Conventional optimal sensor placement(OSP)methods employ the premise that all sensors work perfectly during long-term structural monitoring.However,this premise is often difficult to fulfill in real applications due t...Conventional optimal sensor placement(OSP)methods employ the premise that all sensors work perfectly during long-term structural monitoring.However,this premise is often difficult to fulfill in real applications due to poor manufacturing and material aging of sensors,human damage,and electromagnetic interference.This paper presents a robustness-oriented OSP method that considers sensor failures.The OSP problem is designed with consideration of sensor failures to ensure that both complete vibration data collected by all sensors and incomplete vibration data caused by individual sensor failures can accurately identify structural modal parameters.A dispersion-aggregation firefly algorithm(DAFA),which is derived from the basic firefly algorithm,has been proposed to solve this complicated optimization problem.The dispersion and aggregation operators are designed to prevent falling into local optima and to rapidly converge to the global optima.The proposed methodology is confirmed by extracting the robust sensor configuration for a long-span cable-stayed bridge.The robustness of the optimal sensor configurations against sensor failure is thoroughly explored,and the performance of the proposed DAFA is extensively examined.展开更多
This paper provides a review on the development of structural monitoring in Japan, with an emphasis on the type, strategy, and utilization of monitoring systems. The review focuses on bridge and building structures us...This paper provides a review on the development of structural monitoring in Japan, with an emphasis on the type, strategy, and utilization of monitoring systems. The review focuses on bridge and building structures using vibration-based techniques. Structural monitoring systems in Japan historically started with the objective of evaluating structural responses against extreme events. In the development of structural monitoring, monitoring systems and collected data were used to verify design assumptions, update speci cations, and facilitate the ef cacy of vibration control systems. Strategies and case studies on monitoring for the design veri cation of long-span bridges and tall buildings, the performance of seismic isolation systems in building and bridges, the veri cation of structural retro t, the veri cation of structural control systems (passive, semi-active, and active), structural assessment, and damage detec- tion are described. More recently, the application of monitoring systems has been extended to facilitate ef cient operation and effective maintenance through the rationalization of risk and asset management using monitoring data. This paper also summarizes the lessons learned and feedback obtained from case studies on the structural monitoring of bridges and buildings in Japan.展开更多
Operational modal analysis is a non-destructive structural investigation that considers only the loads resulting from service conditions.This approach allows the measurement of vibrations on a given structure with no ...Operational modal analysis is a non-destructive structural investigation that considers only the loads resulting from service conditions.This approach allows the measurement of vibrations on a given structure with no need to interrupt its use.The present work aims to develop a numerical model to represent the global structural behavior of a vessel breasting dolphin using a technique that is simple and cheap in order to obtain a fast answer about the stiffness of a pier after the collision of ships with capacity up to 400,000 t.To determine the modes of vibration,one accelerometer was installed on the breasting dolphin located on the pier and a frequency domain technic was conducted over recorded data to obtain modal parameters of the structure.In situ measurements were compared to data from a finite element model based on the original structural design in order to adapt the model to accurately represent the actual behavior of the system.This allowed a reliable structural analysis that accounted for existing structural damage and imperfections.The results of the experiment presented herein are the numerical characterization of the structure,along with the structural analysis to assess the degree of damage currently observed on the system.It is noted that the dolphin subjected to ship impacts presents a reduction in stiffness of approximately10%and its global damage level can be monitored from now after new accidents.展开更多
