Poor indoor air quality reduces the comfort experienced in the environment and can also harm our physical health. Mechanical ventilation design plays an important role in improving the indoor environment and the safety of public toilets. Therefore, in this study, we aimed to evaluate public toilet ventilation design schemes through a digital twin to determine the most effective scheme for reducing indoor pollutant concentrations. In this study, we used Autodesk Revit to create a digital twin BIM of different ventilation systems. We simulated the diffusion of pollutants in these models using computational fluid dynamics (CFD)-based methods, and we used DesignBuilder to simulate building energy consumption. From the perspective of architectural design, we determined measures important for reducing the concentration of air pollutants by increasing the number and volume of air exchanges and controlling the installation height of exhaust vents. The results show that the ventilation design of an all-air air conditioning system with an exhaust height of 400 mm can remarkably improve the indoor environmental health and ventilation efficiency of public toilets, while consuming 20.4% less energy and reducing carbon emissions by 30,681 kg CO₂.

The outdoor wind environment and thermal environment are important factors affecting human comfort in cold winter conditions. The spatial layout of plant communities plays an important role in improving the outdoor microclimate and improving outdoor comfort. In order to explore the positive effect of plant layout on outdoor comfort in cold winter, this study took Xuzhou Tangfang Middle School with typical layout characteristics as the research object. In this study, we simulated the wind environment of these models using computational fluid dynamics (CFD) methods and the outdoor thermal environment using Ecotect (2011), and used linear regression and one-way ANOVA for mathematical statistics. The wind environment and Universal Thermal Climate Index (UTCI) of campus outdoor activities distributed in different spaces were analyzed and evaluated. The research results showed that the superposition of wind and thermal environments identified the key areas of the campus (cross-flow area and corner flow area) and showed a negative correlation. The staggered layout of the three plant combinations increases the wind prevention efficiency by 39.4%. At the same time, this study established the linkage mechanism of campus plant layout, environmental microclimate, and activity area comfort, which effectively improved outdoor human comfort in cold winter. This research can provide a reference for the remediation and improvement of the comfort of the same type of campus, and also provide data support and reference significance for the research on the outdoor pedestrian environment in winter.

The construction industry is energy-intensive and labor-intensive, which has great potential in reducing energy demand and carbon emissions. The construction method of off-site prefabricated components has many advantages to make it a good substitute for traditional methods. The purpose of this paper is to reduce the carbon emissions of prefabricated residential concrete members by improving the standardization rate of prefabricated components in the architectural design stage. In addition, this paper also uses building information modeling technology to establish Revit models and develops Revit using C# language to achieve rapid calculation of the prefabrication rate of building and standardization rate of components. The calculation results of the case show that, in the design stage, the carbon emissions are reduced by 2034.16kg CO₂e by improving the standardization rate of prefabricated components, accounting for 0.1552 of the carbon emissions of all prefabricated components. This study can help the designer reduce the carbon emissions of prefabricated components, and this technology may make a significant contribution in improving the environmental sustainability of prefabricated buildings.

As urbanization advances, rural construction and resource development in China encounter significant challenges, leading to the widespread adoption of standardized planning and design methods to manage increasing population pressure. These uniform approaches often prioritize economic benefits over climate adaptability and energy efficiency. This paper addresses this issue by focusing on traditional mountain villages in northern regions, particularly examining the wind and thermal environments of courtyards and street networks. This study integrates energy consumption and comfort performance analysis early in the planning and design process, utilizing Genetic and XGBoost algorithms to enhance efficiency. This study began by selecting a benchmark model based on simulations of courtyard PET (Physiological Equivalent Temperature) and MRT (mean radiant temperature). It then employed the Wallacei_X plugin, which uses the NSGA-II algorithm for multi-objective genetic optimization (MOGO) to optimize five energy consumption and comfort objectives. The resulting solutions were trained in the Scikit-learn machine learning platform. After comparing machine learning models like RandomForest and XGBoost, the highest-performing XGBoost model was selected for further training. Validation shows that the XGBoost model achieves an average accuracy of over 80% in predicting courtyard performance. In the project’s validation phase, the overall street network framework of the block was first adjusted based on street performance prediction models and related design strategies. The optimized model prototype was then integrated into the planning scheme according to functional requirements. After repeated validation and adjustments, the performance prediction of the village planning scheme was conducted. The calculations indicate that the optimized planning scheme improves overall performance by 36% compared with the original baseline. In conclusion, this study aimed to integrate performance assessment and machine learning algorithms into the decision-making process for optimizing traditional village environments, offering new approaches for sustainable rural development.

The article aims to reduce PM_2.5 concentration by improving the spatial comfort of blocks and the vegetation configuration. It mainly analyzes the impact of the following five aspects on the PM_2.5 concentration distribution in blocks, including different angles between the prevailing wind direction and blocks, different vegetation types, the distance between vegetation and buildings, vegetation height and building height, and different street tree configuration types on both sides of the block. The results show that: when the street angle is 45 degrees, the PM_2.5 concentration in the air is the lowest. The PM_2.5 concentration in the air is significantly improved when the enclosed vegetation type (F1–F2) is planted, and the spacing between vegetation and buildings has no obvious effect on PM_2.5 concentration distribution. There is a negative correlation between the height of vegetation on both sides and the PM_2.5 concentration. At the height of 6 m, the PM_2.5 concentrations on the windward and leeward sides are relatively balanced. When the street trees are evenly distributed, they have the least effect on reducing PM_2.5 concentrations. However, the richer the distribution levels of street trees, the more obvious the effect on reducing PM_2.5 concentrations.

As urbanization and rapid industrialization accelerate, rural areas face increasing pressure on resources and the environment, leading to challenges such as energy waste and reduced comfort. Traditional village planning and design methods are based on economic benefits and often lack consideration of climate adaptability. To address these issues, a comprehensive assessment of building and courtyard performance should be introduced early in the planning of traditional villages. This approach can better adapt the buildings to their climatic conditions. Introducing relevant performance indicators, such as outdoor comfort, indoor lighting, and building energy consumption, at the initial design stage is crucial. This article employs performance-based multi-objective optimization algorithms and machine learning techniques to investigate the design workflow of courtyards and their combinations. The goal is to enhance planners’ design efficiency in village planning by integrating data-driven and performance-driven methods. The research results show that during the performance-driven phase, by adjusting the spatial morphology and architectural parameters, the performance of the courtyard significantly improved compared to the baseline model. Energy efficiency increased by 32.3%, the physiological equivalent temperature (PET) comfort time ratio in winter was enhanced by 8.3%, and the ratio in summer increased by 3.8%. During the data-driven phase, the classification prediction accuracy of courtyard performance can reach 83%, and the F₁ score is 0.81. In the project validation phase, it has also been proven that the performance of different plans can be quickly verified. Compared to the base’s original status, the design solutions’ performance score can be improved from 59.12 to 85.62. In summary, this workflow improves the efficiency of the interaction between design decisions and performance evaluation in the conceptual stage of village planning, providing a solid foundation for promoting subsequent solutions.