Waterfront buildings are increasingly popular in river-dense cities such as Changsha, where water bodies contribute significantly to urban microclimate regulation. However, uncoordinated building spatial design can compromise these benefits and lead to suboptimal outdoor thermal comfort. Existing studies often focus on isolated factors without addressing their combined effects on outdoor thermal comfort. Following ENVI-met simulation, Morris sensitivity analysis, and Generalized Additive Modelling, this study identifies dominant morphological factors and assesses their seasonal synergistic impact. Building upon these results, a comprehensive thermal comfort index tailored to waterfront environments (PETCIW) is proposed, integrating spatial, environmental, and seasonal variables through the Analytic Hierarchy Process and multiple linear regression analysis. Based on 40 residential cases, the study develops climate-responsive design strategies that balance seasonal needs. The findings offer a quantitative foundation and practical guidance for early-stage urban design in waterfront areas, contributing to more resilient and thermally adaptive urban planning.

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

Courtyard dwellings represent a significant architectural typology widely distributed across diverse climatic zones globally. Evidence indicates that users in various regions of China are implementing energy-saving modifications to their courtyard spaces, characterized by the transformation of original open courtyards into indoor spaces through the Courtyard Being Roofed (CBR). However, the climatic adaptability and energy-saving effectiveness of CBR remain unclear due to its spontaneous, non-standardized implementation. This study evaluates CBR performance across 339 Chinese cities using EnergyPlus-based simulations, validated by field measurements. Results indicate that CBR yields annual energy savings of 8,465 kWh (approximate to 26 %) for conditioned functional rooms (e.g., bedrooms, living room). This reduction is largely attributed to a decrease in heating load, despite a concurrent increase in cooling demand that is frequently observed in warm climates. Statistical analysis identifies annual mean dry-bulb temperature and altitude as dominant predictors of energy-saving potential (R2 = 0.96), enabling the development of a simplified predictive model. Furthermore, K-means clustering classifies the study area into four climatic suitability zones-Severe Cold, Cold High-Altitude, Moderate Heating Demand, and Low Heating Demand-each with tailored optimization strategies. By integrating large-scale simulation, mechanism analysis, and regional classification, this research provides a practical framework for climateresponsive retrofitting of courtyard dwellings and informs sustainable renovation policies.

This study proposed a multi-objective optimization framework for buildings incorporating stochastic energy use behaviors, exploring energy-saving designs for college dormitories. First, EnergyPlus models for inner and outer corridor dormitories were constructed, and the optimization objectives were defined, respectively, building energy consumption (BEC), thermal discomfort time (TDT), initial incremental cost (IIC), and global incremental cost (GIC). Subsequently, global sensitivity analyses were conducted on building design parameters to identify key technologies, and artificial neural networks were utilized to predict building performance rapidly. Then, the non-dominated genetic algorithm-II was used to perform multi-objective optimizations on college dormitories. Pareto optimal solution sets were obtained, from which eleven optimal solutions were extracted: the lowest BEC solution, the lowest TDT solution, and the lowest IIC, GIC, and balanced solutions under constraints for three energy-saving goals. The results indicated that the optimization focus for the lowest BEC and TDT solutions lay in enhancing the U-value of roofs and exterior walls, improving air infiltration and air conditioning (AC) performance; the optimization focus for the lowest IIC and GIC solutions centered on improving air infiltration and AC performance, followed by insulation for roofs, south walls, and north walls; while that for balanced solutions pointed toward improving air infiltration, AC performance, and exterior wall insulation. In addition, compared to the Chinese near-zero energy standard (GB/T 51,350-2019), the BEC of the lowest IIC solution significantly reduced costs while fulfilling corresponding energy-saving goals. This study can provide methodological guidance and data support for the construction of zero energy college dormitories in China.