Fatigue damage is a primary contributor to the failure of composite structures,underscoring the critical importance of monitoring its progression to ensure structural safety.This paper introduces an innovative approac...Fatigue damage is a primary contributor to the failure of composite structures,underscoring the critical importance of monitoring its progression to ensure structural safety.This paper introduces an innovative approach to fatigue damage monitoring in composite structures,leveraging a hybrid methodology that integrates the Whale Optimization Algorithm(WOA)-Backpropagation(BP)neural network with an ultrasonic guided wave feature selection algorithm.Initially,a network of piezoelectric ceramic sensors is employed to transmit and capture ultrasonic-guided waves,thereby establishing a signal space that correlates with the structural condition.Subsequently,the Relief-F algorithm is applied for signal feature extraction,culminating in the formation of a feature matrix.This matrix is then utilized to train the WOA-BP neural network,which optimizes the fatigue damage identification model globally.The proposed model’s efficacy in quantifying fatigue damage is tested against fatigue test datasets,with its performance benchmarked against the traditional BP neural network algorithm.The findings demonstrate that the WOA-BP neural network model not only surpasses the BP model in predictive accuracy but also exhibits enhanced global search capabilities.The effect of different sensor-receiver path signals on the model damage recognition results is also discussed.The results of the discussion found that the path directly through the damaged area is more accurate in modeling damage recognition compared to the path signals away from the damaged area.Consequently,the proposed monitoring method in the fatigue test dataset is adept at accurately tracking and recognizing the progression of fatigue damage.展开更多
This study developed a digital twin(DT)and structural health monitoring(SHM)system for a balanced cantilever bridge,utilizing advanced measurement techniques to enhance accuracy.Vibration and dynamic strain measuremen...This study developed a digital twin(DT)and structural health monitoring(SHM)system for a balanced cantilever bridge,utilizing advanced measurement techniques to enhance accuracy.Vibration and dynamic strain measurements were obtained using accelerometers and piezo-resistive strain gauges,capturing low-magnitude dynamic strains during operational vibrations.3D-LiDAR scanning and Ultrasonic Pulse Velocity(UPV)tests captured the bridge's as-is geometry and modulus of elasticity.The resulting detailed 3D point cloud model revealed the structure's true state and highlighted discrepancies between the as-designed and as-built conditions.Dynamic properties,including modal frequencies and shapes,were extracted from the strain and acceleration measurements,providing critical insights into the bridge's structural behavior.The neutral axis depth,indicating stress distribution and potential damage,was accurately determined.Good agreement between vibration measurement data and the as-is model results validated the reliability of the digital twin model.Dynamic strain patterns and neutral axis parameters showed strong correlation with model predictions,serving as sensitive indicators of local damage.The baseline digital twin model and measurement results establish a foundation for future bridge inspections and investigations.This study demonstrates the effectiveness of combining digital twin technology with field measurements for real-time monitoring and predictive maintenance,ensuring the sustainability and safety of the bridge infrastructure,thereby enhancing its overall resilience to operational and environmental stressors.展开更多
Lost acceleration response reconstruction is crucial for assessing structural conditions in structural health monitoring(SHM).However,traditional methods struggle to address the reconstruction of acceleration response...Lost acceleration response reconstruction is crucial for assessing structural conditions in structural health monitoring(SHM).However,traditional methods struggle to address the reconstruction of acceleration responses with complex features,resulting in a lower reconstruction accuracy.This paper addresses this challenge by leveraging the advanced feature extraction and learning capabilities of fully convolutional networks(FCN)to achieve precise reconstruction of acceleration responses.In the designed network architecture,the incorporation of skip connections preserves low-level details of the network,greatly facilitating the flow of information and improving training efficiency and accuracy.Dropout techniques are employed to reduce computational load and enhance feature extraction.The proposed FCN model automatically extracts high-level features from the input data and establishes a nonlinearmapping relationship between the input and output responses.Finally,the accuracy of the FCN for structural response reconstructionwas evaluated using acceleration data from an experimental arch rib and comparedwith several traditional methods.Additionally,this approach was applied to reconstruct actual acceleration responses measured by an SHM system on a long-span bridge.Through parameter analysis,the feasibility and accuracy of aspects such as available response positions,the number of available channels,and multi-channel response reconstruction were explored.The results indicate that this method exhibits high-precision response reconstruction capability in both time and frequency domains.,with performance surpassing that of other networks,confirming its effectiveness in reconstructing responses under various sensor data loss scenarios.展开更多
