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China is in the transition period of urbanization, and the imbalance of land use generated during the original rapid development of megacities still exists, resulting in the preservation of a large number of urban villages in the peripheral areas of cities. Unlike formal housing, these villages are heavily influenced by the confrontation and interweaving between top-down policies of local governments and bottom-up spontaneous construction by local villagers, resulting in a diverse and complex status quo. At the same time, self-built housing in urban villages has become a gathering place for a large number of low-income migrants due to its relatively low rental prices. In terms of the research of urban villages on the current situation of housing, the real needs of users and housing performance are mostly subjective judgments and conclusions. Therefore, the entry point of this paper is the leased space and the leasers, taking Laojuntang Village in Chaoyang District of Beijing as the research object and analyzing its current problems and the real needs of users. This paper adopts an integrated approach of fuzzy Kano model evaluation and building performance simulation. After analyzing the data and discussing the influencing factors, this paper identifies the specific problems and user needs that exist in leased spaces in Laojuntang Village: (1) The architectural performance of leased space is a Must-be Quality, which is the bottom-line requirement and must be improved. (2) The quality of the basic facilities and equipment of the leased space is mostly a One-dimensional Quality, Attractive Quality, and the degree of importance is lower than that of the building performance. (3) The external activity facilities of the leased space are mostly undifferentiated needs, for which the local users are not particularly prominent. (4) The scale and plot ratio of local housing is relatively high, and users are dissatisfied and complaining, which falls under the category of Reverse Quality. [ABSTRACT FROM AUTHOR]

In order to fully implement systematic, continuous and effective supervision of financial institutions and promote the safe, steady, and efficient operation of China's financial system, this research needs to develop a fully intelligent financial supervision information system, so as to take measures to effectively prevent and resolve financial risks. In this paper, based on ML (machine learning), an LSTM (long short-term memory) model with good comprehensive performance is built. This model is different from the existing scorecard model which relies on statistical learning. It not only further reduces the dependence on financial experts, but also has the ability of rapid iteration. The post-loan risk early warning model based on RF (random forest) algorithm is designed. The model parameters are optimized, which makes the risk early warning model have higher accuracy. The results show that when the data amount is 10,000 to 50,000, the accuracy of the model is relatively high when it is input into the model. When the amount of data is low, the overall throughput of the model is still very high. It shows that the pre-warning model of post-lending risk constructed in this paper has a strong risk prediction ability. [ABSTRACT FROM AUTHOR]

The proper application of machine learning and genetic algorithms in the early stage of library design can obtain better all-around building performance. The all-around performance of the library, such as indoor temperature, solar radiation, indoor lighting, etc., must be fully considered in the initial design stage. Aiming at building performance optimization and based on the method of "generative design", this paper constructs the library's comprehensive performance evaluation workflow and rapid prediction combined with the LightGBM algorithm. A library in a cold region of China is taken as the research object to verify its application. In this study, 5000 scheme samples generated in the iterative genetic optimization process were taken as data sets. The LightGBM algorithm was used to classify and predict design schemes, with a precision of 0.78, recall rate of 0.93, and F1-Score of 0.851. This method can help architects to fully exploit the optimization potential of the building's all-around performance in the initial stage of library design and ensure the timely interaction and feedback between design decisions and performance evaluation. [ABSTRACT FROM AUTHOR]

In the circumstances of "Carbon Peak," it is imminent to implement green construction technology in the construction industry as it is a major source of CO2 emissions. Nevertheless, little study has been done on the impact of self‐face awareness (SA) on green building project performance (GP). On the basis of a large‐scale questionnaire survey in China and an in‐depth analysis of the four categories (cognition, behavior, satisfaction, and performance), this paper has added an SA dimension, and proposed a new impacting path. Then, based on the improved conceptual model for measuring the impact of green construction behavior on GP, this paper has conducted empirical tests by using a large sample of questionnaire data and partial least square‐structural equation modeling method. The results show that: (1) green construction behavior has a positive impact on GP, and the impact of perceived behavioral control of green construction technology is most significant for promoting green construction in the industry, and SA has a significant positive effect on green construction in the industry; (2) environmental perception and perceived behavioral control for green construction technology play a mediating role; and (3) significant differences indeed exist in the multigroup analysis, and the biggest difference is working experiences in the engineering field. Finally, this paper has proposed corresponding management implications based on the above findings. The main contribution of this study is that this study has developed a new dimension (SA) to the model considering Chinese national and cultural differences, in an attempt to reveal the impact of green construction technology on GP in the Chinese context. In addition, the promotion of green construction (PGC) in the construction industry was also added based on the sustainability perspective, which is the first attempt to explore the role of the PGC. [ABSTRACT FROM AUTHOR]

