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An integrated framework for automatic green building evaluation:A case study of China

Frontiers of Engineering Management 2024年第2期11卷 269-287页

作者： Qiufeng HE Zezhou WU Xiangsheng CHEN State Key Laboratory of Intelligent Geotechnics and Tunnelling Shenzhen 518060China Key Laboratory for Resilient Infrastructures of Coastal Cities(Shenzhen University) Ministry of EducationShenzhen 518060China sino-australia joint research Centre in bim and smart construction Shenzhen UniversityShenzhen 518060China Shenzhen Key Laboratory of Green Efficient and Intelligent Construction of Underground Metro StationShenzhen 518060China

With the burgeoning emphasis on sustainable construction practices in China,the demand for green building assessment has significantly *** overall evaluation process comprises two key components:The acquisition of evaluation data and the evaluation of green scores,both of which entail considerable time and *** research predominantly concentrated on automating the latter process,often neglecting the exploration of automating the former in accordance with the Chinese green building assessment ***,there is a pressing requirement for more streamlined management of structured standard knowledge to facilitate broader *** response to these challenges,this paper presents a conceptual framework that integrates building information modeling,ontology,and web map services to augment the efficiency of the overall evaluation process and the management of standard *** specifically,in accordance with the Assessment Standard for Green Building(GB/T 50378-2019)in China,this study innovatively employs visual programming software,Dynamo in Autodesk Revit,and the application programming interface of web map services to expedite the acquisition of essential architectural data and geographic information for green building ***,ontology technology is harnessed to visualize the management of standard knowledge related to green building assessment and to enable the derivation of green scores through logical ***,a residential building is employed as a case study to validate the theoretical and technical feasibility of the developed automated evaluation conceptual framework for green *** research findings hold valuable utility in providing a self-assessment method for applicants in the field.

关键词： automatic evaluation green building bim web map service ontology inference application

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Novel machine learning paradigms-enabled methods for smart building operations in data-challenging contexts:Progress and perspectives

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National Science Open 2024年第3期3卷 88-109页

作者： Cheng Fan Yutian Lei Jinhan Mo Huilong Wang Qiuting Wu Jiena Cai Key Laboratory for Resilient Infrastructures of Coastal Cities Ministry of EducationShenzhen University sino-australia joint research center in bim and smart construction Shenzhen University College of Civil and Transportation Engineering Shenzhen University

The increasing availability of building operational data has greatly encouraged the development of advanced data-driven technologies for smart building *** operational data typically suffer from data quality problems,such as insufficient labeled and imbalanced data,making them incompatible with conventional machine learning *** advances in data science have provided novel machine learning paradigms to tackle such data challenges for practical applications,such as transfer learning,semi-supervised learning,and generative *** review aims to present the progress and perspectives on the effective utilization of novel machine learning paradigms for three major building energy management tasks,i.e.,building energy predictions,fault detection and diagnosis,and control ***-depth discussions have been provided to demonstrate the pros and cons of different learning approaches in terms of data compatibility,modeling difficulties,and possible application scenarios,which could be helpful for enhancing the feasibility of data-driven technologies for smart building operations.

关键词： smart building operations building energy management transfer learning semi-supervised learning generative learning

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Problem identification and revitalization strategies for the recovery and reconstruction of traditional villages in the Ms 6.8 Luding Earthquake

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Journal of Mountain Science 2024年第2期21卷 361-379页

作者： HUANG Chao QIU Jian LIU Chun JIANG Rui ZHAO Chuanrong ZHANG Yi School of Architecture and research center of Space Planning and Engineering Design Southwest Jiaotong UniversityChengdu 611756China Sichuan Provincial Rural construction and Development center Chengdu 610041China Sichuan Institute of Urban and Rural construction Chengdu 610043China China Association for Promoting Democracy(CAPD)Sichuan Provincial Committee Chengdu 610016China Sichuan Provincial Architectural Design and research Institute(SADI) Chengdu 610095China

