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建筑模拟（英文版）
建筑模拟（英文版）

双月刊

1996-3599

建筑模拟（英文版）/Journal Building SimulationCSCD北大核心EISCI
正式出版
收录年代

    The use of green infrastructure and irrigation in the mitigation of urban heat in a desert city

    Kai GaoShamila HaddadRiccardo PaoliniJie Feng...
    679-694页
    查看更多>>摘要：Severe urban heat,a prevalent climate change consequence,endangers city residents globally.Vegetation-based mitigation strategies are commonly employed to address this issue.However,the Middle East and North Africa are under investigated in terms of heat mitigation,despite being one of the regions most vulnerable to climate change.This study assesses the feasibility and climatic implications of wide-scale implementation of green infrastructure(GI)for heat mitigation in Riyadh,Saudi Arabia—a representative desert city characterized by low vegetation coverage,severe summer heat,and drought.Weather research forecasting model(WRF)is used to simulate GI cooling measures in Riyadh's summer condition,including measures of increasing vegetation coverage up to 60％,considering irrigation and vegetation types(tall/short).In Riyadh,without irrigation,increasing GI fails to cool the city and can even lead to warming(0.1 to 0.3 ℃).Despite irrigation,Riyadh's overall GI cooling effect is 50％lower than Gl cooling expectations based on literature meta-analyses,in terms of average peak hour temperature reduction.The study highlights that increased irrigation substantially raises the rate of direct soil evaporation,reducing the proportion of irrigation water used for transpiration and thus diminishing efficiency.Concurrently,water resource management must be tailored to these specific considerations.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    Systematic review of the efficacy of data-driven urban building energy models during extreme heat in cities:Current trends and future outlook

    Nilabhra MondalPrashant AnandAnsar KhanChirag Deb...
    695-722页
    查看更多>>摘要：Energy demand fluctuations due to low probability high impact(LPHI)micro-climatic events such as urban heat island effect(UHI)and heatwaves,pose significant challenges for urban infrastructure,particularly within urban built-clusters.Mapping short term load forecasting(STLF)of buildings in urban micro-climatic setting(UMS)is obscured by the complex interplay of surrounding morphology,micro-climate and inter-building energy dynamics.Conventional urban building energy modelling(UBEM)approaches to provide quantitative insights about building energy consumption often neglect the synergistic impacts of micro-climate and urban morphology in short temporal scale.Reduced order modelling,unavailability of rich urban datasets such as building key performance indicators for building archetypes-characterization,limit the inter-building energy dynamics consideration into UBEMs.In addition,mismatch of resolutions of spatio-temporal datasets(meso to micro scale transition),LPHI events extent prediction around UMS as well as its accurate quantitative inclusion in UBEM input organization step pose another degree of limitations.This review aims to direct attention towards an integrated-UBEM(i-UBEM)framework to capture the building load fluctuation over multi-scale spatio-temporal scenario.It highlights usage of emerging data-driven hybrid approaches,after systematically analysing developments and limitations of recent physical,data-driven artificial intelligence and machine learning(AI-ML)based modelling approaches.It also discusses the potential integration of google earth engine(GEE)-cloud computing platform in UBEM input organization step to(ⅰ)map the land surface temperature(LST)data(quantitative attribute implying LPHI event occurrence),(ⅱ)manage and pre-process high-resolution spatio-temporal UBEM input-datasets.Further the potential of digital twin,central structed data models to integrate along UBEM workflow to reduce uncertainties related to building archetype characterizations is explored.It has also found that a trade-off between high-fidelity baseline simulation models and computationally efficient platform support or co-simulation platform integration is essential to capture LPHI induced inter-building energy dynamics.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    Investigation of acoustic attributes based on preference and perceptional acoustics of Korean traditional halls for optimal design solutions

    Beta Bayu SantikaHaram LeeJin Yong Jeon
    723-738页
    查看更多>>摘要：Immersed in the rich tapestry of traditional culture,Gugak,the traditional Korean music stands as a captivating embodiment of artistic expression.This study embarked on a comprehensive evaluation of a Gugak hall,employing acoustic measurements,computer simulations,and subjective perception surveys.The evaluation focused on the reverberance,clarity,spatial impression,and preference,unravelling the secrets that shape the immersive Gugak experience.Through intricate computer simulations and auralization,the experience of Gugak performances was meticulously brought to life,allowing exploration under diverse conditions by adjusting stage volume ratios from-20％to+20％and modifying the interior materials,including the walls,ceiling,and lateral reflectors.Although Gugak halls exhibited relatively low values of reverberation time(RT),early decay time(EDT),and binaural quality index(BQI)the dominant factor influencing the acoustic environment was the effect of sound strength(G).Musical clarity(C80)value did not show an inverse proportionality to the reverberation time.Furthermore,genre differences between traditional Korean and Western classical music did not significantly affect listeners'perception and satisfaction with regards to reverberance,clarity,and spatial impression.As a result,Gugak halls can adhere to the same acoustic design criteria as Western orchestra halls,since this study found that people perceived them the same way.In this study,sound strength was found to be strongly correlated with perception indicators.It was possible to enhance listeners'perception and preference regarding the acoustic environment through material and structural changes to the sidewalls and ceiling.These changes improved the reinforcement of low frequencies and simultaneously enhanced the relative effect of side reflections.Additionally,enhancing the reflection and spatial characteristics of the materials effectively improved listener preference.Based on these findings,an optimal design solution was proposed.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    An innovative heterogeneous transfer learning framework to enhance the scalability of deep reinforcement learning controllers in buildings with integrated energy systems