In the fabrication and monitoring of parts in composite structures,which are being used more and more in a variety of engineering applications,the prediction and fatigue failure detection in composite materials is a d...In the fabrication and monitoring of parts in composite structures,which are being used more and more in a variety of engineering applications,the prediction and fatigue failure detection in composite materials is a difficult problem.This difficulty arises from several factors,such as the lack of a comprehensive investigation of the fatigue failure phenomena,the lack of a well-defined fatigue damage theory used for fatigue damage prediction,and the inhomogeneity of composites because of their multiple internal borders.This study investigates the fatigue behavior of carbon fiber reinforced with epoxy(CFRE)laminated composite plates under spectrum loading utilizing a uniqueDeep LearningNetwork consisting of a convolutional neural network(CNN).Themethod includes establishing Finite Element Model(FEM)in a plate model under a spectrum fatigue loading.Then,a CNN is trained for fatigue behavior prediction.The training phase produces promising results,showing the model’s performance with 94.21%accuracy,92.63%regression,and 91.55%F-score.To evaluate the model’s reliability,a comparison is made between fatigue data from the CNN and the FEM.It was found that the error band for this comparison is less than 0.3878MPa,affirming the accuracy and reliability of the proposed technique.The proposed method results converge with available experimental results in the literature,thus,the study suggests the broad applicability of this method to other different composite structures.展开更多
Predictive maintenance is essential for the implementation of an innovative and efficient structural health monitoring strategy.Models capable of accurately interpreting new data automatically collected by suitably pl...Predictive maintenance is essential for the implementation of an innovative and efficient structural health monitoring strategy.Models capable of accurately interpreting new data automatically collected by suitably placed sensors to assess the state of the infrastructure represent a fundamental step,particularly for the railway sector,whose safe and continuous operation plays a strategic role in the well-being and development of nations.In this scenario,the benefits of a digital twin of a bonded insu-lated rail joint(IRJ)with the predictive capabilities of advanced classification algorithms based on artificial intelligence have been explored.The digital model provides an accurate mechanical response of the infrastructure as a pair of wheels passes over the joint.As bolt preload conditions vary,four structural health classes were identified for the joint.Two parameters,i.e.gap value and vertical displacement,which are strongly correlated with bolt preload,are used in different combinations to train and test five predictive classifiers.Their classification effectiveness was assessed using several performance indica-tors.Finally,we compared the IRJ condition predictions of two trained classifiers with the available data,confirming their high accuracy.The approach presented provides an interesting solution for future predictive tools in SHM especially in the case of complex systems such as railways where the vehicle-infrastructure interaction is complex and always time varying.展开更多
An output-only modal identification method by a combination use of the peak-picking method and the cross spectrum methods are presented. Meanwhile, a novel mode shape optimum method of the deck is proposed. The method...An output-only modal identification method by a combination use of the peak-picking method and the cross spectrum methods are presented. Meanwhile, a novel mode shape optimum method of the deck is proposed. The methods are applied to the operational modal identification system of the Runyang Suspension Bridge, which can be used to obtain the modal parameters of the bridge from out-only data sets collected by its structural health monitoring system （SHMS）. As an example, the vibration response data of the deck, cable and tower recorded during typhoon Matsa excitation are used to illustrate the program application. Some of the modal frequencies observed from deck vibration responses are also found in the vibration responses of the cable and the tower. The results show that some modal shapes of the deck are strongly coupled with the cable and the tower. By comparing the identification results from the operational modal system with those from field measurements, a good agreement between them is achieved, but some modal frequencies identified from the operational modal identification system （OMIS）, such as L1 and L2, obviously decrease compared with those from the field measurements.展开更多
This paper deals with an improved bonding approach of surface-bonded fiber Bragg grating (FBG) sensors for airship envelope structural health monitoring (SHM) under the strain transfer theory. A theoretical formula is...This paper deals with an improved bonding approach of surface-bonded fiber Bragg grating (FBG) sensors for airship envelope structural health monitoring (SHM) under the strain transfer theory. A theoretical formula is derived from the proposed model to predict the strain transfer relationship between the airship envelope and fiber core. Then theoretical predictions are validated by numerical analysis using the finite element method (FEM). Finally, on the basis of the theoretical approach and numerical validation, parameters that influence the strain transfer rate from the airship envelope to fiber core and the ratio of effective sensing length are analyzed, and some meaningful conclusions are provided.展开更多