Selecting an appropriate ANN model is crucial for speeding up the process of building performance simulation during the design phase of residential building layouts, particularly when evaluating three or more green performance metrics simultaneously. In this study, daylight, visual, and outdoor thermal metrics were selected as main green performance. To find the suitable ANN model, sensitivity analysis was used to obtain a set of proper parameters applied to the ANN structure. To train the ANN model with a higher predicting accuracy, this paper tested four different scenarios of ANN parameter setups to find some general guidelines about how to set up an ANN model to predict DF, sunlight hours, QuVue and UTCI. The results showed that an ANN model with a combined output variable demonstrated better average prediction accuracy than ANN models with a separated output variable. Having two times the number of training samplings compared to the number of input variables can lead to a high accuracy of prediction. The ideal number of neurons in the hidden layer was approximately 1.5 times the number of input variables. These findings of how to improve the ANN model may provide guidance for modeling an ANN for building performance. [ABSTRACT FROM AUTHOR]

Current requirements for residential energy-efficient design solutions in China are relatively homogeneous, requiring only improvements in overall building energy performance, often ignoring the energy differences between different residential housing units. This paper proposes a multi-objective research framework based on an occupant behavior model of air conditioning and a Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to study energy-efficient design solutions for residential envelopes. The optimal energy-saving design scheme has a 22.56% and 35.34% reduction in energy consumption difference between residential units and overall building energy consumption, respectively, both of which have been optimized substantially. Moreover, the overall energy performance of the building can be improved when the "energy performance difference between housing units" (EDH) is reduced, which verifies that EDH is an important criterion for improving the energy performance of residential housing units. [ABSTRACT FROM AUTHOR]

Research background: With the rapid development of civil engineering, the traditional low rise building model can no longer meet the needs of urbanization, and high-rise building structure has become the future development direction. In recent years, earthquake disasters occur frequently in China, which has caused huge losses to high-rise buildings. Therefore, we must strengthen the relevant research of high-rise building structures, which can make the high-rise building structural columns meet the requirements of safety performance. Therefore, the health detection and seismic performance evaluation of high-rise buildings will be of great significance. Research method: Through static elastic-plastic evaluation, we can calculate the dissipated strain energy of the structural column, which will further obtain the damage index of the structural column and obtain relevant data. Through this calculation method, we can complete the seismic performance evaluation of high-rise buildings, which is a calculation mode different from the traditional finite element software analysis. Through static elastic-plastic analysis, the seismic performance evaluation of high-rise buildings will have higher accuracy, which will build a damage index. Therefore, this paper uses a variety of research methods, mainly literature survey and empirical analysis. Through literature analysis, this paper can fully and correctly understand the methods of health detection and seismic performance evaluation of high-rise building structures at home and abroad, which will further clarify the current research level at home and abroad. Through empirical research, this paper analyzes specific examples, which can obtain more accurate results. Conclusion: In this paper, the seismic performance of high-rise buildings is comprehensively evaluated according to the damage index value of building structural columns. The simulation results show that more ideal seismic evaluation results can be obtained. [ABSTRACT FROM AUTHOR]

Prefabricated building construction has emerged as a transformative technology in construction engineering and the building industries. However, owing to its research characteristics, the relevant literature on prefabricated buildings is diverse and fragmented. This study offers a comparative review of relevant 21st century literature on prefabricated buildings using VOSviewer1.6.18 software. The research progress and future opportunities about prefabricated buildings were comprehensively analyzed, which provides recommendations for its subsequent development. Through keyword searches on the Web of Science, 3214 documents were identified, and an overall analysis of co-citations and co-authorship was conducted. Additionally, a comparative co-occurrence analysis highlighted the differences between China and other countries. Further elaboration of research hotspots is provided, and three future research directions are proposed: (1) energy conservation and reducing the environmental impact of prefabricated buildings, (2) improving the performance of prefabricated building components, and (3) deepening the understanding of the behavior of prefabricated structures under seismic and dynamic conditions. This study provides practitioners and scholars in the field of construction engineering with a comprehensive overview of the literature on prefabricated buildings and paves the way for future advancements in the industry. The findings of this study can be used to promote prefabricated buildings in the architecture, engineering, and construction industries. [ABSTRACT FROM AUTHOR]

This paper investigates the case building's cooling effect, energy saving, and carbon sequestration with green facade and green roof. The west and east GF is covered with wisteria, and the GR is planted with different kinds of vegetables. The cooling effect of the GF and GR is analyzed by field test. The cooling energy saving of the GF and GR is deeply discussed with EnergyPlus software simulation. Carbon sequestrations of the GF and GR are calculated considering both plant photosynthetic and energy saving with the field test and simulation. The results show that the average external surface temperature reduction of the west GF, east GF, and GR is 4.5°C, 4.2°C, and 9.7°C on sunny days, 1.3°C, 1.3°C, and 1.4°C respectively on cloudy days. The total cooling energy saving of the GF and GR is 4.75 kWh/m2. The cooling energy saving of the GR with seven kinds of vegetables varies from 529.6 to 2936.3 kWh, which is related to the LAI and height of vegetables. The daily cooling energy saving in hot sunny weather scenario is almost three times that in cool sunny. The carbon sequestration ability per planted GF area is 1.4 kg C/m2, and per planted GR area with seven different vegetables varies from 0.88 to 2.21 kg C/m2, in which the strongest is peanut, and the weakest is lettuce. At last, the cooling energy savings of GF in different orientations on hot, warm, and cool sunny days are discussed, which is shown that the cooling energy saving effect of the different oriented GF mainly depends on the received solar radiation amount in this orientation. The results of this research can provide some assistance in improving the design of the building GR and GF. [ABSTRACT FROM AUTHOR]