Post-disaster reconstruction is a topic of global concern,and traditional villages have special heritage attributes and need to face more requirements and obstacles in post-disaster *** paper summarizes four concepts based on the research on post-disaster reconstruction both domestically and internationally,as well as the recovery and reconstruction of cultural *** a field survey of traditional villages in the Ms 6.8 Luding earthquake-stricken area,it is found that there are problems such as insufficient awareness of heritage value,misalignment of scientific reconstruction technology,and insufficient protection of reconstruction elements during the reconstruction *** villages face the risk of declining or even loss of heritage *** order to effectively protect traditional villages and inherit the carrier of regional culture,four targeted reconstruction response strategies are proposed,i.e.,to"establish special planning for traditional village preservation","emphasize recovery of the authenticity of village heritage","ensure elements for village heritage recovery"and"promote the activation and utilization of village heritage",based on the problems discovered during the survey and the four concepts summarized in the research on post-disaster reconstruction of traditional *** research results hope to provide useful reference for ancient cultural areas affected by earthquakes on how to protect cultural heritage during the post-disaster reconstruction process.

关键词： Seismic hazard Decision making Traditional village Cultural heritage protection Post-earthquake recovery and reconstruction Revitalization strategy

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基于贝叶斯自优化Bi-LSTM组合网络的高速铁路轨道-桥梁系统震后响应预测方法

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Journal of Central South University 2024年第3期31卷 965-975页

作者：彭康蒋丽忠周旺保余建向平吴凌旭 National Engineering research center of High-speed Railway construction Technology Central South UniversityChangsha 410075China

中国高速铁路(HSR)规划建设逐渐向地震易发地区延伸,亟需一种及时、准确的灾后地震响应快速预测方法,以实现高速铁路系统运输生命线安全的快速评估。本文提出了一种基于贝叶斯自优化双向长短期记忆(Bi-LSTM)网络的快速预测方法,以经过... 详细信息

中国高速铁路(HSR)规划建设逐渐向地震易发地区延伸,亟需一种及时、准确的灾后地震响应快速预测方法,以实现高速铁路系统运输生命线安全的快速评估。本文提出了一种基于贝叶斯自优化双向长短期记忆(Bi-LSTM)网络的快速预测方法,以经过实验验证的高速铁路轨道-桥梁系统有限元模型地震动响应计算数据为样本,将预测地震响应和有限元计算结果进行比较,验证所提方法的精度和鲁棒性,表明该方法在预测高速铁路桥梁结构的非线性地震反应方面是有效的,且高速铁路轨道-桥梁系统的不同预测位置对预测精度的影响不明显;此外,为了降低神经网络训练数据量需求,提出了一种基于离散小波分解的分层聚类算法,结果表明,基于小波分解的分层聚类方法在保证预测精度的同时,有效地减少了训练地震集的输入数量。

关键词：高速铁路轨道-桥梁系统贝叶斯优化 Bi-LSTM神经网络离散小波分解聚类分析

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A method for support stiffness failure identification in a steel spring floating slab track of urban railway: a case study in China

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2024年第3期25卷 206-222页

作者： Junyuan ZHENG Caiyou ZHAO Duojia SHI Ping WANG Jian WANG Bolong JIANG Xi SHENG Key Laboratoryof High-speed Railway Engineering Ministry of EducationSouthwest Jiaotong UniversityChengdu 60031China School of Civil Engineering Southwest Jiaotong UniversityChengdu 610031China 01 DYNA(Qingdao)Test&Data Co. Ltd.Qingdao 266000China Rail Transit Digital construction and Measurement Technology National Engineering research center China Railway Design CorporationTianjin300308China Institute of Urban smart Transportation&Safety Maintenance Shenzhen UniversityShenzhen 518060China

The extensive use of steel spring floating slab tracks has effectively addressed the challenge of alleviating the environmental vibrations induced by urban rail transit ***,under the combined action of train dynamic loads and complex environmental factors,problems,such as the fracture of steel spring vibration isolators and suspension vibrations induced by the uneven settlement of the base,often *** failure of isolator support stiffness is often hidden in its early stages and is challenging to identify by conventional detection *** the same time,it will aggravate the wheel-rail interaction,accelerate the deterioration of track structure,and even affect the driving *** study first establishes a detailed coupled train-floating slab track-foundation analytical *** the influence of the vibration isolator support stiffness failure on the dynamic indices of the floating slab track system response is analyzed.A set of defect identification methods that can detect the number of failed steel springs,severity of damage,and their location is ***,an intelligent monitoring system for support stiffness of floating slab track is built by combining the density-based spatial clustering of applications with noise algorithm and statistical data analysis and is applied to a rail line in southern *** a three-year monitoring campaign,a suspension failure and a fracture of a steel spring were each successfully detected and detailed failure information was *** investigation results were consistent with the damage identification *** repair,the track structure dynamic response returned to the average pre-damage level and further deterioration had been *** proposed damage identification methods and monitoring system provide an approach for intelligent identification of track structure support stiffness failures.