This paper focuses on developing an online structural condition assessment technique using long-term monitoring data measured by a structural health monitoring system. The seasonal correlations of frequency-temperatur...This paper focuses on developing an online structural condition assessment technique using long-term monitoring data measured by a structural health monitoring system. The seasonal correlations of frequency-temperature and beam-end displacement-temperature for the Runyang Suspension Bridge are performed, first. Then, a statistical modeling technique using a six-order polynomial is further applied to formulate the correlations of frequency-temperature and displacement-temperature, from which abnormal changes of measured frequencies and displacements are detected using the mean value control chart. Analysis results show that modal frequencies of higher vibration modes and displacements have remarkable seasonal correlations with the environmental temperature and the proposed method exhibits a good capability for detecting the micro damage-induced changes of modal frequencies and displacements. The results demonstrate that the proposed method can effectively eliminate temperature complications from frequency and displacement time series and is well suited for online condition monitoring of long-span suspension bridges.展开更多
Structural deformation monitoring of flight vehicles based on optical fiber sensing(OFS)technology has been a focus of research in the field of aerospace.After nearly 30 years of research and development,Chinese and i...Structural deformation monitoring of flight vehicles based on optical fiber sensing(OFS)technology has been a focus of research in the field of aerospace.After nearly 30 years of research and development,Chinese and international researchers have made significant advances in the areas of theory and methods,technology and systems,and ground experiments and flight tests.These advances have led to the development of OFS technology from the laboratory research stage to the engineering application stage.However,a few problems encountered in practical applications limit the wider application and further development of this technology,and thus urgently require solutions.This paper reviews the history of research on the deformation monitoring of flight vehicles.It examines various aspects of OFS-based deformation monitoring including the main varieties of OFS technology,technical advantages and disadvantages,suitability in aerospace applications,deformation reconstruction algorithms,and typical applications.This paper points out the key unresolved problems and the main evolution paradigms of engineering applications.It further discusses future development directions from the perspectives of an evolution paradigm,standardization,new materials,intelligentization,and collaboration.展开更多
Many theoretical studies have been developed to study the spectral response of a fiber Bragg grating (FBG) under non-uniform strain distribution along the length of FBG in recent years. However, almost no experiments ...Many theoretical studies have been developed to study the spectral response of a fiber Bragg grating (FBG) under non-uniform strain distribution along the length of FBG in recent years. However, almost no experiments were designed to obtain the evolution of the spectrum when a FBG is subjected to non-uniform strain. In this paper, the spectral responses of a FBG under non-uniform strain distributions are given and a numerical simulation based on the Runge-Kutta method is introduced to investigate the responses of the FBG under some typical non-uniform transverse strain fields, including both linear strain gradient and quadratic strain field. Experiment is carried out by using loads applied at different locations near the FBG. Good agreements between experimental results and numerical simulations are obtained.展开更多
Over the past few years,major investments have been directed toward building new railway lines and upgrading existing ones.Many of these lines include critical infrastructure where operational and safety conditions mu...Over the past few years,major investments have been directed toward building new railway lines and upgrading existing ones.Many of these lines include critical infrastructure where operational and safety conditions must be carefully considered throughout their life cycle.Recent advancements in science and technology have enabled more effective structural monitoring of railway systems,largely driven by the adoption of intelligent strategies for inspection,maintenance,monitoring,and risk management.Research continues to expand and deepen the knowledge in this area;however,it remains a challenging field due to factors such as the complexity of railway systems,the high cost of implementation,and the need for reliable long-term data.展开更多