The excessive simplification of occupant behavior is considered as the most important factor that affects the uncertainty of building performance simulation, thus affects the reliability and generalizability of simulation-based design and forecast. In this paper, occupant behavior in air-conditioned office buildings of the Pearl River Delta (PRD) region was investigated and defined. Copies of 873 questionnaires about the occupant behavior in air-conditioned office buildings in the PRD region were collected to study the relationship between indoor environment quality and adaptive behaviors. Eight typical office occupant schedules were defined via K-means clustering method. A probability prediction model of cooling temperature set-point was established by using the Ordinal Logistic Regression method. According to the different control modes of air conditioning, window, blind and lighting equipment, four types of typical behavior patterns were proposed using the K-prototype clustering method, which could be developed into 20 typical occupant behavior styles of office buildings in the PRD region. [ABSTRACT FROM AUTHOR]

Purpose: A firm's inventions provide technical information for product planning and technical support for new product development (NPD). In the knowledge-based theory, inventing is regarded as a process of knowledge combination. This paper aims to classify the firm's inventive capabilities based on the combinatorial view and investigate the effects of inventive capabilities on NPD performance. Design/methodology/approach: Four types of inventive capabilities are identified concerned with the knowledge used to combine in the inventive activities. By utilizing a dataset of 572 firms from China's automotive manufacturing industry, the roles of different inventive activities in the generation of new inventions are compared. Then the effects of different inventive capabilities on NPD performance are empirically examined by using negative binomial regression analysis. Findings: The time series for the number of patented inventions derived from different types of combinations generally exhibits a steady upward trend, and the number of patents derived from recombination is much higher. The empirical results demonstrate that the inventive capabilities associated with reused recombination and creative recombination exhibit positive effects on NPD performance, and the inventive capabilities associated with novel combination and original combination exhibit non-linear effects on NPD performance. Originality/value: The findings contribute to NPD literature by investigating the effects of different inventive capabilities on NPD performance. This study also provides guidelines for manufacturing managers to improve NPD performance by building appropriate inventive capabilities. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanization process in China, the usage of sustainable green buildings is getting a great attention and significance in order to reduce energy consumption. However, there are still challenges for evaluating the performance of green buildings in China due to the lack of unified codes/standards. This paper proposes a method based on cloud model to evaluate the performance of green buildings. This method overcomes the randomness and subjectivity of fuzzy concepts to the maximum extent; on this basis, the system's indexes including environmental–economic balance, regional social coordination, health, comfort, and green management can be established. The analytic hierarchy process and entropy weight methods are used to calculate the index weight; the cloud model is used to realize the transformation between qualitative and quantitative characteristics; and then, a mathematical evaluation model based on the cloud model is established. Finally, an empirical study is made based on a typical case in Shenzhen city, China. The findings of this research work reveal a performance index for green building created based on expert evaluations and a standard cloud yardstick created in the comprehensive cloud model. Based on the results of this manuscript, the industry can acquire benefits by suggesting effective measures that can be implemented in all stages of green buildings construction where the measurements execution and persuasive components make appraisal models for a green building. This study provides a consistent framework for researchers and other public to evaluate green buildings, and provides a solid foundation for further research and consideration basis on sustainable development and green building operation. [ABSTRACT FROM AUTHOR]

A reliable and effective qualitative near-infrared (NIR) spectroscopy discrimination method is critical for excellent model building, yet the performance of models built by these methods is highly dependent on valid feature extraction. The goal of feature selection is to associate the selected variables with the property of interest, which many have done successfully. However, many of selection methods focus only on strong association with the analytes or properties of interest, neglecting correlations between variables. A variable selection method based on a fast nondominated-ranking genetic algorithm (NSGA-II) was proposed in this paper for qualitative discrimination of NIR spectra. The method had two objective functions: (1) maximizing the sum of ratios of interclass variance to intraclass variance, (2) minimizing the sum of correlation coefficients between the selected variables. FT-NIR spectra of a total of 124 tobacco samples from different origins and parts in Guizhou Province, China, were used as the experimental objects, and the part-grade discrimination models of tobacco leaves were established by combining this method with partial least squares-based discriminant analysis (PLS-DA), and compared with PLS-DA model based on the full spectrum. The results showed that the performance of PLS-DA model with the NSGA-II was improved, with a comparable or better correct discrimination rate and reasonable discrimination rate, and could discriminate different parts of the tobacco leaves well. It indicates that the NSGA-II can select a few and effective feature variables to build a high-performance qualitative discrimination model and is proved to be a promising algorithm. In addition, the method is not designed exclusively for spectral data. It is a general strategy that could be used for variable selection for other types of data. [ABSTRACT FROM AUTHOR]