关键词： Floating slab track Support stiffness Detailed analytical model Failure identification Monitoring system

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A novel deep generative modeling-based data augmentation strategy for improving short-term building energy predictions

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Building Simulation 2022年第2期15卷 197-211页

作者： Cheng Fan Meiling Chen Rui Tang Jiayuan Wang Key Laboratory for Resilient Infrastructures of Coastal Cities(Shenzhen University) Ministry of EducationChina sino-australia joint research center in bim and smart construction Shenzhen UniversityShenzhenChina Building Technology&Urban Systems Division Lawrence Berkeley National LaboratoryUSA

Short-term building energy predictions serve as one of the fundamental tasks in building operation *** large numbers of studies have explored the value of various supervised machine learning techniques in energy predictions,few studies have addressed the potential data shortage problem in developing data-driven *** promising solution is data augmentation,which aims to enrich existing building data resources for reliable predictive *** study proposes a deep generative modeling-based data augmentation strategy for improving short-term building energy *** types of conditional variational autoencoders have been designed for synthetic energy data generation using fully connected and one-dimensional convolutional layers *** experiments have been designed to evaluate the value of data augmentation using actual measurements from 52 *** results indicate that conditional variational autoencoders are capable of generating high-quality synthetic data samples,which in turns helps to enhance the accuracy in short-term building energy *** average performance enhancement ratios in terms of CV-RMSE range between 12%and 18%.Practical guidelines have been obtained to ensure the validity and quality of synthetic building energy *** research outcomes are valuable for enhancing the robustness and reliability of data-driven models for smart building operation management.

关键词： building energy predictions data augmentation data-driven models generative modeling variational autoencoders

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Intrinsic pentagon defect engineering in multiple spatial-scale carbon frameworks for efficient triiodide reduction

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Journal of Energy Chemistry 2024年第8期95卷 20-28,I0002页

作者： Siyi Hou Xuedan Song Chang Yu Jiangwei Chang Yiwang Ding Yingbin Liu Xiubo Zhang Weizhe Liu Jieshan Qiu State Key Lab of Fine Chemicals Liaoning Key Lab for Energy Materials and Chemical EngineeringFrontier Science Center for Smart MaterialsSchool of Chemical EngineeringDalian University of TechnologyDalian 116024LiaoningChina Liaoning Key Lab for Energy Materials and Chemical Engineering Frontier Science Center for Smart MaterialsSchool of ChemistryDalian University of TechnologyDalian 116024LiaoningChina State Key Laboratory of Chemical Resource Engineering College of Chemical EngineeringBeijing University of Chemical TechnologyBeijing 100029China

Intrinsic topological defect engineering has been proven as a promising strategy to elevate the electrocatalytic activity of carbon ***,the controllable construction of high-density and specific topological defects in carbon frameworks to reveal the relationship between reactivity and defect structure remains a challenging ***,the intrinsic pentagon carbon sites that can favor electron overflow and enhance their binding affinity towards the intermediates of catalytic reaction are firstly presented by the work function and the p-band center *** experimentally verify this,the cage-opening reaction of fullerene is proposed and utilized for synthesizing carbon quantum dots with specific pentagon configuration(CQDs-P),subsequently utilizing CQDs-P to modulate the micro-scale defect density of three-dimensional reduced graphene oxide(rGO)viaπ-π*** multiple spatial-scale rGO-conjugated CQDs-P structure simultaneously possesses abundant pentagon and edge defects as catalytic active sites and long-range-orderedπelectron delocalization system as conductive *** defects-rich CQDs-P/rGO-4 all-carbon-based catalyst exhibits superb catalytic activity for triiodide reduction reaction with a high photoelectric conversion efficiency of 8.40%,superior to the Pt reference(7.97%).Theoretical calculations suggest that pentagon defects in the carbon frameworks can promote charge transfer and modulate the adsorption/dissociation behavior of the reaction intermediates,thus enhancing the electrocatalytic activity of the *** work confirms the role of intrinsic pentagon defects in catalytic reactions and provides a new insight into the synthesis of defects-rich carbon catalysts.

