Energy and Buildings最新文献

{"title":"Artificial intelligence for improving thermal comfort through envelope design in residential buildings: Recent developments and future directions","authors":"Arda Bayraktar,&nbsp;Berk Ekici","doi":"10.1016/j.enbuild.2026.117007","DOIUrl":"10.1016/j.enbuild.2026.117007","url":null,"abstract":"<div><div>Envelopes are vital components for improving thermal comfort in almost all building typologies. Yet, the design and analysis of envelopes are complex, as they involve multiple aspects and various parameters, ensuring comfort standards. Improving thermal comfort in residential buildings is within the scope of researchers to suggest sustainable design alternatives that consider multiple performance aspects and design parameters. Previous review articles have focused on improving thermal performance in residential buildings from the perspective of envelope technology, materials, and design strategies. However, none of them investigated current developments using artificial intelligence (AI), which inevitably supports decision-making in complex circumstances for a sustainable built environment. This review examines the contribution of AI methods, which consist of metaheuristic optimization and machine learning algorithms as sub-branches, to envelope parameters. The paper systematically reviews 95 relevant works on AI, including early approaches, to provide a comprehensive overview of current developments, following PRISMA guidelines. The results showed that early applications considered conventional approaches to improve thermal comfort and energy performance, which mostly limit the results to specified cases. On the other hand, studies utilizing AI methods dealt with numerous parameters, allowing them to cope with complex envelope systems in a reasonable amount of time. The study addresses relevant research questions related to the trends, research methods, system types, AI methods, data types, and their relation to performance and envelope parameters. The study also provides actionable insight, underlining gaps and future works for utilizing machine learning methods in the reviewed research domain.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117007"},"PeriodicalIF":7.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145962618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploration of key design questions in rule-based shading control for building energy load reduction","authors":"Jihyeon Cho ,&nbsp;Suyeon Bang ,&nbsp;Hoseong Lee ,&nbsp;Yeonsook Heo","doi":"10.1016/j.enbuild.2026.117038","DOIUrl":"10.1016/j.enbuild.2026.117038","url":null,"abstract":"<div><div>Automated shading can curb building energy use; however, its performance depends on the control strategy design. This study quantified how state variable, dynamicity level, and adaptation horizon shape end-use savings and visual comfort. and formulated static and dynamic rule-based strategies for exterior slat-type blinds that track the solar horizontal profile angle (SHPA). Both schemes were tuned monthly or annually. The candidate state variables include direct solar irradiance, outdoor temperature, and indoor temperature. We simulated the total energy loads (cooling, heating, and lighting) and operational behavior (mode duration and switching frequency) for the perimeter zones across the orientations. Monthly optimized static control achieved 39–41% total-load savings vs. a no-control baseline and outperformed a fixed 90° reference with dimming (∼29%). Dynamic SHPA tracking offers marginal energy gains (&lt;≈1 percentage point) but incurs orders-of-magnitude higher switching frequency. Hourly analysis showed that neither the slat modulation level nor state variable choice produced load savings because the shaded areas controlled by the strategies were not significantly different. Monthly tuning outperformed annual optimized cases by ∼ 12 percentage points, reflecting seasonal non-stationarity in sun geometry and weather. This indicates that the adaptation time interval is critical in rule-based shading control. Finally, visual comfort of static controls remained acceptable; hours with visual discomfort (daylight glare index &gt; 22) were &lt; 3% in every zone. Overall, these findings provide a practical guideline: use optimized static rules as the default approach and select the adaptation timescale according to local climate variability, while aligning shading system design with the proposed control framework.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117038"},"PeriodicalIF":7.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Linear temperature model for rapid prediction of multi-scale urban thermal environments toward climate-resilient city design","authors":"Chen Ren ,&nbsp;Hao-Cheng Zhu ,&nbsp;Shi-Jie Cao","doi":"10.1016/j.enbuild.2026.117117","DOIUrl":"10.1016/j.enbuild.2026.117117","url":null,"abstract":"<div><div>Urban heat island (UHI) effects, intensified by global climate change and rapid urbanization, significantly elevate urban air temperatures. This phenomenon leads to substantial increases in building energy consumption, aggravated urban carbon emission, and raised public health risks under extreme heat. Facing these multifaceted challenges, there is a pressing need to develop accurate and efficient tools for predicting non-uniform urban thermal environment to support the UHI mitigation towards climate-resilient city design. High-resolution computational fluid dynamics (CFD) required large expenses for practical application, prompting the exploration of alternative approach such as machine learning. Nevertheless, these data-driven methods still encounter challenge in extensive training data requirement and limited physical