The number of studies considering building performance optimization (BPO) in the building design phase is steadily growing, but many of the existing studies do not consider the applicability of many-objective optimization algorithms when increasing the objective dimensions. This article first compares the NSGA-II, IDBEA, MSOPS-II, and NSGA-III algorithms. Then, the algorithm most suitable for many-objective optimization is combined with Artificial natural network(ANN) and TOPSIS-AHP to complete the optimization of four dimensions of building energy consumption (EC), useful daylight illuminance (UDI), comfort time ratio (CTR) and energy-saving envelope cost (ESEC) for village houses in cold regions of China. The results show that the NSGA-III algorithm performs well in terms of convergence speed, convergence, diversity, and uniformity when solving many-objective problems compared to the other three algorithms. Finally, four optimization strategies were selected using the TOPSIS-AHP method. [ABSTRACT FROM AUTHOR]

Featured Application: In cold climatic zones of China, an attached sunspace passive solar heating system was installed on a farmhouse to improve the indoor thermal environment in winter. The application of attached sunspace passive solar heating systems (ASPSHS) for farmhouses can improve building performance, reduce heating energy consumption and carbon dioxide emissions. In order to take better use of the attached sunspace to prevent heat transfer or promote natural ventilation, this paper presented a zero-state response control strategy for the opening and closing time of active interior window in the ASPSHS. In order to verify the application of this strategy, an attached sunspace was built in an actual farmhouse. A natural ventilation heat exchange model was built based on the farmhouse with attached sunspace. The proposed zero-state response control strategy was implemented in TRNSYS software. Field measurement in living lab was carried out to inspect the distribution of the thermal environment in the farmhouse with attached sunspace under a zero-state response control strategy in the cold region of northern China. The experimental results show that, even under −5.0–2.5 °C ambient temperature, the application of zero-state response control strategy effectively increases the internal temperature to an average of 25.45 °C higher than the outside, with 23% indoor discernible temperature differential in the sample daytime. The whole-season heating performance was evaluated by simulating the model for the heating season in 2020–2021. The simulation demonstrates that the ASPSHS under zero-state response control strategy can maintain a basic indoor temperature of 14 °C for 1094 h during the heating season, with a daytime heating guarantee rate of 73.33%, thus ensuring higher indoor heating comfort during the day. When compared to a farmhouse with an attached sunspace under the zero-state response control strategy, the energy savings rate can be enhanced by 20.88%, and carbon emissions can be reduced by 51.73%. Overall, the attached sunspace with the zero-state response control strategy can effectively increase the indoor temperature when the solar radiation is intensive and create a suitable thermal environment for the farmhouse in the cold region of northern China. [ABSTRACT FROM AUTHOR]

The seismic performance of a building hinges on the seismic capacity and damage features of the reinforced concrete (RC) frame with masonry infill walls. To reasonably evaluate the seismic performance and seismic economic loss of masonry infill walls, it is necessary to consider the in-plane (IP) and out-of-plane (OOP) interactions of these walls under seismic actions, and to model the vulnerability of the infill walls and the frame. Based on the test data on masonry infill walls, this paper designs a performance indicator for infill wall in the light of IP-OOP interactions, and determines the response threshold of each damage state. With the aid of OpenSees, the authors developed and verified a reasonable modeling method for RC frames with infill walls. As per the current code in China, a 5-storey RC frame with infill walls was designed, and two three-dimensional (3D) space models were established for the structure by the proposed modeling method. One of them considers IP-OOP interactions, and the other does not. Then, the structure was subjected to incremental dynamic analyses (IDA), and different damage indicators were determined to examine the damage of the infill walls and the overall structure, producing a set of vulnerability curves. The results show that the consideration of IP-OOP interactions significantly increases the probability of seismic damages on the infill walls and the overall structure. The most prominent increase was observed in the medium to serious damage stages. [ABSTRACT FROM AUTHOR]

Limited by the climate, economic level and energy-saving awareness of rural residents in the western China, rural houses are still in a stage of high energy consumption (EC) and low comfort. The passive design strategies should be given priority to provide an optimal design mode. The challenge is to find the best combination of design parameters. In this paper, rural single-storey detached houses are taken as the research objects. Three conflicted indicators are set as the objective function, namely building EC, thermal comfort and economy. The investigated design variables include building orientation, insulating layer thickness, window width & type, and indoor design temperature. A methodology of combining EnergyPlus and MOBO is used for multi-objective optimization, thus getting the Pareto solutions and using the weighted sum method to obtain the optimum parameter combinations. The proposed methodology for simulation-based multi-objective optimization is a useful tool to facilitate decision-making in building design. [ABSTRACT FROM AUTHOR]