Structure health monitoring based on diagnostic Lamb waves has been found to be one of the most promising techniques recently. This paper has a brief review of the new developments on this method including the basic n...Structure health monitoring based on diagnostic Lamb waves has been found to be one of the most promising techniques recently. This paper has a brief review of the new developments on this method including the basic novel of the method, fundamentals and mathematics of Lamb wave propagation, narrowband and wideband Lamb wave excitation methods, optimization of excitation factors and diagnostic Lamb wave interpretation methods.展开更多
During the service life of civil engineering structures such as long-span bridges, local damage at key positions may continually accumulate, and may finally result in their sudden failure. One core issue of global vib...During the service life of civil engineering structures such as long-span bridges, local damage at key positions may continually accumulate, and may finally result in their sudden failure. One core issue of global vibration-based health monitoring methods is to seek some damage indices that are sensitive to structural damage, This paper proposes an online structural health monitoring method for long-span suspension bridges using wavelet packet transform （WPT）. The WPT- based method is based on the energy variations of structural ambient vibration responses decomposed using wavelet packet analysis. The main feature of this method is that the proposed wavelet packet energy spectrum （WPES） has the ability to detect structural damage from ambient vibration tests of a long-span suspension bridge. As an example application, the WPES-based health monitoring system is used on the Runyang Suspension Bridge under daily environmental conditions. The analysis reveals that changes in environmental temperature have a long-term influence on the WPES, while the effect of traffic loadings on the measured WPES of the bridge presents instantaneous changes because of the nonstationary properties of the loadings. The condition indication indices VD reflect the influences of environmental temperature on the dynamic properties of the Runyang Suspension Bridge. The field tests demonstrate that the proposed WPES-based condition indication index VD is a good candidate index for health monitoring of long-span suspension bridges under ambient excitations.展开更多
In order to evaluate objectively and accurately the integrity, safety and operating conditions in real time for the Nanjing Yangtze River Bridge, a large structural safety monitoring system was described. The monitori...In order to evaluate objectively and accurately the integrity, safety and operating conditions in real time for the Nanjing Yangtze River Bridge, a large structural safety monitoring system was described. The monitoring system is composed of three parts: sensor system, signal sampling and processing system, and safety monitoring and assessment system. Combining theoretical analysis with measured data analysis, main monitoring contents and layout of measuring points were determined. The vibration response monitoring was significantly investigated. The main contents of safety monitoring on vibration response monitoring are vibration of the main body of the Nanjing Yangtze river bridge, collision avoidance of the bridge piers, vibration of girders on high piers for the bridge approach and earthquake. As a field laboratory, the safety monitorying system also provides information to investigate the unknown and indeterminate problems on bridge structures and specific environment around bridges.展开更多
Fiber Bragg grating (FBG) based sensors offer important advantages over traditional instrumentation with regards to real-time structural health monitoring (SHM) of composite materials and structures in recent years. F...Fiber Bragg grating (FBG) based sensors offer important advantages over traditional instrumentation with regards to real-time structural health monitoring (SHM) of composite materials and structures in recent years. FBG sensors, integrated into existing structures or embedded into new ones, have played a major role in assessing the safety and integrity of engineering structures. In this paper, a review on the latest research of the FBG-based SHM technique for composite field is presented. Firstly, the FBG sensing principle is briefly discussed and FBG and several other optical fiber sensors (OFSs) for SHM are performance-compared. Then, several examples of the use of FBG sensors in composite SHM are illustrated, including those from the field of cure monitoring, civil engineering, aviation, aerospace, marine and offshore platform. Finally, some existing problems are pointed out and some proposals for further researches are provided.展开更多
Based on the physical meaning of sensitivity,a new finite element(FE) model updating method was proposed. In this method,a three-dimensional FE model of the Nanjing Yangtze River Bridge(NYRB) with ANSYS program was es...Based on the physical meaning of sensitivity,a new finite element(FE) model updating method was proposed. In this method,a three-dimensional FE model of the Nanjing Yangtze River Bridge(NYRB) with ANSYS program was established and updated by modifying some design parameters. To further validate the updated FE model,the analytical stress-time histories responses of main members induced by a moving train were compared with the measured ones. The results show that the relative error of maximum stress is 2.49% and the minimum relative coefficient of analytical stress-time histories responses is 0.793. The updated model has a good agreement between the calculated data and the tested data,and provides a current baseline FE model for long-term health monitoring and condition assessment of the NYRB. At the same time,the model is validated by stress-time histories responses to be feasible and practical for railway steel bridge model updating.展开更多
