Tunnel facility management(FM)is crucial for ensuring safety,efficiency,and resilience of tunnel infrastructure.Current FM practices,such as reactive and preventive maintenance,have limitations-reactive maintenance ca...Tunnel facility management(FM)is crucial for ensuring safety,efficiency,and resilience of tunnel infrastructure.Current FM practices,such as reactive and preventive maintenance,have limitations-reactive maintenance can't prevent failures,and preventive maintenance can't predict asset maintenance needs,leading to costly and inefficient processes.In addition,existing computerized tunnel FM systems face various challenges,including the lack of integrated real-time monitoring information,visualization of assets in a three-dimensional(3D)environment,supporting predictive maintenance,and scaling into long infrastructure with complicated spatiotemporal relationships.This study addresses these limitations by proposing a data-driven digital twin(DT)-based framework that supports predictive maintenance to improve tunnel FM processes and enhance resilience.The proposed framework consists of six layers,allowing the integration of data from monitoring system,FM system,and building information modeling(BIM)models.The framework proposes a flexible tunnel data model and classification system that hierarchically divides the tunnel models,ensuring an efficient data connection from the physical twin to the DT.The system was implemented in a tunnel case study that generates maintenance plans and work orders using historical and current condition monitoring data,and the 3D visualization technology suggests maintenance and repair processes,making the FM decision process more effective.The proposed system detected and predicted the twin state based on a data-driven analysis,and the prediction accuracy of the machine learning models was sufficiently high for use in real scenarios to make FM plans in advance and prevent asset failures.The proposed framework is contributing to the infrastructure resilience by enhancing the tunnel system ability to predict the maintenance tasks and prevent failures using data-driven DT technology.展开更多
In this study,we present a calibration methodology for drinking water distribution network models using two hydraulic simulation approaches:Demand Driven Analysis(DDA)and Pressure Driven Analysis(PDA),implemented in E...In this study,we present a calibration methodology for drinking water distribution network models using two hydraulic simulation approaches:Demand Driven Analysis(DDA)and Pressure Driven Analysis(PDA),implemented in EPANET 2.2.The calibration process focuses on determining two critical parameters the discharge coefficient and the pressure exponent which govern flow behavior under pressure-dependent conditions.Unlike DDA,which assumes fixed consumption at nodes regardless of pressure,PDA accounts for variable flow rates based on available pressure,offering a more realistic representation of network performance during low-pressure scenarios.These coefficients were derived from national plumbing standards applicable to different consumer categories in Algeria and Romania,and integrated into the numerical models to enhance simulation accuracy.A detailed comparison between DDA and PDA is provided,highlighting the strengths and limitations of each approach.Importantly,this type of numerical model is especially valuable during the design phase of water distribution systems,when physical measurements are unavailable and planning decisions must rely on regulatory data and theoretical assumptions.展开更多
Efficiently predicting effluent quality through data-driven analysis presents a significant advancement for consistent wastewater treatment operations.In this study,we aimed to develop an integrated method for predict...Efficiently predicting effluent quality through data-driven analysis presents a significant advancement for consistent wastewater treatment operations.In this study,we aimed to develop an integrated method for predicting effluent COD and NH3 levels.We employed a 200 L pilot-scale sequencing batch reactor(SBR)to gather multimodal data from urban sewage over 40 d.Then we collected data on critical parameters like COD,DO,pH,NH_(3),EC,ORP,SS,and water temperature,alongside wastewater surface images,resulting in a data set of approximately 40246 points.Then we proposed a brain-inspired image and temporal fusion model integrated with a CNN-LSTM network(BITF-CL)using this data.This innovative model synergized sewage imagery with water quality data,enhancing prediction accuracy.As a result,the BITF-CL model reduced prediction error by over 23%compared to traditional methods and still performed comparably to conventional techniques even without using DO and SS sensor data.Consequently,this research presents a cost-effective and precise prediction system for sewage treatment,demonstrating the potential of brain-inspired models.展开更多
文摘Tunnel facility management(FM)is crucial for ensuring safety,efficiency,and resilience of tunnel infrastructure.Current FM practices,such as reactive and preventive maintenance,have limitations-reactive maintenance can't prevent failures,and preventive maintenance can't predict asset maintenance needs,leading to costly and inefficient processes.In addition,existing computerized tunnel FM systems face various challenges,including the lack of integrated real-time monitoring information,visualization of assets in a three-dimensional(3D)environment,supporting predictive maintenance,and scaling into long infrastructure with complicated spatiotemporal relationships.This study addresses these limitations by proposing a data-driven digital twin(DT)-based framework that supports predictive maintenance to improve tunnel FM processes and enhance resilience.The proposed framework consists of six layers,allowing the integration of data from monitoring system,FM system,and building information modeling(BIM)models.The framework proposes a flexible tunnel data model and classification system that hierarchically divides the tunnel models,ensuring an efficient data connection from the physical twin to the DT.The system was implemented in a tunnel case study that generates maintenance plans and work orders using historical and current condition monitoring data,and the 3D visualization technology suggests maintenance and repair processes,making the FM decision process more effective.The proposed system detected and predicted the twin state based on a data-driven analysis,and the prediction accuracy of the machine learning models was sufficiently high for use in real scenarios to make FM plans in advance and prevent asset failures.The proposed framework is contributing to the infrastructure resilience by enhancing the tunnel system ability to predict the maintenance tasks and prevent failures using data-driven DT technology.
文摘In this study,we present a calibration methodology for drinking water distribution network models using two hydraulic simulation approaches:Demand Driven Analysis(DDA)and Pressure Driven Analysis(PDA),implemented in EPANET 2.2.The calibration process focuses on determining two critical parameters the discharge coefficient and the pressure exponent which govern flow behavior under pressure-dependent conditions.Unlike DDA,which assumes fixed consumption at nodes regardless of pressure,PDA accounts for variable flow rates based on available pressure,offering a more realistic representation of network performance during low-pressure scenarios.These coefficients were derived from national plumbing standards applicable to different consumer categories in Algeria and Romania,and integrated into the numerical models to enhance simulation accuracy.A detailed comparison between DDA and PDA is provided,highlighting the strengths and limitations of each approach.Importantly,this type of numerical model is especially valuable during the design phase of water distribution systems,when physical measurements are unavailable and planning decisions must rely on regulatory data and theoretical assumptions.
基金supported by the National Key R&D Program of China(No.2021YFC1809001).
文摘Efficiently predicting effluent quality through data-driven analysis presents a significant advancement for consistent wastewater treatment operations.In this study,we aimed to develop an integrated method for predicting effluent COD and NH3 levels.We employed a 200 L pilot-scale sequencing batch reactor(SBR)to gather multimodal data from urban sewage over 40 d.Then we collected data on critical parameters like COD,DO,pH,NH_(3),EC,ORP,SS,and water temperature,alongside wastewater surface images,resulting in a data set of approximately 40246 points.Then we proposed a brain-inspired image and temporal fusion model integrated with a CNN-LSTM network(BITF-CL)using this data.This innovative model synergized sewage imagery with water quality data,enhancing prediction accuracy.As a result,the BITF-CL model reduced prediction error by over 23%compared to traditional methods and still performed comparably to conventional techniques even without using DO and SS sensor data.Consequently,this research presents a cost-effective and precise prediction system for sewage treatment,demonstrating the potential of brain-inspired models.