Photovoltaic shading devices (PVSDs) have the dual function of providing shade and generating electricity, which can reduce building energy consumption and improve indoor daylighting levels. This study adopts a parametric performance design method and establishes a one-click simulation process by using the Grasshopper platform and Ladybugtools. The research focuses on the effect of dynamic PVSDs on daylighting and energy consumption in an office building in Qingdao. The optimal configuration of PVSDs for each month under three dynamic strategies (rotation, sliding, and hybrid) is determined here. Additionally, different control strategies and fixed PVSDs are compared to clarify the impact of various control strategies on daylighting and energy consumption. The findings reveal that, compared to no shading, dynamic PVSDs in the rotation strategy, sliding strategy, and hybrid strategy can achieve energy savings of 32.13%, 47.22%, and 50.38%, respectively. They can also increase the annual average UDI by 1.39%, 2.8%, and 3.1%, respectively. Dynamic PVSDs can significantly reduce the energy consumption of office buildings in Qingdao while improving indoor daylighting levels. A flexible control strategy that adapts to climate change can significantly improve building performance. This research can provide theoretical, methodological, and data support for the application of the PVSD in cold-climate regions in China. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the effects of courtyard envelope design on the energy performance of office buildings in the hot summer–cold winter region of China. Two types of courtyard buildings were simulated with 200 energy models by changing the following variables: window–wall ratio (south, north, east, and west walls), window U-value, wall U-value, solar heat gain coefficient, and orientation. The treed Gaussian process (TGP) sensitivity analysis method was employed to quantify the contributions of parameters related to courtyard design that result in the changes of annual cooling, heating, lighting, and total energy consumption. The results show that the courtyard envelope design has a significant effect on the energy consumption in this case study. The most influential factor affecting annual cooling energy consumption and the annual total energy consumption is the orientation, which is responsible for 37% and 81%, respectively, in two layouts to annual cooling energy use. The corresponding proportion increased to 45% and 86% in terms of the total energy consumption. The most influential factor influencing annual heating energy use is the window U-value, which explained nearly 60% of the changes to the heating energy use. The effect of the solar heat gain coefficient in two kinds of layouts is as high as 82% and 79% for lighting energy use in this case study. Due to the different courtyard forms, the magnitude of the effect of the parameters on energy consumption and the main trend of the effect is slightly different. [ABSTRACT FROM AUTHOR]

• BIM-DB can effectively simulate building performance parameters. • The PSO-RF-NSGA-III hybrid method can be used to predict and optimize building performance. • NSGA can simultaneously optimize energy consumption, carbon emissions and thermal comfort. • The optimal building parameters are different in different regions. Near-zero-energy-consumption buildings (NZEBs) are of great significance for sustainable development, and their design and research have attracted increasing academic attention. To drive and realize the energy saving design and achieve carbon emission and thermal comfort optimization of NZEBs, in this paper, an intelligent optimization method integrating the BIM-DB and PSO-RF-NSGA-III method is established. Multiobjective optimization problems involving NZEBs in four typical climate regions in China are explored. With a typical office building as an example, first, simulation calculations regarding the energy consumption, carbon emissions and indoor thermal comfort in the four climatic regions are performed based on orthogonal tests and BIM-DB. Second, the nonlinear mapping relationships between building design parameters and prediction targets are constructed with the PSO-RF model, which is trained with sample data. The obtained nonlinear mapping relations are used to establish the objective function of NSGA-III, and the multiobjective Pareto-optimal solution set is obtained with the developed PSO-RF-NSGA-III algorithm. Finally, the only optimal solution is determined by using the ideal point method, and reference near-zero-energy-consumption office building parameters are calculated for different climate regions. The conclusions are as follows. (1) The PSO-RF algorithm can efficiently predict building energy consumption, carbon emissions and thermal comfort. In the four regions, the goodness of fit of the three targets is greater than 0.94. (2) Multiobjective optimization can be performed with the proposed RF-NSGA-III intelligent optimization method. After optimizing multiple groups of optimization schemes and adopting energy saving measures, the energy consumption levels in the four climate regions are reduced by 39.72 %, 32.22 %, 26.94 % and 35.37 %, and the other goals are optimized. (3) Index calculations indicate that the optimized building design parameters meet the specified standards for NZEBs, and the main influencing factors and corresponding measures vary from region to region. [ABSTRACT FROM AUTHOR]

