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| 1 | Advanced data analytics for enhancing building performances: From data-driven to big data-driven approaches显示文摘Buildings have a significant impact on global sustainability.During the past decades,a wide variety of studies have been conducted throughout the building lifecycle for improving the building performance.Data-driven approach has been widely adopted owing to less detailed building information required and high computational efficiency for online applications.Recent advances in information technologies and data science have enabled convenient access,storage,and analysis of massive on-site measurements,bringing about a new big-data-driven research paradigm.This paper presents a critical review of data-driven methods,particularly those methods based on larger datasets,for building energy modeling and their practical applications for improving building performances.This paper is organized based on the four essential phases of big-data-driven modeling,i.e.,data preprocessing,model development,knowledge post-processing,and practical applications throughout the building lifecycle.Typical data analysis and application methods have been summarized and compared at each stage,based upon which in-depth discussions and future research directions have been presented.This review demonstrates that the insights obtained from big building data can be extremely helpful for enriching the existing knowledge repository regarding building energy modeling.Furthermore,considering the ever-increasing development of smart buildings and IoT-driven smart cities,the big data-driven research paradigm will become an essential supplement to existing scientific research methods in the building sector. | Cheng Fan Da Yan Fu Xiao Ao Li Jingjing An Xuyuan Kang | 2021 | Building Simulation2021，14，1： | 10 |
| 2 | An action-based Markov chain modeling approach for predicting the window operating behavior in office spaces显示文摘Reliable energy and performance prediction for building design and planning is important for newly-designed or retrofitted buildings.Window operating behavior has an important influence on the ventilation and energy consumption of these buildings under different realistic scenarios.Therefore,quantitatively describing this behavior and constructing a prediction model are important.In this work,an action-based Markov chain modeling approach for predicting window operating behavior in office spaces was proposed.Two summer measurement data(2016 and 2018)were used to verify the accuracy and validity of the modeling approach.The opening rate,outdoor temperature,time distribution,and on-off curve were proposed as four inspection standards.This study also compared the prediction performance between the action-based Markov chain modeling approach with the state-based Markov chain modeling approach,which is the most popular modeling approach to model occupant window operating behavior.This study proved that the yearly variation of occupants’behavior performed a form of action that remained unchanged during a certain period.Meanwhile,the results also proved that the action-based Markov chain modeling approach can reflect the actual window operating behavior accurately within an open-plan office,which is a beneficial supplement for energy-consumption simulation software in a window-state prediction module.The state-based Markov chain modeling approach showed better stability and accuracy in terms of the opening rate,whereas the action-based Markov chain modeling approach showed good consistency with the measurement data in the on-off curves and in situations with little data.For the on-off curves,the accuracy of action-based modeling approach in the prediction of window open-state is 20%higher. | Xin Zhou Tiance Liu Da Yan Xing Shi Xing Jin | 2021 | Building Simulation2021，14，2： | 7 |
| 3 | Impacts of technology-guided occupant behavior on air-conditioning system control and building energy use显示文摘Occupant behavior is an important factor affecting building energy consumption.Many studies have been conducted recently to model occupant behavior and analyze its impact on building energy use.However,to achieve a reduction of energy consumption in buildings,the coordination between occupant behavior and energy-efficient technologies are essential to be considered simultaneously rather than separately considering the development of technologies and the analysis of occupant behavior.It is important to utilize energy-efficient technologies to guide the occupants to avoid unnecessary energy uses.This study,therefore,proposes a new concept,“technology-guided occupant behavior”to coordinate occupant behavior with energy-efficient technologies for building energy controls.The occupants are involved into the control loop of central air-conditioning systems by actively responding to their cooling needs.On-site tests are conducted in a Hong Kong campus building to analyze the performance of“technology-guided occupant behavior”on building energy use.According to the measured data,the occupant behavior guided by the technology could achieve“cooling on demand”principle and hence reduce the energy consumption of central air-conditioning system in the test building about 23.5%,which accounts for about 7.8%of total building electricity use. | Rui Tang Shengwei Wang Shaobo Sun | 2021 | Building Simulation2021，14，1： | 6 |