interpretability. To address these gaps, this study develops a linear temperature model (LTM) to rapidly predict urban temperature distributions by linear superposition of pre-computed thermal contributions from heat sources. The prediction model is systematically validated across three representative test cases at different urban scales, i.e., an isolated building, a neighborhood, and a real street block. Results showed that by largely improving computational efficiency, the LTM maintained high prediction accuracy in terms of mean absolute error (MAE), root mean square error (RMSE), and mean relative error (MRE). Compared to CFD simulations, the LTM achieved good prediction precision at the building scale (MAE = 0.11°C, RMSE = 0.22°C, MRE = 0.28%), the neighborhood scale (MAE = 0.12°C, RMSE = 0.17°C, MRE = 0.31%), and the street block scale (MAE = 0.29°C, RMSE = 0.41°C, MRE = 0.84%). This model can provide urban planners, designers as well as policymakers with a practical tool for rapid thermal impact assessment, thereby guiding the development of UHI mitigation and climate-resilient design strategies.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117117"},"PeriodicalIF":7.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146186341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligence-based prediction of coefficient of performance for a novel high-temperature industrial heat pump: Comparative performance of ANN and ANFIS models","authors":"Iman Golpour ,&nbsp;José Daniel Marcos ,&nbsp;Rubén Barbero ,&nbsp;Antonio Rovira ,&nbsp;Alex Butean ,&nbsp;Arne Høeg","doi":"10.1016/j.enbuild.2026.117078","DOIUrl":"10.1016/j.enbuild.2026.117078","url":null,"abstract":"<div><div>This study presents a comparative evaluation of the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) approaches for predicting the coefficient of performance (COP) of the HoegTemp, a high-temperature heat pump (HTHP) based on a Stirling cycle (SC) with a design heat capacity of 400 kW. Experimental tests were conducted at the IVAR biogas facility in Stavanger, Norway. This study employed a feedforward backpropagation neural network (FFBPNN) model with one and two hidden layers, with various numbers of neurons and three activation functions, as well as the ANFIS approach, to estimate the COP of the SC-HTHP. The FFBPNN model used the Levenberg-Marquardt (LM) and Bayesian regularization (BR) training algorithms, while the ANFIS model utilized a hybrid optimization method and grid partitioning. The ANN and ANFIS models were evaluated using the following input variables: temperature ratio (1.4–1.6 K/K), average source temperature (21–22 °C), average sink temperature (139–199 °C) and hot water inlet temperature (137–197 °C), with COP as the output variable. The results demonstrated that the FFBP-ANN model exhibited superior predictive accuracy compared to the ANFIS model, achieving R² = 0.9999, MSE = 0.00010, MAE = 0.00804, and RMSE = 0.01000, whereas the ANFIS approach resulted in R² = 0.9863, MSE = 0.00019, MAE = 0.01114, and RMSE = 0.01392. The optimal ANN topology was 4–23-16–1 with tansig–logsig–purelin activation functions. In contrast, the best membership functions selected for ANFIS were Gaussian for the input layer and constant for the output layer.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117078"},"PeriodicalIF":7.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146089314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized lime-based renders with Phase Changing Materials (PCMs) for energy-efficient and climate resilient traditional and contemporary structures","authors":"Magdalini Theodoridou ,&nbsp;Loucas Kyriakou ,&nbsp;Ioannis Ioannou","doi":"10.1016/j.enbuild.2026.117069","DOIUrl":"10.1016/j.enbuild.2026.117069","url":null,"abstract":"<div><div>Improving building energy efficiency is a top priority for the European Union. Member States take measures to encourage property owners to enhance thermal performance and reduce heating and cooling demands. Conventional thermal insulating materials, such as expanded or extruded polystyrene, remain widely used but present drawbacks, such as thermal bridging, imposition of additional loads on the structures and increased building envelope thickness. Hence, alternative solutions are continuously being sought. This experimental study focuses on developing innovative, smart, cementless renders, enhanced with Phase Changing Materials (PCMs), tailored for traditional and contemporary structures in southern Europe. Following a parametric analysis, a series of PCM-enhanced lime composites were designed and assessed for their thermal and physico-mechanical properties. Accelerated weathering tests were also carried out to investigate the durability of the new end-products against salt crystallization, which is critical for restoration and renovation projects. The addition of PCMs at 5% w/w of the binder and aggregates resulted in the optimum performance, considering the experimentally determined thermal, physico-mechanical and durability properties of the end-products. The results confirm that PCMs indeed have the potential to enhance the thermal efficiency of lime-based composites, while careful mix design may allow for tailored selection of end-products based on specific needs. Future market adoption of PCM-enhanced renders is expected to contribute to lower energy consumption, supporting the EU’s strategic goals for energy-efficient and climate-resilient structures.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117069"},"PeriodicalIF":7.