Purpose-Rural residential buildings in China's cold areas still have large room for improvement in lighting, energy saving and other aspects due to their own development lag. Design/methodology/approach-Taking Zhaoyuan County residential buildings as an example, the spatial elements of residential buildings are quantified, and the variation intervals of each spatial element are determined based on this. Then the standard model of residential buildings is constructed, and the changing trend and related mechanism of heating energy consumption and natural lighting under the change of residential space elements are discussed. Findings-The results show that: in terms of heating energy saving, there is an obvious optimal solution between the window wall ratio of the south facade and the orientation of residential buildings; in indoor natural lighting, all spatial elements show positive and negative correlation without considering glare; building depth, eave height and plane girth area ratio are the key variables affecting heating energy consumption. Window to floor ratio, building eave height, building depth, and plane aspect ratio are the key variables that affect indoor lighting. The research results can provide reference for the energy-saving construction of residential buildings in cold areas. [ABSTRACT FROM AUTHOR]

Health climate is critical for achieving a better health performance in building construction projects. However, the topic is rarely investigated by extant literature. The aim of this study is to identify key determinants of health climate in building construction projects. To achieve this goal, a hypothesis was established between practitioners' perceptions of health climate and their health status, based on a comprehensive literature review and structured interviews conducted with experienced experts. Then, a questionnaire was developed and administered for data collection. Partial least-squares structural equation modeling was used for data processing and hypothesis test. Results showed that health climate in building construction projects is positively correlated with the health status of the practitioners, and that employment involvement was the most important determinant of health climate in building construction projects, followed by management commitment, and supportive environment. Moreover, significant factors under each determinant of health climate were also disclosed. As limited research has been conducted to examine health climate in building construction projects, this study bridges the knowledge gap and is a contributory work to the current body of knowledge of construction health. Additionally, the findings of this study can provide authorities and practitioners with a deeper understanding of construction health and thereby helping them bring forward more feasible measures to improve health in building construction projects. Thus, this study is useful to the practice as well. [ABSTRACT FROM AUTHOR]

• SL, DL and DRL models were constructed for multi performance prediction of gymnasiums under different weather conditions. • Accuracy, time, interpretability, ease-of-use, hyperparameters, and robustness of the metamodels have been investigated. • Energy consumption, temperature and CO 2 time series vectors prediction were modeling with nine training sets. • Methodology for hyperparameters comparison using exhaustive grid search and sensitivity analysis. • RDPG is most accurate but time-consuming. LSTM is more efficient and also has relatively excellent accuracy. Building performance simulation (BPS) is essential for testing energy demand and indoor environment quality of different building designs. However, software for BPS is computationally intensive and impractical to run thousands even millions of simulations for performance analysis and optimization. Especially for the large space buildings, which usually have complex forms and high energy consumption. The computational problem could be overcome by the adoption of metamodels. Most correlational research focuses on single performance prediction of a particular building, which makes the model less robust when applied to multi vectors prediction for different buildings under multiple weather conditions. This paper leveraged six metamodels, which include recurrent deterministic policy gradient (RDPG), asynchronous advantage actor-critic (A3C), long short-term memory (LSTM), convolution neural network (CNN), artificial neural network (ANN) and support vector regression (SVR), to predict hourly-based multi performance vectors of gymnasiums under various design parameters and multiple weather conditions. Six metamodels are trained and tested on a large scale of datasets simulated by EnergyPlus over four gymnasium cases in different cities of China. The accuracy, efficiency, ease-of-use, robustness and interpretability of the models are investigated. To conduct a fair and detailed comparison, a methodological approach using grid searches for model settings selection assisted by sensitivity analysis is pursued. Principal component analysis (PCA) is also adopted to interpret the work process of the metamodels. The comparison showed that the RDPG model provides the most accurate prediction results with R2 converges at 0.993, 0.982 and 0.941 for energy, temperature and CO 2 , respectively. LSTM model is more efficient than RDPG, and suitable for users who need emphasis on both time and accuracy. ANN is suitable for users with limited time and require models of ease-of-use and robustness. SVR and ANN could be used for the automatically co-simulation with BPS software. In future research, the influence of occupant behavior also should be investigated. [ABSTRACT FROM AUTHOR]

• POE was carried out to compare the design target and operational effect after use. • A Large Irregular Green Commercial Building was chosen for in-depth analysis. • We recommend the building energy saving and carbon emission reduction strategy. Commercial buildings, especially large irregular commercial buildings with complex space and dense occupation, have high energy consumption and carbon emissions. Moreover, the actual operating performance differs significantly from its design, in terms of energy conservation and carbon emissions. This study selected an irregular large-scale green commercial building in Dalian, a coastal city in the cold zone of China. The running energy consumption and indoor physical environment were investigated and analyzed, and a satisfaction survey was conducted using the Post-occupancy evaluation (POE) methods. The promotion strategies of holistic design were proposed by comparing the operational building performance with the design target. The research shows that the energy consumption of the building was 68.02 kWh/m2, which failed to meet the standard requirements. The carbon emission was 74.76 kgCO 2 /m2. Less than 10 % of humidity in summer fell in the expected range, and the lowest value of PM 2.5 concentration in winter was 28.3 %. The score of indoor environmental quality and service satisfaction was low. This paper identifies the problems and analyzes the differences between the operation stage and design phase. It provides recommendations and guidelines for optimizing the carbon emission and indoor environment based on the performance improvement of building operational conditions. [ABSTRACT FROM AUTHOR]