Accurate assessment of microclimatic characteristics in urban blocks is a prerequisite for climate-adaptive urban design. However, traditional numerical simulations face high computational costs. This study proposes a novel data-driven prediction framework that combines semantic computing of multi-source Street View Imagery (SVI) with stacked ensemble learning to demonstrate the spatial heterogeneity and diurnal asymmetry of the urban microclimate. Focusing on five typical urban functional zones, three mainstream computer vision datasets were comparatively tested to extract morphological parameters. A two-layer stacked prediction model was constructed to interpret the nonlinear spatiotemporal driving mechanisms of six microclimatic variables. The results indicate that: (1) The Stacking model achieves high accuracy, with R2 values for air temperature and relative humidity exceeding 0.97. ADE20K outperforms other models in predicting Mean Radiant Temperature (Tmrt ) and Predicted Mean Vote (PMV) due to its fine-grained semantic granularity. (2) Microclimatic driving mechanisms exhibit prominent spatial heterogeneity: high-density central business districts are dominated by geometric morphology (SVF/Enclosure), the influence of vegetation (GVI) is more significant in low-density areas, and old communities are affected by the thermal capacity effects of Impervious Surface Ratio (ISR). (3) Significant diurnal asymmetry exists: long-wave radiation dominates at night, and the explanatory power of static visual features is higher than during the day. This study also proposes the concept of the ”Steady-state Variance Trap” to explain the physical reasons for the decline in model performance during stable atmospheric periods. These findings provide a reference for morphology-driven microclimatic interventions in urban systems.

Building energy efficiency plays a crucial role in reducing carbon emissions and achieving sustainable development. However, during the building design stage, due to the discrepancy between idealized energy use behavior and actual conditions, the effectiveness of energy-saving measures often falls short of expectations, hindering the achievement of energy efficiency goals. To solve this issue, a multi-objective optimization framework for ultra-low-energy college dormitories based on actual energy use behavior was proposed, with building energy consumption (BEC), thermal discomfort time (TDT), and initial incremental cost (IIC) as optimization objectives. First, based on the actual energy use behavior, an EnergyPlus model was constructed for college dormitories in Changsha, China. Then, the key building design parameters were identified through global sensitivity analysis. Furthermore, an agent model was constructed using artificial neural networks to rapidly predict building performance. Finally, for different building design scenarios, the non-dominated sorting genetic algorithm II was used to perform multi-objective optimization of the design parameters. The results showed that air infiltration and air-conditioning (AC) performance were the most critical factors influencing BEC; air infiltration, south wall insulation, and north wall insulation were the primary factors affecting TDT, while AC performance, interior wall insulation, and floor insulation had the greatest impact on IIC. Compared to the base case (Standard JGJ 134-2010), seven optimal solutions reduced BEC by 9.3% to 50.7%, TDT by 1.2% to 7.8%, and increased IIC by 5.6 to 288.4 CNY/m2. This study can provide methodological guidance and data support for the construction of ultra-low-energy college dormitories in China.

Vernacular architecture is deeply rooted in specific regions and evolves under urbanization while maintaining a close connection to the natural environment. Using the case study of vernacular courtyards evolving into vernacular atriums, this study examines the spatial distribution characteristics of vernacular atriums in 37 counties (districts) in southern Hebei, China, by mining data through deep learning networks. The results reveal a tendency for vernacular atriums to concentrate in the southwest, while regions less frequented, such as the east and north, hold potential for promotion. Furthermore, the correlation between 15 geo-climatic factors and vernacular atriums is explored. Geodetector analysis indicates that altitude, slope, rainfall, wind speed, sunshine duration, shortwave radiation intensity and PM2.5 provide varying levels of explanatory power for different types of vernacular atriums. The response of different factors reflects the similarities and differences in the needs of various vernacular atriums to improve the living environment and adapt to the climate conditions, such as insulation, sunshade, lighting, rain protection, and ventilation, while adapting the geographical environment through house form and culture. With AI-assisted fieldwork, this study offers insights into the macro-scale climate adaptability of vernacular courtyards, inspiring sustainable development in vernacular architecture.