    Davide CoraciSilvio BrandiTianzhen HongAlfonso Capozzoli...
    739-770页
    查看更多>>摘要：Deep Reinforcement Learning(DRL)-based control shows enhanced performance in the management of integrated energy systems when compared with Rule-Based Controllers(RBCs),but it still lacks scalability and generalisation due to the necessity of using tailored models for the training process.Transfer Learning(TL)is a potential solution to address this limitation.However,existing TL applications in building control have been mostly tested among buildings with similar features,not addressing the need to scale up advanced control in real-world scenarios with diverse energy systems.This paper assesses the performance of an online heterogeneous TL strategy,comparing it with RBC and offline and online DRL controllers in a simulation setup using EnergyPlus and Python.The study tests the transfer in both transductive and inductive settings of a DRL policy designed to manage a chiller coupled with a Thermal Energy Storage(TES).The control policy is pre-trained on a source building and transferred to various target buildings characterised by an integrated energy system including photovoltaic and battery energy storage systems,different building envelope features,occupancy schedule and boundary conditions(e.g.,weather and price signal).The TL approach incorporates model slicing,imitation learning and fine-tuning to handle diverse state spaces and reward functions between source and target buildings.Results show that the proposed methodology leads to a reduction of 10％in electricity cost and between 10％and 40％in the mean value of the daily average temperature violation rate compared to RBC and online DRL controllers.Moreover,online TL maximises self-sufficiency and self-consumption by 9％and 11％with respect to RBC.Conversely,online TL achieves worse performance compared to offline DRL in either transductive or inductive settings.However,offline Deep Reinforcement Learning(DRL)agents should be trained at least for 15 episodes to reach the same level of performance as the online TL.Therefore,the proposed online TL methodology is effective,completely model-free and it can be directly implemented in real buildings with satisfying performance.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    Evaluation of transmission risk of respiratory particles under different ventilation strategies in an elevator

    Liangyu ZhuXian LiBujin FengFan Liu...
    771-784页
    查看更多>>摘要：People in elevators are at risk of respiratory infection because the elevator cabin is crowded and has poor ventilation.The exhaled particles may be inhaled by the susceptible person,deposited on the surface and suspended in the elevator,which can result in direct and indirect transmission.However,whether the air vent designs adopted in the elevator can effectively reduce the transmission risk of respiratory particles remains unknown.In this study,the dispersion of particles under four common ventilation strategies used in the commercial elevator was investigated by proven computational fluid dynamics(CFD)simulations.The flow field was simulated with the RNG k-e turbulence model and the Lagrangian method was adopted to track particle trajectories.The effects of air vent layout and airflow rate on particle transmission were analyzed.We found that more than 50％of exhaled particles(average value)were suspended in the cabin and difficult to discharge under the investigated ventilation strategies.The deposited fraction of particles on the susceptible person reached up to 39.14％for infiltration ventilation,which led to a high risk of contact infection.Increasing the ventilation rate could not significantly reduce the inhalation proportion of particles due to the poor airflow distribution inside the elevator.A more proper ventilation strategy should be explored for the elevator to control transmission risk.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    Study on the performance of lightweight roadway wall thermal insulation coating containing EP-GHB mixed ceramsite