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146089313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neighborhood-level analysis of building use and socio-economic effects on urban energy consumption","authors":"Jihyun Kang,&nbsp;Chaerin Kim,&nbsp;Albert Tonghoon Han","doi":"10.1016/j.enbuild.2026.117084","DOIUrl":"10.1016/j.enbuild.2026.117084","url":null,"abstract":"<div><div>Urban energy consumption is unevenly distributed across neighborhoods, yet most studies in South Korea remain focused on national or metropolitan scales. This study examines neighborhood-level (<em>dong</em>-level) energy demand in Seoul by integrating building characteristics, socio-economic indicators, and infrastructural access. Using multivariate regression models for gas, electricity, and total energy use across 426 neighborhoods, we identify localized drivers of urban energy demand. Results show that apartments and townhouses are consistently associated with higher energy usage, while commercial and educational facilities such as cafés, restaurants, tutoring centers, and universities substantially increase electricity demand. Socio-economic variables including income, worker density, household size, and demographic dependency ratios produce varying effects, with lower consumption in some aging or dependent populations reflecting energy deprivation rather than efficiency. District heating access demonstrates a strong negative association with neighborhood gas use, underscoring the role of centralized infrastructure. These findings reveal how built environment, social structure, and energy infrastructure jointly shape intra-urban disparities in energy demand. We argue for neighborhood-scale, equity-sensitive energy governance as a complement to city-level policies.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117084"},"PeriodicalIF":7.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146098302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new approach for HEMS to optimize cost, emissions and comfort through a smart integration of V2G, load scheduling, and PVT generation","authors":"Rodrigo Fiorotti ,&nbsp;Jussara F. Fardin ,&nbsp;Helder R.O. Rocha ,&nbsp;Augusto C. Rueda-Medina ,&nbsp;Antonio M. Pantaleo ,&nbsp;Mohammad R. Nasab ,&nbsp;Sergio Bruno","doi":"10.1016/j.enbuild.2026.117094","DOIUrl":"10.1016/j.enbuild.2026.117094","url":null,"abstract":"<div><div>This paper introduces a new Home Energy Management System (HEMS) strategy designed for Smart Homes that performs load scheduling and contains Photovoltaic/Thermal (PVT) generation and Electric Vehicle (EV) using two approaches: one designed for mono-directional EV charger and the other for bi-directional one, which can perform vehicle-to-grid (V2G). The problem is solved using the Non-dominated Sorting Genetic Algorithm III metaheuristic and Long Short-Term Memory to predict the PVT generation. The occupant behavior applied utilizes 3 feature parameters to define the usage profile of controllable loads and determine the periods in which the user is most adapted to using the equipment to quantify their comfort, in addition to modeling the dependency between the operation of some loads. The electrical and thermal loads are categorized into non-controllable, deferrable, and thermo-controllable. An annual case study shows an average cost reduction of 14.57% achieved by leveraging the flexibility of the bidirectional charger for similar values of emissions and user comfort. This reduction occurs by exploiting time-of-use tariffs (which lead to an average savings of 22.23%) and reducing the maximum demand (resulting in an average reduction of 21.73%). These savings are sufficient to offset the increase in battery losses and degradation costs to perform V2G. Finally, the comparison of various HEMS architectures highlights the advantages of EV adoption through V2G implementation, positioning EVs as a more competitive solution for promoting clean and affordable energy in residential buildings.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117094"},"PeriodicalIF":7.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146098304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Future building cooling energy demand under climate change in the Mediterranean region","authors":"Lei Zhu ,&nbsp;Lingye Leng ,&nbsp;Yong Huang ,&nbsp;Sun Lei","doi":"10.1016/j.enbuild.2026.117058","DOIUrl":"10.1016/j.enbuild.2026.117058","url":null,"abstract":"<div><div>The objective of the research team in the current study was to establish the presence and the percentage change in cooling energy usage in Italian residential buildings in relation to climate change. It is a specific intervention to establish cooling demands, as it analyses historical trends and projections of Cooling Degree Hours (CDHs). The study will also focus on identifying high-priority areas for energy actions by comparing population patterns with CDHs. Based on hourly outdoor temperature predictions from the Weather Research and Forecasting (WRF) Model, both CDHs for the cooling seasons between 2002 and 2021 and for 2050 and 2080, according to the IPCC predictions, were calculated. Due to the Mediterranean region’s geographic vulnerability and Italy’s diverse climate, Italy was selected as a case study. The data on CDH were clustered into 4 5-year intervals for analytical convenience. The median CDHs have increased twice between 2012 and 2016 and four times between 2017 and 2020; nevertheless, the levels remained unchanged between 2002 and 2011. Following that, it increased exponentially. It is projected to rise the most in 2080 under the RCP8.5 scenario, with the highest concentrations found in urban areas, the Po Valley, and the southern coastal regions. These findings highlight the importance of modernizing Italy’s building stock to make buildings more resilient and efficient, as well as the anticipated significant growth in cooling requirements in buildings in the future.