Tibetan-style dwellings form a significant part of China’s residential heritage, reflecting distinct plateau characteristics and a rich Tibetan cultural history. With the rapid advancement of urbanization, traditional Tibetan-style dwellings have evolved spontaneously to accommodate modern lifestyles. This study focuses on Tibetan-style dwellings in the urban villages of Shannan City, Tibet Autonomous Region, as research samples. Through fieldwork and on-site measurements, the study documents the evolution of these dwellings, identifying the spontaneous changes in the context of urban development. It explores the mechanisms by which urbanization influences the evolution of Tibetan-style dwellings and proposes strategies for their scientific development. The findings reveal that local Tibetan-style dwellings have undergone significant changes in various aspects, including house forms, overall layout, spatial functions, and structural decorations over time. The driving factors behind these changes include economic conditions, industrial and population structures, education levels, the materials market, construction technology, and housing needs. Based on the characteristics and mechanisms of the evolution of Tibetan-style dwellings, the study offers recommendations for their modernization, providing valuable insights for their sustainable development.

The solar collector is a key component of solar assisted air source heat pump (SAASHP) systems that absorbs solar energy and converts it into heat, significantly affecting the system performance. The traditional control method (TCM) for solar collector in SAASHP systems utilizes several key parameters: the upper and lower limit temperature differences, and the flow rate of pump. However, these parameters were set to constant values and cannot be adjusted for varying solar irradiance, resulting in poor system performance. To solve the above problem, this study proposed a novel dynamic operation method (DOM) for SAASHP systems, and investigated its performance and feasibility using a university dormitory hot water supply as an example. TRNSYS was used to simulate the SAASHP system and the accuracy of the model was verified by field test. The results showed that the DOM dynamically adapted control parameters according to the solar irradiance, thus significantly enhancing the solar energy utilization. For the DOM, the thermal efficiency of solar collector was 34.2% and the system coefficient of performance was 4.58, which improved by 11.8% (6.5%) and 8.3% (3.4%), respectively, compared to the pre-optimization (post-optimization) TCM. In addition, the DOM exhibited excellent performance in different climate zones of China.

Forest land plays a vital role as a terrestrial carbon sink. Urbanization, particularly the conversion of forest land into agricultural and construction areas, has significantly affected the carbon sink capacity of forests. The protection of carbon sinks in forest land has become a critical issue in advancing the dual carbon strategy. Taking Liushahe Town as a case study, this study develops an integrated framework of analysis and response strategies, which encompass “land use change prediction, forest land carbon sink evaluation, and multi-objective optimization”. The purpose is to identify an optimal forest planning scheme that balances carbon sink capacity and biodiversity. The results indicate that: (1) Land use change substantially affects the extent of forest land in Liushahe Town, in which the area exhibits an initial increase followed by a decrease, and is projected to shrink to 89.88% of its 2021 level by 2041. (2) There are significant disparities in carbon sink performance among various forest land plots. The strategic elimination of inefficient plots and preservation of those with high carbon sink potential are key to enhancing the resilience of forest land to disturbances. (3) Multi-objective optimization planning schemes effectively reconcile carbon sinks and biodiversity, and enhance the synergistic effects of forest ecosystem services. Overall, this research provides practical guidance and methodological support for the protection of carbon sinks in forest land within township-scale spatial planning.

College dormitories, characterized by high occupancy and energy density, have huge potential for energy savings. However, most existing energy efficiency studies of college dormitories dealt with air conditioning (AC) use behavior on fixed schedules, ignoring the stochastic nature of occupants, resulting in gaps between the designed and actual energy consumption, and making the application of energy-saving technologies much less effective. The key to solving this problem is to understand and quantify energy use behavior in college dormitories. Therefore, this study conducted a large-scale questionnaire survey to obtain AC use modes in college dormitories and reveal the intrinsic connection between them and energy consumption, and based on this, categorize typical behaviors. Firstly, typical building models of college dormitories in Changsha were built through field surveys. Subsequently, AC use modes in college dormitories were obtained through a large-scale questionnaire survey. Furthermore, the energy consumption of each AC use mode was simulated by EnergyPlus, and several representative AC use patterns were clustered using the K-means algorithm. Finally, based on the actual AC use behavior, the energy consumption and thermal comfort characteristics of typical dormitory models were analyzed. The results categorized five representative patterns in college dormitories, namely little AC use in summer” pattern (2.56%), “no AC in winter “pattern (25.71%), “no AC” pattern (2.7%), “thermal sensation-activated “pattern (29.91%), and “time-based” pattern 39.12%). These findings are beneficial to building energy efficiency, specifically, researchers and designers can use these typical AC use patterns and their proportions to predict building energy consumption, thus eliminating the inaccuracies brought about by personnel behaviors, and then to achieve more accurate building energy simulations and to ensure the effectiveness of energy-saving technologies.