| 4 | A data-driven model predictive control for lighting system based on historical occupancy in an office building: Methodology development显示文摘The lighting system accounts for 8%of the total electricity consumption in commercial buildings in the United States and 12%of the total electricity consumption in public buildings globally.This consumption share can be effectively reduced using the demand-response control.The traditional lighting system control method commonly depends on the real-time occupancy data collected using the passive infrared(PIR)sensor.However,the detection inaccuracy of the PIR sensor usually results in false-offs.To diminish the false-error frequency,the existing lighting system control simply deploys a delayed reaction period(e.g.,5 to 20 min),which is not sufficiently accurate for the demand-response operation.Therefore,in this research,a novel data-driven model predictive control(MPC)method that is based on the temporal sequential-based artificial neural network(TS-ANN)is proposed to overcome this challenge using an updated historical occupancy status.Using an office as case study,the proposed model is also compared with the traditional lighting system control method.In the proposed model,the occupancy data was trained to predict the occupancy pattern to improve the control.It was found that the occupancy prediction mainly correlates with the historical occupancy ratio and the time sequential feature.The simulation results indicated that the proposed method achieved higher accuracy(97.4%)and fewer false-offs(from 79.5 with traditional time delay method to 0.6 times per day)are achieved by the MPC model.The proposed TS-ANN-MPC method integrates the analysis of the occupant behavior routine into on-site control and has the potential to further enhance the control performance practice for maximum energy conservation. | Yuan Jin Da Yan Xingxing Zhang Jingjing An Mengjie Han | 2021 | Building Simulation2021，14，1： | 6 |
| 5 | A real-time forecast of tunnel fire based on numerical database and artificial intelligence显示文摘The extreme temperature induced by fire and hot toxic smokes in tunnels threaten the trapped personnel and firefighters.To alleviate the potential casualties,fast while reasonable decisions should be made for rescuing,based on the timely prediction of fire development in tunnels.This paper targets to achieve a real-time prediction(within 1 s)of the spatial-temporal temperature distribution inside the numerical tunnel model by using artificial intelligence(Al)methods.A CFD database of 100 simulated tunnel fire scenarios under various fire location,fire size,and ventilation condition is established.The proposed Al model combines a Long Short-term Memory(LSTM)model and a Transpose Convolution Neural Network(TCNN).The real-time ceiling temperature profile and thousands of temperature-field images are used as the training input and output.Results show that the predicted temperature field 60 s in advance achieves a high accuracy of around 97%.Also,the Al model can quickly identify the critical temperature field for safe evacuation(i.e.,a critical event)and guide emergency responses and firefighting activities.This study demonstrates the promising prospects of Al-based fire forecasts and smart firefighting in tunnel spaces. | Xiqiang Wu Xiaoning Zhang Xinyan Huang Fu Xiao Asif Usmani | 2022 | Building Simulation2022，15，4： | 6 |
| 6 | Physiological and subjective thermal responses to heat exposure in northern and southern Chinese people显示文摘When studying the thermal adaptation of building occupants,understanding the effects of different thermal experiences on adaptation is necessary,particularly for moderate and severe heat exposure.However,this area has seen limited research.Further,skin temperature,a common parameter for quantifying thermal sensation,may insufficiently reflect the automatic thermoregulation of the human body.This study investigates the effects of long-term heat exposure on the human body using multiple physiological and subjective indexes.Two heat exposure experiments were conducted on healthy male participants from northern and southern China.Participant responses,including skin temperature,heart rate,heart rate variability,blood volume pulse(BVP),subjective thermal comfort thermal sensation,thermal acceptability,and normalized high and low frequency values were collected and compared The results indicated that the subjective responses of northern and southern participants were not significantly different;however,the subjective physiological symptoms and self-reported discomfort of the latter were less than those of the former,indicating that the southern participants had superior heat tolerance.Additionally,the physiological responses of all the participants were largely similar.However,southern participants showed slightly higher normalized high frequency and BVP values,indicating that they have more active vagus nerves and better vasodilation.They also showed a wider acceptable temperature range and better acclimation to heat exposure.Notably,the mean skin temperature could not effectively predict thermal sensation during heat exposure;this was more accurately achieved using the rate of change of skin temperature.These findings suggest that long-term thermal experiences can affect building occupants’thermal adaptability. | Yufan Lin Liu Yang Maohui Luo | 2021 | Building Simulation2021，14，6： | 5 |