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117058"},"PeriodicalIF":7.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An IoT-integrated stochastic cloud theory framework for carbon-restricted smart building energy management within the context of next-generation energy performance certificates","authors":"Biao Wan,&nbsp;Dinghua He","doi":"10.1016/j.enbuild.2026.117008","DOIUrl":"10.1016/j.enbuild.2026.117008","url":null,"abstract":"<div><div>The transition toward Next-Generation Energy Performance Certificates (EPCs) has created new opportunities for integrating digitalization and intelligent control in smart buildings. In this context, the rapid development of smart building technologies and the Internet of Things (IoT) has intensified the demand for advanced energy management frameworks capable of balancing energy efficiency, economic performance, and environmental sustainability. This study proposes a stochastic carbon-restricted energy management framework for IoT-integrated smart buildings that combine renewable energy sources (RES), combined heat and power (CHP) units, auxiliary boilers, electric vehicles (EVs), and hybrid electrical–thermal energy storage systems. The model aims to optimize the trade-off among cost, carbon reduction, and occupant comfort under emission-constrained operation. To accurately capture the uncertainties in renewable generation, load demand, and electricity price, a Cloud Theory-based uncertainty modeling approach is adopted, enabling the simultaneous representation of randomness and fuzziness in system parameters. The optimization process employs the Boxelder Bug Search Optimization (BBSO) algorithm, which offers strong global exploration and fast convergence. IoT-based monitoring and data interoperability enhance real-time coordination among distributed resources, contributing to the digital transformation envisioned in EPC frameworks. Three operational scenarios are investigated: deterministic operation without carbon limits, deterministic operation under carbon limits, and stochastic operation under Cloud Theory-based uncertainty. All simulations are conducted over a representative two-day (48-hour) winter period with an hourly time resolution to ensure consistent temporal modeling across all scenarios. Results indicate that carbon restriction increases total operating cost by 17.3%, while stochastic modeling adds an additional 4.6% due to risk buffering. Meanwhile, key EPC-relevant indicators show that carbon intensity and primary energy consumption increase moderately, while the self-sufficiency ratio decreases from 61.93% to 55.95%, revealing a clear cost–resilience trade-off beyond static EPC metrics. Nevertheless, the proposed strategy reduces grid dependency by 5.8%, improves renewable utilization by 6.4%, and ensures full occupant comfort across all cases. Comparative benchmarking confirms the superiority of the proposed BBSO over other established optimization techniques, achieving up to a 5.14% reduction in operational cost while maintaining high solution robustness. These findings demonstrate that the IoT-integrated and Cloud Theory-based optimization approach provides a robust pathway toward data-driven, low-carbon, and EPC-aligned smart building management systems.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117008"},"PeriodicalIF":7.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145995602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Value of probabilistic spatiotemporal urban temperature forecasts for building energy purchases in day-ahead market","authors":"Byeongseong Choi ,&nbsp;Mario Bergés ,&nbsp;Matteo Pozzi","doi":"10.1016/j.enbuild.2026.116961","DOIUrl":"10.1016/j.enbuild.2026.116961","url":null,"abstract":"<div><div>Urban heat significantly affects human comfort and increases energy consumption for space cooling in built environments. Adaptive decision-making regarding space cooling can reduce the economic cost of a building. In particular, accurate weather forecasts enable operators to predict building energy consumption more precisely and make informed decisions. In this paper, we assess the impact of urban weather forecasts on an energy-related decision in a building using Value of Information (VoI) analysis, a framework to rigorously quantify how reducing uncertainty propagates to informed decisions. To achieve this, we integrate two models: (a) a probabilistic spatiotemporal model for temperature forecasting and (b) a probabilistic model for forecasting the power consumption of a building. By coupling these two models, we quantify the propagating uncertainty from temperature forecasts to load forecasts and assess its influence on adaptive energy purchasing strategy in an energy market, specifically in the day-ahead market, which sets electricity prices based on purchasing timing. Our investigation shows that improved temperature forecasts can reduce energy costs by up to 38.9 % in commercial buildings and up to 8.2 % in residential buildings, compared to a scenario that does not incorporate temperature forecasts, while the degree of savings varies significantly depending on the building types. The main contribution of this study is to propose a VoI-based framework that quantifies the benefit of reducing uncertainty in building-environment interactive systems, demonstrated through the practical application of uncertainty-aware urban temperature forecasts for optimizing energy decisions in buildings.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 116961"},"PeriodicalIF":7.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145995563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}