In plateau regions like Tibet, the cold climatic conditions persistent challenges for local building performance, while abundant solar energy resources enable low-carbon building retrofits. On the Qinghai-Tibetan plateau, many residents have enclosed their open courtyards with Courtyard Being Roofed (CBR) renovations to enhance indoor thermal comfort and increase temperatures. However, improper CBR implementation may compromise energy savings, worsen indoor light and heat environments, and lead to unnecessary financial burdens. This study analyzes CBR's operational mechanisms, quantifies its performance trade-offs, and identifies risks under plateau conditions through a residential building case in Shannan City. Focusing on annual energy consumption (AEC), thermal discomfort hours (TDH), and initial cost (IC), we apply the NSGA-II algorithm and Artificial Neural Networks (ANN) to optimize CBR across conflicting objectives. The variables considered include courtyard dimensions, material properties, shading devices, and operational schedules. Sobol sensitivity analysis was employed to optimize these variables. Results show that, compared to the open courtyard, the optimal solution reduces AEC by 53 % and TDH by 63 %; compared to the actual retrofit scenario, it further lowers AEC by 10 % and TDH by 39 %, requiring only 47 % of the implemented cost. Additionally, the study ranks the sensitivity of different parameters to these objectives, highlighting window materials and shading systems as critical factors. This research uncovers the complexities and contradictions of solar energy utilization in highland areas, providing a theoretical foundation and practical guidelines for courtyard dwellings in plateau regions and contributing to the sustainable development of Tibetan residential dwellings.

The gap between the designed and actual energy performance of buildings (BEPG) poses a severe challenge to achieving building energy efficiency goals and has garnered global attention. However, previous studies have predominantly concentrated on the identification of factors influencing BEPG and have not systematically explored the interrelationships among these factors. In this study, the Integrated Decision-Making combined with Trial Evaluation Laboratory and Adversarial Interpretive Structural Modeling (DEMATEL-AISM) were employed to explore the driving mechanism for BEPG. Initially, a total of 40 influencing factors were identified through literature review and interviews, and 18 representative factors were selected from the building lifecycle perspective. Subsequently, the importance level and causal attributes of each influential factor were assessed by the DEMATEL method. Furthermore, a multi-level hierarchy was constructed based on the AISM model to illustrate the intricate interrelationships among the representative factors. The results indicated that there were seven cause factors and eleven effect factors for BEPG, with the six most significant factors were setting errors in simulation, lack of supervision, performance of equipment, differences in building performance, occupant behavior, and improper design. Additionally, the hierarchical structure model was categorized into five levels, which comprehensively indicated the driving relationship between the 18 influencing factors and the generation mechanism of BEPG. Finally, nine targeted strategies to address BEPG were proposed. This study offers new insights into the mechanisms of BEPG formation through the integration of DEMATEL and AISM, providing a robust framework for researchers and practitioners to systematically understand and address BEPG, as well as essential guidance for policymakers to develop effective strategies to reduce BEPG.

The aim of this study is to optimize the exterior wall insulation of different orientations to further reduce the heating and cooling energy consumption of office buildings, and to analyze its economic, energy and carbon saving potential in China. A six-story office building was selected as the case building and its energy consumption was evaluated using TRNSYS, considering variations in latitude, window-to-wall ratio, aspect ratio, and exterior wall U-values of four orientations. In addition, the exterior wall insulation in four directions was optimized by coupling artificial neural network and genetic algorithm. The main results showed that in the 20(degrees)N to 40(degrees)N region of China, the GA-optimized exterior wall U-values were highest in the southern direction, followed by the eastern and western directions, and finally the northern direction. In addition, overall, the optimal combination of exterior wall U-values showed a trend of high at low latitudes and low at high latitudes, and increased with increasing WWR and decreased with increasing AR. Finally, compared to the exterior wall requirements for energy efficient buildings in GB 55015-2021, the optimized office buildings at low altitude in China showed a 3.26 %, 6.77 % and 6.59 % reduction in ALCC, total energy consumption and total carbon emissions respectively.