| 7 | Design-oriented stress–strain model for FRP-confined concrete显示文摘 | L. Lam J.G. Teng | 2003 | Construction and Building Materials2003，，6： | 5 |
| 8 | Evaluating the improvement effect of low-energy strategies on the summer indoor thermal environment and cooling energy consumption in a library building:A case study in a hot-humid and less-windy city of China显示文摘Public buildings such as libraries consume a vast amount of cooling energy for maintaining a comfortable and stable indoor environment in summer,especially in the hot-humid climate.This study used a case study approach to discuss the effect of low-energy strategies that can be applied to improve indoor thermal environment and cooling energy consumption of library buildings in hot and humid cities like Nanning City(a southern city,China).The use of cooling window shutters(a shutter with the effects of shading and evaporative cooling)and ceiling fans for generating airflow was considered as applicable energy-saving measures in this study,and a university library was selected as the study building in which the two energy-saving measures were employed.The SET*and annual cooling load before and after the adoption of the proposed measures were quantitatively investigated with a building energy consumption simulation software(DesignBuilder).Simulation results showed that the daytime SET*values can be reduced by 3.0℃and 4.5℃respectively on a typical summer day after the use of the cooling shutters and ceiling fans.Moreover,the cooling loads can also be decreased by 8.4%and 16.6%respectively.Particularly,the combination of these two measures enabled the daytime SET*value and annual cooling load lower by 7.0℃and 60.8%respectively. | Yigang Li Jiang He | 2021 | Building Simulation2021，14，5： | 5 |
| 9 | DeST 3.0:A new-generation building performance simulation platform显示文摘Buildings contribute to almost 30%of total energy consumption worldwide.Developing building energy modeling programs is of great significance for lifecycle building performance assessment and optimization.Advances in novel building technologies,the requirements of high-performance computation,and the demands for multi-objective models have brought new challenges for building energy modeling software and platforms.To meet the increasing simulation demands,DeST 3.0,a new-generation building performance simulation platform,was developed and released.The structure of DeST 3.0 incorporates four simulation engines,including building analysis and simulation(BAS)engine,HVAC system engine,combined plant simulation(CPS)engine,and energy system(ES)engine,connected by air loop and water loop balancing iterations.DeST 3.0 offers numerous new simulation features,such as advanced simulation modules for building envelopes,occupant behavior and energy systems,cross-platform and compatible simulation kernel,FMI/FMU-based co-simulation functionalities,and high-performance parallel simulation architecture.DeST 3.0 has been thoroughly evaluated and validated using code verification,inter-program comparison,and case-study calibration.DeST 3.0 has been applied in various aspects throughout the building lifecycle,supporting building design,operation,retrofit analysis,code appliance,technology adaptability evaluation as well as research and education.The new generation building simulation platform DeST 3.0 provides an efficient tool and comprehensive simulation platform for lifecycle building performance analysis and optimization. | Da Yan Xin Zhou Jingjing An Xuyuan Kang Fan Bu Youming Chen Yiqun Pan Yan Gao Qunli Zhang Hui Zhou Kuining Qiu Jing Liu Yan Liu Honglian Li Lei Zhang Hong Dong Lixin Sun Song Pan Xiang Zhou Zhe Tian Wenjie Zhang Ruhong Wu Hongsan Sun Yu Huang Xiaohong Su Yongwei Zhang Rui Shen Diankun Chen Guangyuan Wei Yixing Chen Jinqing Peng | 2022 | Building Simulation2022，15，11： | 5 |
| 10 | Study on the application of reinforcement learning in the operation optimization of HVAC system显示文摘Supervisory control can be used to optimize the HVAC system operation and achieve building energy conservation,while reinforcement learning(RL)is considered as a promising model-free supervisory control method.In this paper,we apply RL algorithm to the operation optimization of air-conditioning(AC)system and propose an innovative RL-based model-free control strategy combining rule-based and RL-based control algorithm as well as complete application process.We use a variable air volume(VAV)air-conditioning system for a single-storey office building as a case study to validate the optimization performance of the RL-based controller.We select control strategies with the rule-based control controller(RBC)and proportional-integral-derivative(PID)controller respectively as the reference cases.The results show that,for the air supply of single zone,the RL controller performs the best in terms of both non-comfortable time and energy costs of AC system after one-year exploration learning.The total energy consumption of AC system reduced by 7.7%and 4.7%,respectively compared with RBC and PID strategies.For the air supply of multi-zone,the performance of RL controller begins to outperform the reference strategies after two-year exploration learning and two-year buffer stage.From the seventh year on,RL controller performs much better in terms of both non-comfortable time and operating costs of AC system,while the operating cost of AC system is reduced by 2.7%to 4.6%compared with the reference strategies.In addition,RL controller is more suitable for small-scale operation optimization problems. | Xiaolei Yuan Yiqun Pan Jianrong Yang Weitong Wang Zhizhong Huang | 2021 | Building Simulation2021，14，1： | 5 |