Wind resistance is one of the most important safety targets for high-rise buildings, especially slab-shaped ones with relatively large length–width ratios. In this study, the characteristics of wind pressure on a reduced-scale model of a slab-shaped high-rise building were analyzed experimentally. The experiment was conducted using the DTC Initium electronic scanning pressure measurement system in the wind tunnel at the Xiamen University of Technology, China. The spatial distribution and time-frequency characteristics of the wind pressure signals were analyzed with various wind flow directions under uniform and boundary-layer inflow conditions. The results show that both of the inflow conditions and the wind directions have significant influences on the magnitude and distribution characteristics of the wind pressure on the building surfaces. The wavelet transform-based analysis shows that the wind pressure on the building surfaces presents obvious intermittent characteristics, with the instantaneous energies pulsating intensively in the time-frequency domain, illustrating the unsteady nature of the wind pressure loads on the building. The influence and risk of the unsteady pulsating pressure loads should be considered when evaluating the wind-resistant performances of this type of building. [ABSTRACT FROM AUTHOR]

Given the prediction of global warming, there is a growing concern about overheating in the severe cold and cold regions of China. In the past decades, indoor thermal comfort has been neglected in building practice, while efficiency-oriented improvements have been developed, such as increased insulation of building envelope. An extensive literature review shows contradictory conclusions about whether increased insulation can alleviate overheating. The aim of this research is to conduct simulations based on measured data to reveal the influence of insulation on overheating in dwellings in these regions of China. An unoccupied residential flat was monitored to determine the extent of overheating while eliminating the effects of other contributors, such as natural ventilation. Validated building performance simulations were carried out with altered insulation layer thickness in Integrated Environmental Solutions-Virtual Environment IESVE software to examine its influence on overheating during summertime in five representative cities. The results showed clear evidence of summer overheating in these regions. In Yichun, Harbin, Shenyang, Dalian and Beijing, walls with increased insulation were found to exacerbate overheating in the bedrooms without natural ventilation by 22.5%, 16.6%, 20.3%, 11.8% and 6.9%, respectively, compared to the uninsulated walls. This study provides useful information for building regulations on energy efficiency and thermal comfort. [ABSTRACT FROM AUTHOR]

Monitoring buildings' heat loss and quickly discovering high energy-consuming buildings in a certain area is of great significance for urban management departments to propose urban governance and management measures from the perspective of global optimization. Developing a 3D-infrared (3D-IR) model of a building or a complex created with infrared thermograph (IRT) obtained by unmanned aerial vehicle (UAV) technologies would greatly contribute to a comprehensive observation on the thermal performance of a study objective. In this paper, a procedure for high energy-consuming building screen based on the 3D infrared (IR) models of multiple buildings is proposed and demonstrated as an effective approach for regional evaluation comparing with measured heating energy data provided by the local municipal department. Firstly, the 3D-IR model of multiple buildings is established, where the single building models are built based on UAV-IRT technology respectively, and the modelling condition as well as the temperature-color mapping relationship of each building is unified. Secondly, the measuring schemes and the evaluation method of the building thermal performance based on the 3D-IR model is proposed. Thirdly, the energy-consuming performance of five buildings in Jining, a city in northern China, is measured and analyzed by using the above-mentioned methods. Finally, the proposed evaluation method of the building performance is applied to evaluate the tested buildings. By referencing to measured heat energy data, the proposed method can be taken as an efficient and reasonably accurate screen method. [ABSTRACT FROM AUTHOR]

Mechanical ventilation has a great impact on building simulation performance, such as indoor environment quality and building energy consumption. However, there is still a lack of accurate mechanical ventilation models established from long-term field data that can effectively predict building performance. In this study, one-year measurements on mechanical ventilation operation behavior were collected from 85 apartments, which were conducted with a mechanical ventilation system of the same brand in cold regions of North China. This permitted statistical analysis and clustering of the mechanical ventilation operation behavior by using the K-means method, leading to five behavior patterns. The results showed that 24% households operated mechanical ventilation system nearly all day, and there was a large difference in usage behaviors between the split system and the centralized system. Furthermore, two classes of models based on random forest and logistic regression were developed for predicting mechanical ventilation system operation (on/off) behavior. The models based on random forest showed high accuracy as it resulted in a 0.992 average in predictions. These models using field data can guide the selection of accurate input boundary conditions of mechanical ventilation and improve the accuracy of dwelling numerical simulations. [ABSTRACT FROM AUTHOR]

