generate executable code for BIM data query. The core of this framework is a Retrieval-Augmented Generation (RAG) pipeline that, instead of retrieving factual data, retrieves relevant code exemplars demonstrating the use of IFC parser. The LLM-based agent analyzes a user’s natural language query, retrieves functionally relevant code snippets from a specialized vector database, and synthesizes these examples into a tailored, executable script. This “BIM code-retrieval” approach eliminates the need for IFC conversion using databases, allowing the agent to directly use the full power of the IFC parser to obtain the data it needs, and for the LLM to make appropriate inferences to the question. The proposed system demonstrates flexibility and query power compared to methods reliant on static data abstractions. It can handle complex queries that are intractable for previous RAG-based approaches. This AI agent code generation framework demonstrates the cost-effective, inference-based human–BIM interaction method in situations where IFC-to-database conversion is difficult. The prototype of study was developed to analyze the performance of the proposed BIM AI Agent framework and its strengths and weaknesses were examined. Full article

(This article belongs to the Special Issue Advancing Construction Management with BIM and AI Agent Technology)

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32 pages, 29701 KB

Open AccessArticle

Seismic Mechanism and Restoring Force Model of Precast Concrete Superposed Shear Walls with Concrete-Filled Steel Tubular End Columns

by Bian Wu, Min Zhang and Feng-Liang Zhang

Buildings 2026, 16(14), 2785; https://doi.org/10.3390/buildings16142785 - 13 Jul 2026

Abstract

Precast concrete (PC) structures are increasingly adopted in building construction for their sustainable construction advantages. However, theoretical models for seismic design of precast concrete walls with concrete-filled steel tubular (CFST) elements remain limited. The lack of such models hinders the performance-based seismic design [...] Read more.

Precast concrete (PC) structures are increasingly adopted in building construction for their sustainable construction advantages. However, theoretical models for seismic design of precast concrete walls with concrete-filled steel tubular (CFST) elements remain limited. The lack of such models hinders the performance-based seismic design and resilience assessment of these hybrid structures. This study investigates the seismic mechanism and develops a restoring force model for precast concrete superposed shear walls with CFST end columns (PCSSWEC). A refined three-dimensional finite element model was established using ABAQUS and validated against quasi-static cyclic test results of three full-scale specimens. The four-stage loading mechanism—elastic, wall cracking, elastoplastic yielding, and ultimate failure—was revealed, with the precast–postcast concrete interface identified as the primary weak link governing post-peak strength degradation. Comprehensive parametric studies examined the influence of shear span ratio (λ = 0.75–3.25), axial compression ratio (n_a = 0.1–0.6), steel tube width-to-thickness ratio (B/t = 20–80), and concrete strength (C30–C60) on seismic performance. Results indicate that intermediate walls (λ = 1.75–2.25) exhibit optimal ductility, and a steel tube with B/t = 40–60 provides a balanced combination of strength and deformation capacity. A tri-linear backbone curve model with explicit formulae for equivalent stiffness and load capacity was developed, along with modified Clough-based hysteretic rules incorporating stiffness degradation through a common yield-point approach. Validation against experimental and numerical results demonstrates reliable model performance for primary structural parameters: lateral load bearing capacity and ultimate drift ratio are predicted within ±10%, while yield load and ductility predictions show larger scatter due to inherent challenges in cyclic behavior characterization. The proposed restoring force model provides a practical tool for performance-based seismic design and resilience assessment of precast concrete buildings. Full article

(This article belongs to the Special Issue Advances in Steel-Concrete Composite Structure—2nd Edition)

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25 pages, 3233 KB

Open AccessArticle

Scaffolding Safety Assessment Framework Integrating Vision-Based Geometry Recognition and Structural Simulation

by Hao Peng, Lintao Zhang, Jing Dong, Yu Du and Han Wu

Buildings 2026, 16(14), 2784; https://doi.org/10.3390/buildings16142784 - 13 Jul 2026

Abstract

The assembly quality of scaffolding systems directly governs the safety of personnel on construction sites. According to construction safety statistics, scaffolding-related accidents account for approximately 30–40% of construction fatalities globally, with geometric assembly deviations being a contributing factor in over 60% of scaffold [...] Read more.

The assembly quality of scaffolding systems directly governs the safety of personnel on construction sites. According to construction safety statistics, scaffolding-related accidents account for approximately 30–40% of construction fatalities globally, with geometric assembly deviations being a contributing factor in over 60% of scaffold collapse incidents. Traditional scaffolding inspections rely heavily on manual measurements, which are inherently inefficient, hazardous, and difficult to scale comprehensively. This study presents an automated evaluation framework that integrates computer vision with structural mechanics simulations. First, an object detection model based on the SegFormer encoder architecture is developed to precisely identify scaffolding standards, ledgers, and couplers against complex site backgrounds. Its hierarchical Transformer encoder and global self-attention mechanism enable the model to capture long-range topological relationships, achieving a mean Average Precision (mAP@0.5) of 95.2% on a custom dataset with an inference speed of 45 FPS per 640 × 640 image patch. For complete high-resolution frame processing including tiling and geometric extraction, the end-to-end pipeline requires approximately 8–12 s per frame. Second, a simplified Hough transform with a restricted parameter domain is introduced. Integrated with a dual-track image processing workflow, this algorithm performs sub-pixel centerline fitting to automatically extract critical geometric parameters, including lift height and bay width, maintaining a relative measurement error within 3.5% compared to manual ground truth. Finally, a parameterized finite element model is established. An automated mapping middleware dynamically injects the extracted as-built parameters into the simulation environment. Comparative simulation analysis indicates that a 14.7% deviation in standard lift height, coupled with an initial tilt defect of 1/150, precipitates a 22.4% reduction in the predicted structural stability factor, illustrating the framework’s capability for assessing relative capacity degradation between design intent and as-built conditions. This framework establishes a robust, closed-loop pipeline spanning visual perception and structural safety assessment, indicating potential for automated construction site safety management. Full article