| 11 | Multi-objective optimization of thermochromic glazing based on daylight and energy performance evaluation显示文摘Many efforts have been detected to investigate thermochromic(TC)glazing for improving building energy saving,while only a few approaches for daylight performance analysis.In this study,the performance of TC glazing is investigated based on multi-objective optimization for minimizing energy demand while maximizing daylight availability.The effects of five parameters including transition temperature,solar transmittance in clear state,solar transmittance modulation ability,luminous transmittance in clear state,and luminous modulation ability on the building energy consumption and useful daylighting illuminance(UDI_(300-3000))are examined.Linear Programming Technique for Multi-dimensional Analysis of Preference(LINMAP)is used for the decision-making of Pareto frontier.This research aims to explore the ideal thermochromic glazing by considering the daylight and energy performance of a typical office room,taking the weather condition of Xiamen,China as an example.Although it is impossible to achieve both optimal values of energy consumption and UDI_(300-3000)simultaneously,the proposed multi-objective optimization method could still provide low energy consumption with sufficient daylight.The non-dominated sorting of Pareto optimal solution(POS)demonstrated that the optimum building energy consumption and UDI_(300-3000)for single glazed windows are 46.64 kWh/m^(2)and 70.92%,respectively,while the values for double glazed windows are 44.40 kWh/m^(2)and 71.88%,respectively.The selected hypothetical TC windows can improve the building energy and daylighting performance simultaneously when compared with traditional clear single and double glazed windows.The presented framework provides a multi-objective optimization method to determine the most suitable TC glazing technologies for designers and architects during the design and retrofit procedure. | Xiaoqiang Hong Feng Shi Shaosen Wang Xuan Yang Yue Yang | 2021 | Building Simulation2021，14，6： | 4 |
| 12 | The energy performance and passive survivability of high thermal insulation buildings in future climate scenarios显示文摘Given that the passive performance simulation of buildings based on typical meteorological year data and specific design schemes makes it challenging to respond to climate change and refine design requirements on time,this article established a passive performance prediction model for future buildings considering multi-dimensional variables including climate change,building design,and operational characteristics.For high thermal insulation buildings under future climates,the mild climate zone is more sensitive than the others,cooling energy demand is more sensitive than heating demand,apartments are more sensitive than office buildings,and passive survivability is more sensitive than energy performance;for buildings of the same type located in the same climate zone,thermal design solutions determine the increase rate of cooling demand.The potential benefits of climate warming on heating demand reduction are almost zero,but the cooling demand increases significantly,with apartments and office buildings increasing up to 22.1% and 5.0%,respectively.Buildings generally overheat in the future,and the increase rate of the mild zone far exceeds other zones with duration and severity being 3004.8% and 877.7%for apartments,and 884.3% and 288.9%for office buildings,respectively. | Ran Wang Shilei Lu Xue Zhai Wei Feng | 2022 | Building Simulation2022，15，7： | 4 |
| 13 | Development of a procedure for estimating the parameters of mechanistic VOC emission source models from chamber testing data显示文摘In order to evaluate the impacts of volatile organic compounds(VOCs)emissions from building materials on the indoor air quality beyond the standard chamber test conditions and test period,mechanistic emission source models have been developed in the past.However,very limited data are available for the required model parameters including the initial concentration(C_(m0)),in-material diffusion coefficient(D_(m)),partition coefficient(Kma),and convective mass transfer coefficient(k_(m)).In this study,a procedure was developed for estimating the model parameters by using VOC emission data from standard small chamber tests.In the procedure,initial values of the model parameters were refined by multivariate regression analysis of the measured emission data.To verify the procedure and estimate its uncertainty,simulated chamber test data were generated by adding 10% experimental uncertainties on the theoretical curve from the analytical solution to a mechanistic emission model.Then the procedure was applied to the generated data to estimate the model parameters.Results indicated that estimates converged to the original parameter values used for the data generation and the error of estimated parameters D_(m1)C_(m0) and K_(ma) were within±10%,±23%,and±25%of the true values,respectively.The procedure was further demonstrated by applying it to estimate the model parameters from real chamber test data.Wide application of the procedure would result in a database of mechanistic source model parameters for assessing the impact of VOC emissions on indoor pollution load,which are essential input data for evaluating the effectiveness of various indoor air quality(IAQ)design and control strategies as well as the energy required for meeting given IAQ requirements. | Zhenlei Liu Andreas Nicolai Marc Abadie Menghao Qin John Grunewald Jianshun Zhang | 2021 | Building Simulation2021，14，2： | 4 |