    Yongliang ZhangShili YinHongwei MuXilong Zhang...
    785-798页
    查看更多>>摘要：As the mining depth increases,the problem of high-temperature thermal damage mainly caused by heat dissipation of surrounding rock is becoming more and more obvious.It is very important to solve the environmental problem of mine heat damage to improve the efficiency of mineral resource exploitation and protect the physical and mental health of workers.One can apply thermal insulation coating on the walls of mine roadways as a means of implementing active heat insulation.In this paper,expanded perlite(EP)and glazed hollow bead(GHB)are used as the main thermal insulation materials,ceramsite and sand as aggregate,plus glass fiber and sodium dodecyl sulfate to develop a new lightweight composite thermal insulation coating through orthogonal experiment method.According to the plate heat flow meter method and mechanical test method,the thermal insulation and mechanical properties of EP-GHB mixed ceramsite coating were studied by making specimens with different parameter ratios,and according to the analysis of the experimental results,the optimal mix ratio of the coating was selected.In addition,Fluent numerical simulation software was used to establish the roadway model,and the thermal insulation effect of the coating in the roadway under different working conditions was studied.The results show that the thermal conductivity of the prepared composite thermal insulation coating material is only 8.5％of that of ordinary cement mortar,and the optimal thickness of adding thermal insulation coating is 0.2 m,which can reduce the outlet air temperature of the roadway with a length of 1000 m by 4.87 K at this thickness.The thermal insulation coating developed in this study has the advantages of simple technology and strong practicability,and has certain popularization and application value in mine heat damage control.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    Identification of rural courtyards'utilization status using deep learning and machine learning methods on unmanned aerial vehicle images in north China

    Maojun WangWenyu XuGuangzhong CaoTao Liu...
    799-818页
    查看更多>>摘要：The issue of unoccupied or abandoned homesteads(courtyards)in China emerges given the increasing aging population,rapid urbanization and massive rural-urban migration.From the aspect of rural vitalization,land-use planning,and policy making,determining the number of unoccupied courtyards is important.Field and questionnaire-based surveys were currently the main approaches,but these traditional methods were often expensive and laborious.A new workflow is explored using deep learning and machine learning algorithms on unmanned aerial vehicle(UAV)images.Initially,features of the built environment were extracted using deep learning to evaluate the courtyard management,including extracting complete or collapsed farmhouses by Alexnet,detecting solar water heaters by YOLOv5s,calculating green looking ratio(GLR)by FCN.Their precisions exceeded 98％.Then,seven machine learning algorithms(Adaboost,binomial logistic regression,neural network,random forest,support vector machine,decision trees,and XGBoost algorithms)were applied to identify the rural courtyards'utilization status.The Adaboost algorithm showed the best performance with the comprehensive consideration of most metrics(Accuracy:0.933,Precision:0.932,Recall:0.984,F1-score:0.957).Results showed that identifying the courtyards'utilization statuses based on the courtyard built environment is feasible.It is transferable and cost-effective for large-scale village surveys,and may contribute to the intensive and sustainable approach to rural land use.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    Utilizing interpretable stacking ensemble learning and NSGA-Ⅲ for the prediction and optimisation of building photo-thermal environment and energy consumption

    Yeqin ShenYubing HuKai ChengHainan Yan...
    819-838页
    查看更多>>摘要：This study develops an approach consisting of a stacking model integrated with a multi-objective optimisation algorithm aimed at predicting and optimising the ecological performance of buildings.The integrated model consists of five base models and a meta-model,which significantly improves the prediction performance.Specifically,the R2 value was improved by 9.19％and the error metrics MAE,MSE,MAPE,and CVRMSE were reduced by 69.47％,79.88％,67.32％,and 57.02％,respectively,compared to the single prediction model.According to the research on interpretable machine learning,adding the SHAP value gives us a deeper understanding of the impact of each architectural design parameter on the performance.In the multi-objective optimisation part,we used the NSGA-Ⅲ algorithm to successfully improve the energy efficiency,daylight utilisation and thermal comfort of the building.Specifically,the optimal design solution reduces the energy use intensity by 31.6 kWh/m2,improves the useful daylight index by 39％,and modulated the thermal comfort index,resulting in a decrement of 0.69 ℃ for the summer season and an enhancement of 0.64 ℃ for the winter season,respectively.Overall,this study provides building designers and decision makers with a tool to make better design decisions at an early stage to achieve a better combination of energy efficiency,daylight utilisation and thermal comfort optimisation in an integrated manner,providing an important support for achieving sustainable building design.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    Predicting the clothing insulation through machine learning algorithms:A comparative analysis and a practical approach

    Pablo Aparicio-RuizElena Barbadilla-MartínJosé GuadixJesús Mu?uzuri...
    839-855页
    查看更多>>摘要：Since indoor clothing insulation is a key element in thermal comfort models,the aim of the present study is proposing an approach for predicting it,which could assist the occupants of a building in terms of recommendations regarding their ensemble.For that,a systematic analysis of input variables is exposed,and 13 regression and 12 classification machine learning algorithms were developed and compared.The results are based on data from 3352 questionnaires and 21 input variables from a field study in mixed-mode office buildings in Spain.Outdoor temperature at 6 a.m.,indoor air temperature,indoor relative humidity,comfort temperature and gender were the most relevant features for predicting clothing insulation.When comparing machine learning algorithms,decision tree-based algorithms with Boosting techniques achieved the best performance.The proposed model provides an efficient method for forecasting the clothing insulation level and its application would entail optimising thermal comfort and energy efficiency.
      原文链接:
    • NETL
    • NSTL
    • 万方数据