(This article belongs to the Special Issue Advances in Building Structure Analysis and Health Monitoring)

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26 pages, 9023 KB

Open AccessSystematic Review

Ecologically Active Soils for Regenerative Retrofitting of Existing Buildings: A Systematic Review

by Alejandro Jiménez Rios, Juliana Calabria-Holley, Francisco Javier Castilla Pascual and Anushka Gupta

Buildings 2026, 16(14), 2783; https://doi.org/10.3390/buildings16142783 - 13 Jul 2026

Abstract

Approximately 80% of the buildings that will exist in Global North countries by 2050 have already been built, yet most perform poorly in terms of energy efficiency and fail to deliver net-positive outcomes. Ecologically active soils, which are engineered to provide the moisture, [...] Read more.

Approximately 80% of the buildings that will exist in Global North countries by 2050 have already been built, yet most perform poorly in terms of energy efficiency and fail to deliver net-positive outcomes. Ecologically active soils, which are engineered to provide the moisture, porosity, and nutrient conditions necessary for plant growth, offer a promising yet underexplored pathway for the regenerative retrofitting of existing building envelopes. This paper presents the rationale, objectives, and results of the Regenerative Retrofitting Via Ecologically Active Soil Structures (Reeco-Soil) project, which investigates the state of the art of ecologically active soil-based building retrofitting through a Systematic Literature Review (SLR) conducted in accordance with PRISMA 2020 guidelines, drawing on searches of the Scopus and Web of Science databases. Results confirm that bio-based clay composites can achieve significant reductions in thermal conductivity, and that robotic and spray-based fabrication methods are capable of depositing earthen materials onto complex building geometries. However, peer-reviewed evidence directly addressing biological component integration remains critically scarce. As conclusion, the review reveals substantial opportunities for integrating earthen materials, biological components, and digital fabrication technologies into regenerative retrofitting strategies, while also highlighting research gaps that must be addressed before they can be implemented at scale. Full article

(This article belongs to the Special Issue Earth-Based Eco-Efficient Architecture and Construction)

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26 pages, 4652 KB

Open AccessArticle

Effects of Basal Reinforcement Methods and Spatial Parameters on Deformation Control of Asymmetric Shared-Wall Excavations in Soft Soil

by Nan Bai, Shenghan Hu, Yongzhi Song, Mingyu Kang, Hongtao Li, Jie Zhen, Yang Han, Jibin Sun, Xuesong Cheng and Gang Zheng

Buildings 2026, 16(14), 2782; https://doi.org/10.3390/buildings16142782 - 13 Jul 2026

Abstract

Deep excavation in soft soil readily induces lateral displacement of retaining structures and basal heave, posing serious threats to adjacent underground structures and the surrounding environment. Basal reinforcement is a key measure for improving excavation stability and controlling deformation; however, systematic comparative studies [...] Read more.

Deep excavation in soft soil readily induces lateral displacement of retaining structures and basal heave, posing serious threats to adjacent underground structures and the surrounding environment. Basal reinforcement is a key measure for improving excavation stability and controlling deformation; however, systematic comparative studies on multiple reinforcement methods and their spatial parameters in complex asymmetric shared-wall excavations remain scarce. Based on the Tianjin Binhai International Airport Integrated Transportation Hub project, a three-dimensional coupled numerical model of the “soil–Z2 excavation–existing M2 metro station” interaction was established using PLAXIS 3D. The effects of reinforcement depth, width, and area replacement ratio on diaphragm wall deformation, ground settlement, and base slab vertical deformation were quantified for four methods: strip, grid, full-area, and skirt reinforcement. All methods exhibit a pronounced critical depth threshold: the effective depth limits are 0.4H for strip, grid, and skirt reinforcement and 0.5H for full-area reinforcement, beyond which engineering benefit decreases substantially. Reinforcement width shows a marginal diminishing effect and should not be treated as the primary controlling parameter. Under the same area replacement ratio, skirt reinforcement leads to significant conical heave in the central excavation base, whereas strip reinforcement provides the best full-profile deformation control. Under the same total reinforcement volume, full-area reinforcement achieves the best performance across all deformation indices through uniform full-coverage constraint of the excavation base soil. This study elucidates the asymmetric deformation control mechanisms of shared-wall excavations and provides theoretical guidance for reinforcement scheme optimization in similar soft soil projects. Full article

(This article belongs to the Section Building Structures)

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