Assessing the heat-related health risks is crucial for promoting the sustainable development of cities, particularly in the face of extreme climates and urban human settlement governance. Heat health risk assessment serves as a foundational element within the risk governance framework, serving to mitigate heat-related morbidity and mortality rates. The revitalization of old neighborhoods, especially those situated in city centers with deficient facilities, emerges as a critical imperative within this context. This study chose Changsha, a city in Central China that is severely affected by high temperatures and has numerous old neighborhoods, as a case study. We developed a heat risk framework that incorporates city level risk, exposure, vulnerability, and the adaptation of old neighborhoods. Validated through data accessed from medical facilities visit, the framework effectively reflects the distribution of heat risks. By combining qualitative and quantitative methods, we investigated the correlation between heat health risk levels and the behaviors of residents in old neighborhoods. The results indicate that urban planners should prioritize comprehensive renovations in medium-risk neighborhoods, resident behavior management in low-risk neighborhoods, and the indoor thermal environment in high-risk neighborhoods. This framework plays an important role in the assessment of future spatial risk for old neighborhoods renewal.

The inherent complexity of historic buildings, particularly their internal structures, presents significant challenges to the efficiency of digital model creation. This paper aims to enhance modeling efficiency by automating the creation of timber frames using a procedural modeling method. It translates the architectural rules used by local carpenters into modeling rules for procedural modeling, allowing for the automatic generation of a digital model that closely resembles the actual timber frame with a few simple constraints. While some manual identification and correction are still necessary, the workload is significantly reduced compared to traditional methods, and the precision meets the requirements of HBIM. The results show that this approach greatly improves the efficiency of modeling large-scale historic buildings and serves as a valuable complement to traditional HBIM methods. Future research will focus on enhancing the integrity and diversity of the models, such as expanding the range of supported traditional building types.

Envelope insulation is an essential measure to reduce building energy consumption and is the focus of many building energy codes. In China, the current standard (GB 55015-2021) only specified limit values for the thermal conductivity (U) of the exterior walls, and it ignored variations in building characteristics and internal parameters. Therefore, it is difficult for architects to quickly determine the exterior wall U-value in building design and retrofit. To address this issue, the exterior wall U-values of office buildings in China were economically optimized by TRNSYS, ANN, and GA. To begin with, the effect of enhancing exterior wall insulation on the heating and cooling energy consumption of office buildings was investigated by TRNSYS software, taking into account variations in building characteristics. Moreover, the exterior wall U-values of office buildings were optimized by coupling ANN and GA. The results showed that the design values of the GB 55015-2021 did not achieve the optimal results in China, and the most economical exterior wall U-value was 0.24 W/(m2 & sdot;K) in Harbin, 0.24 W/(m2 & sdot;K) in Beijing, 0.42 W/(m2 & sdot;K) in Changsha, 1.82 W/(m2 & sdot;K) in Guangzhou, 0.66 W/(m2 & sdot;K) in Kunming. Compared with the pre-optimization, the annual life cycle cost of building heating and cooling reduced by 0.96 % in Harbin, 2.07 % in Beijing, 1.44 % in Changsha, 1.68 % in Guangzhou, and 7.37 % in Kunming. In addition, variations in building parameters should be considered in the design phase of new buildings or in the renovation of old buildings, such as internal heat gain, which may lead to different optimal results.

The building energy performance gap (EPG) seriously restricts the improvement of building energy efficiency. Currently, although many studies on EPG, it is not yet fully understood and addressed. To fill this gap, this paper conducted an extensive review of EPG research. Firstly, the magnitude of EPG was summarized from many case studies, and the results showed that it varied greatly among building types, with gap ratios ranging from 0.5 to 4 for educational/research buildings, concentrated between 0.5 and 2.5 for residential buildings, and between 0 and 1 for office building. Then, fifteen direct causes and seven in-depth drivers of EPG were analyzed from simulation and lifecycle perspectives, and the linkages between them were established. Furthermore, solutions for EPG were summarized, including some state-of-the-art technical and “soft” measures, and their correspondence with the underlying causes. Finally, eight future research recommendations were proposed based on the limitations of existing strategies.