| 14 | Numerical study on indoor environment and thermal comfort in train station waiting hall with two different air-conditioning modes显示文摘With the increasing size of the waiting hall,the large-area use of transparent envelope materials makes the interior surface temperature of the envelope too high,which puts forward higher requirements for the control of environment and thermal comfort indoors.In this paper,the characteristics of indoor temperature distribution,relative humidity distribution and thermal comfort under the all air system(AAs)and the radiant floor cooling and wall cooling combined with air supply system(RC/ASs)were investigated in the large spaces.The computational fluid dynamics(CFD)method was used,and the performance of the CFD model was validated by comparing the measured results with CFD simulation results in the AAs.The numerical results clearly showed that the temperature and relative humidity indoors could satisfy the design requirement both in the AAs and RC/ASs.The indoor air distribution in most areas under the RC/ASs was more uniform based on the indoor heat and humidity requirements under the cooling load of measured day.In the AAs,the total cooling capacity of air conditioning unit was the highest when indoor thermal comfort was the best,that meant that to achieve the best working condition,the air-conditioning system need high energy consumption.Meanwhile,the RC/ASs addressed the problem that the temperature around the seated passengers in the waiting area was relatively high in the AAs.This paper will provide reference for the air conditioning system design in the similar large spaces in the future. | Biyao Zhuang Juan Shi Zhenqian Chen | 2021 | Building Simulation2021，14，2： | 4 |
| 15 | A simulation-based method to investigate occupant-centric controls显示文摘Occupant-centric control (OCC) strategies rely on different algorithms to learn and predict occupants’ patterns and preferences, then utilize these predictions to optimize building operations. However, testing different OCC algorithms or fine-tuning their configurations in real buildings can be a lengthy process. To this end, we present a framework for testing OCCs in a simulation environment prior to field implementation. The proposed workflow entails using synthetic occupant behaviour models and simulating OCC strategies to learn their preferences. The goal is to enable quick comparison of different OCC configurations under various scenarios by modifying occupant behaviour assumptions, as well as climate and design parameters. For proof-of-concept, the proposed method was applied in a case-study to simulate OCCs for lighting and heating/cooling setpoint adjustments in a single office under various occupant types, as well as OCC settings and design configurations. Results demonstrated the benefits of the proposed framework and its potential for providing a more holistic evaluation of OCCs under different scenarios. Using the proposed framework, building designers and operators can identify potential issues with OCCs and fine-tune their settings prior to field implementation. | Mohamed M.Ouf June Young Park H.Burak Gunay | 2021 | Building Simulation2021，14，4： | 4 |
| 16 | Study on the optimal operation mode of ventilation system during metro double-island platform fire显示文摘Large metro transfer stations have been widely constructed in China,among which the double-island station faces the serious fire safety issues owing to its large passenger flow.In this paper,simulation cases were carried out to investigate the effectiveness of different ventilation modes by jointly operating tunnel ventilation fan(TVF)and platform screen doors(PSD)under two typical fire scenarios in the platform.The numerical model was established by Fire Dynamics Simulator software and verified via reduced-scale model experiments.The results indicate that the TVF mode of supplying at the end near fire and exhausting at the other end is superior to that of exhausting at both ends.Besides,activating more PSD and TVF on the both sides of platform will restrict smoke in one end to the greater extent.During a fire in the middle of the platform,opening all PSD near tunnel-2 and TVF in tunnel-2 and tunnel-3 is the most appropriate mode.While during a fire at the left end of the platform,activating all PSD and TVF on both sides is the optimal operation mode.The conclusions can provide guidance for smoke control design and on-site emergency ventilation operation in double-island platform fire. | Zeng Long Yuxuan Yang Chang Liu Maohua Zhong | 2021 | Building Simulation2021，14，3： | 4 |