关键词： Defect engineering Pentagon carbon Carbon quantum dots Electrocatalytic activity Triiodide reduction

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Advanced data analytics for building energy modeling and management

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Building Simulation 2021年第1期14卷 1-2页

作者： Cheng Fan Fu Xiao Da Yan sino-australia joint research center in bim and smart construction College of Civil and Transportation EngineeringShenzhen UniversityShenzhenChina Department of Building Services Engineering The Hong Kong Polytechnic UniversityHong KongChina Building Energy research center School of ArchitectureTsinghua UniversityBeijingChina

The data-driven approach has been widely adopted in fault detection and diagnosis and optimal control of building energy systems for improving building performance since the 1980s,owing to the increasing demands for c... 详细信息

关键词： energy convenient analytic

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Plasma membrane-anchored fluorescent tracker based on boron-dipyrromethene

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Chinese Journal of Chemical Engineering 2024年第8期72卷 220-225页

作者： Yucong Gong Xiangli Li Daqing Ma Lai Wang Lin Zhou Caiwei Lu Yi Xiao Xinfu Zhang State Key Laboratory of Fine Chemicals Frontiers Science Center for Smart Materials Oriented Chemical EngineeringDalian University of TechnologyDalian 116024China China Academy of Safety Science and Technology Beijing 100012China

The construction of a stable-membrane tracker has significant implications for the visualization of the membrane in live ***,most current plasma trackers are not suitable for tracking plasma membranes for a long time due to their limited retention ***,Mem580-F-Sulfo is designed to target and anchor cell membranes and therefore track cell membranes for a longer *** tracker is composed of a lipophilic boron-dipyrromethene(BODIPY)derivative and a hydrophilic zwitterion to form an amphiphilic structure,which enables its targeting ability toward cell ***,a reactive ester group is included to bind with proteins through covalent bonds in cell membranes nonspecifically,which extends retention time in cell ***580-F-Sulfo shows intense brightness(94600),with a high molar absorption coefficient of up to about 100000 L·mol^(-1)·cm^(-1)and a fluorescence quantum yield of up to *** shows fast cell membrane targeting ability and long retention up to 90 *** brief,this work has not only developed a tracker with good cell membrane targetability but also provided a new strategy for improving the targeting stability of cell membranes.

关键词： Plasma membrane Protein Tracker Imaging Microstructure

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Leveraging graph convolutional networks for semi-supervised fault diagnosis of HVAC systems in data-scarce contexts

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建筑模拟(英文) 2023年第8期16卷 1499-1517页

作者： Cheng Fan Yiwen Lin Marco Savino Piscitelli Roberto Chiosa Huilong Wang Alfonso Capozzoli Yuanyuan Ma Key Laboratory for Resilient Infrastructures of Coastal Cities Ministry of EducationShenzhen UniversityShenzhenChina sino-australia joint research center in bim and smart construction Shenzhen UniversityShenzhenChina College of Civil and Transportation Engineering Shenzhen UniversityShenzhenChina Department of Energy TEBE research groupBAEDA LabPolitecnico di TorinoTorinoItaly

The continuous accumulation of operational data has provided an ideal platform to devise and implement customized data analytics for smart HVAC fault detection and *** practice,the potentials of advanced supervised learning algorithms have not been fully realized due to the lack of sufficient labeled *** tackle such data challenges,this study proposes a graph neural network-based approach to effectively utilizing both labeled and unlabeled operational data for optimum *** specifically,a graph generation method is proposed to transform tabular building operational data into association graphs,based on which graph convolutions are performed to derive useful insights for fault *** experiments have been designed to evaluate the values of the methods *** datasets on HVAC air-side operations have been used to ensure the generalizability of results *** data scenarios,which vary in training data amounts and imbalance ratios,have been created to comprehensively quantify behavioral patterns of representative graph convolution networks and their *** research results indicate that graph neural networks can effectively leverage associations among labeled and unlabeled data samples to achieve an increase of 2.86%–7.30%in fault classification accuracies,providing a novel and promising solution for smart building management.

关键词： fault detection and diagnosis graph convolutional networks semi-supervised learning HVAC systems machine learning

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