Background: Energy-efficient university campuses will play a vital role in the development of future sustainable cities, and will be important for achieving the Chinese carbon-neutrality goals. It is, therefore, necessary to develop new decision-making tools for evaluating the sustainability of campus buildings. Since university campuses typically comprise a broad variety of building types, standardized evaluation methods and tools, such as Green BIM, are needed. Green BIM (Building Information Modeling) emphasizes the importance and role of BIM technology in the design and construction of green buildings, providing a standardized framework for the decision-making process, and methods for improving the green performance of buildings. Methods: This study develops a method based on the Green BIM framework, using BIM architecture to analyse building performance, and the Assessment Standard for Green Building (GB/T 50378-2019) standard to establish benchmark values for evaluation, and project objectives. The method is evaluated on three examples of the most representative university buildings in northern China. The goal is to understand common denominators and differences between different types of campus buildings, in terms of green building indicators, that are important to consider in the early design stages of campus building complexes. Results: In this study, a library is used as a case study to demonstrate the tools for evaluating green performance. The study optimizes green performance from five aspects: surrounding environment, function layout, envelope performance and system transformation, and management measures improvement. The results show that this optimization scheme can achieve reductions of the annual loads of about 47.4%, in line with the national energy efficiency standards for public buildings. In particular, the heating load was reduced by 59.1%, and the cooling load reduced by 21.5%. Conclusion: A comprehensive approach, combining the aspects of planning, building design, system design, energy management, and energy conservation planning, is required to improve the green performance of university buildings to meet the goals. In the future, it will be further necessary to perform data mining of energy consumption patterns, and continue energy retrofitting of existing buildings and energy systems, to achieve the goal of green and low-carbon campuses. [ABSTRACT FROM AUTHOR]

The concept of net zero energy building (NZEB) has attracted worldwide attention and has the potential to achieve significant energy and emissions reductions. Various technical strategies have been demonstrated to be able to achieve the net-zero-energy target. However, this issue becomes more complex when the economic performances of these strategies are considered. Although studies on the economic performance of NZEBs can be found in the literature, gaps in the research still exist. This paper presents a systematic methodology that combines life cycle cost analysis, benefit-cost analysis, and dynamic building performance simulation to effectively evaluate the economic performance of individual NZEB technologies and holistic technical strategies and determine the most economically competitive options. To demonstrate the application and validity of the methodology, a case study of office buildings in the Yangtze River Delta region of China is presented. Although this case study is ad hoc, the proposed methodology is applicable to buildings of other types and in other regions as long as the climatic and economic conditions are known. • A methodology for developing economically efficient NZEB strategies is proposed. • The methodology combines building energy simulation, LCC and benefit-cost analysis. • A prototype of office buildings in the Yangtze River Delta region is established. • Economically competitive NZEB strategies for the prototype building are developed. • Without extra rooftop PV, designing the prototype as NZEB is economically inviable. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the comprehensive percentage influence of input parameters on building energy and comfort performance by a new approach of sensitivity analysis (SA) and explore the most reliable and neutral sampling and sensitivity assessment method. The research combined 7 sampling methods with 13 SA methods to comprehensively integrate the percentage influence of 25 input parameters on building energy and comfort performance in 24 coastal cities of China. The results have found that the percentage influence of many important input parameters is affected by geographical position. Considering both energy and comfort performance of the building, the key parameters are heating setpoint, infiltration rate, cooling setpoint, roof U value, roof solar absorptance, window solar heat gain coefficient, equipment, and occupant density, all of which could comprehensively impact 70% of energy demand and comfort performance along the Chinese coastline. This is of great significance for policymakers to formulate relative building regulations. After comparing the F-test and the exceed percentage test, we recommended the Pearson with Quasi-random sampling method as the most reliable SA assessment method in building simulation, followed by the standardized regression coefficient in random sampling and Latin hypercube sampling methods, which can achieve data closest to the average value. [ABSTRACT FROM AUTHOR]

High‐rise buildings are typically subjected to horizontal loads as wind and earthquake, therefore, to provide an adequate lateral stiffness and resistance, structural cores should be considered within the structure. For this reason, to provide alternative yet advantageous solutions, a comparison between concrete cores and composite cores for high‐rise buildings' performances is presented here. Some recent applications in US and China, indeed, showed how the steel‐concrete composite shear wall solution could give better results in terms of construction time and constructability ensuring a significant financial gain. Different ideas of composite shear walls will be presented and parameters of stiffness and resistance for each cross section will be evaluated. Thereafter, the best solutions, in terms of the abovementioned parameters and constructability, will be implemented into a real case of a building, modelled on ETABS software and compared with the original solution in reinforced concrete. In addition to highlighting the structural benefits of the proposed solution, it will be shown that the proposed solution has lower carbon footprint compared to other conventional solutions. [ABSTRACT FROM AUTHOR]