| 17 | Investigation of natural ventilation performance of large space circular coal storage dome显示文摘Large space circular coal storage dome(LSCCSD)offers an environmental and dependable alternative to open stockpiles,and it has been consequently widely applied in China.However,due to the lack of scientific guidelines,its natural ventilation performance is lower than expected.Natural ventilation potential strongly depends on the roof geometry and opening mode,which have not yet been investigated for LSCCSD.This paper presents a detailed evaluation of the impact of dome geometry(rise span ratio),opening height,and opening modes on the ventilation performance of LSCCSD.The evaluation is based on computational fluid dynamics(CFD)methods and is validated by available wind tunnel testing.We employed three evaluation indicators,which are wind pressure coefficient,effective ventilation rate,and wind speed ratio.The results demonstrate that the rise span ratio has a significant effect on the wind pressure difference and the effective ventilation rate increases by approximately 9%–42%with a single-annular opening.When double-annular openings are set in a strong positive pressure zone,the effective ventilation rate increases by 100%and the average wind speed ratio increases by 50%.When it is compared with single one with similar opening height,the effective ventilation rate increases by 25%.The optimum natural ventilation performance for LSCCSD is achieved at a rise span ratio of 0.37.In addition,the lateral middle opening is kept higher than the ridge top of the coal pile.The proposed evaluation approach and design parameters provided instructive information in the building design and ventilation control for LSCCSDs. | Yaohua Hou Chao Chen Yu Zhou Zhixiang Yang Shen Wei | 2021 | Building Simulation2021，14，4： | 4 |
| 18 | Multi-objective optimization of building design for life cycle cost and C0_(2) emissions:A case study of a low-energy residential building in a severe cold climate显示文摘Currently,building construction and operation are responsible for 36%of global final energy usage and nearly 40%of energy-related carbon dioxide(CO_(2))emissions.From the sustainable development perspective,it is crucial to consider the impact of construction material on the achievement of life cycle benefits.This study proposed a simulation-based multi-objective optimization method to minimize both life cycle cost and CO_(2) emissions of buildings.We built an energy simulation model with hybrid ventilation and light-dimming control in EnergyPlus based on an operational passive residential building in a severe cold climate.Next,this investigation selected insulation thickness,window type,window-to-wall ratio,overhang depth and building orientation as design variables.The study ran parametric simulations to establish a database and then used artificial neural network models to correlate the design variables and the objective functions.Finally,we used the multi-objective optimization algorithm NSGA-Ⅱ to search for the optimal design solutions.The results showed potential reductions of 10.9%-18.9%in life cycle cost and 13.5%-22.4%in life cycle CO_(2) emissions compared with the initial design.The results indicated that the optimization approach in this study would improve building performance.The optimal values of the design variables obtained in this study can guide designers in meeting economic and environmental targets in passive buildings. | Qingwen Xue Zhaojun Wang Qingyan Chen | 2022 | Building Simulation2022，15，1： | 4 |
| 19 | Challenges and opportunities for carbon neutrality in China’s building sector—Modelling and data显示文摘The building sector is one of the largest energy user and carbon emitter globally.To achieve China’s national carbon target,the building sector in China needs to achieve carbon peaking and neutrality targets by 2030 and 2060,respectively.However,data deficiency on building energy and emissions become barriers for tracking the status of building energy and emissions,and identify potential opportunities for achieving dual carbon targets.To address these shortcomings,this study established an integrated China Building Energy and Emission Model(CBEEM).With CBEEM,this study evaluated the building-construction and building-operation energy and emissions in China,and revealed the status quo and potential challenge and opportunities.According to modelling results,building operation energy use of China was 1.06 billion tce in 2020,accounting for 21%of China’s total primary energy consumption.Building construction energy consumption was 0.52 billion tce in 2020,accounting for another 10%of total primary energy consumption.Key messages found on building carbon emissions are:building construction embodied emissions were 1.5 billion tCO_(2) in 2020 and are declining slowly,building operational carbon emissions were 2.2 billion tCO_(2) in 2020 and are still increasing.International comparisons between China and other countries on building stock,energy use intensity and carbon emission intensity were conducted as well,and help shed a light on the challenges for decarbonization of China’s building sector.Finally,technology perspectives to achieve carbon neutrality target were discussed and related policy suggestions were provided. | Shan Hu Yang Zhang Ziyi Yang Da Yan Yi Jiang | 2022 | Building Simulation2022，15，11： | 4 |
| 20 | Effect of random inclusion of sisal fibre on strength behaviour of soil显示文摘 | J Prabakar R.S Sridhar | 2002 | Construction and Building Materials2002，，2： | 4 |
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