A joint effort between the Connecticut Department of Transportation and the University of Connecticut has been underway for more than 20 years to utilize various structural monitoring approaches to assess different br...A joint effort between the Connecticut Department of Transportation and the University of Connecticut has been underway for more than 20 years to utilize various structural monitoring approaches to assess different bridges in Connecticut. This has been done to determine the performance of existing bridges, refine techniques needed to evaluate different bridge components, and develop approaches that can be used to provide a continuous status of a bridge＇s structural integrity. This paper briefly introduces the background of these studies, with emphasis on recent research and the development of structural health monitoring concepts. This paper presents the results from three different bridge types： a post-tensioned curved concrete box girder bridge, a curved steel box-girder bridge, and a steel multi-girder bridge. The structural health monitoring approaches to be discussed have been successfully tested using field data collected during multi-year monitoring periods, and are based on vibrations, rotations and strains. The goal has been to develop cost-effective strategies to provide critical information needed to manage the State of Connecticut＇s bridge infrastructure.展开更多
基金The National Natural Science Foundation of China(No.51978243,52578360).
文摘Conventional optimal sensor placement(OSP)methods employ the premise that all sensors work perfectly during long-term structural monitoring.However,this premise is often difficult to fulfill in real applications due to poor manufacturing and material aging of sensors,human damage,and electromagnetic interference.This paper presents a robustness-oriented OSP method that considers sensor failures.The OSP problem is designed with consideration of sensor failures to ensure that both complete vibration data collected by all sensors and incomplete vibration data caused by individual sensor failures can accurately identify structural modal parameters.A dispersion-aggregation firefly algorithm(DAFA),which is derived from the basic firefly algorithm,has been proposed to solve this complicated optimization problem.The dispersion and aggregation operators are designed to prevent falling into local optima and to rapidly converge to the global optima.The proposed methodology is confirmed by extracting the robust sensor configuration for a long-span cable-stayed bridge.The robustness of the optimal sensor configurations against sensor failure is thoroughly explored,and the performance of the proposed DAFA is extensively examined.
文摘This paper provides a review on the development of structural monitoring in Japan, with an emphasis on the type, strategy, and utilization of monitoring systems. The review focuses on bridge and building structures using vibration-based techniques. Structural monitoring systems in Japan historically started with the objective of evaluating structural responses against extreme events. In the development of structural monitoring, monitoring systems and collected data were used to verify design assumptions, update speci cations, and facilitate the ef cacy of vibration control systems. Strategies and case studies on monitoring for the design veri cation of long-span bridges and tall buildings, the performance of seismic isolation systems in building and bridges, the veri cation of structural retro t, the veri cation of structural control systems (passive, semi-active, and active), structural assessment, and damage detec- tion are described. More recently, the application of monitoring systems has been extended to facilitate ef cient operation and effective maintenance through the rationalization of risk and asset management using monitoring data. This paper also summarizes the lessons learned and feedback obtained from case studies on the structural monitoring of bridges and buildings in Japan.
文摘Operational modal analysis is a non-destructive structural investigation that considers only the loads resulting from service conditions.This approach allows the measurement of vibrations on a given structure with no need to interrupt its use.The present work aims to develop a numerical model to represent the global structural behavior of a vessel breasting dolphin using a technique that is simple and cheap in order to obtain a fast answer about the stiffness of a pier after the collision of ships with capacity up to 400,000 t.To determine the modes of vibration,one accelerometer was installed on the breasting dolphin located on the pier and a frequency domain technic was conducted over recorded data to obtain modal parameters of the structure.In situ measurements were compared to data from a finite element model based on the original structural design in order to adapt the model to accurately represent the actual behavior of the system.This allowed a reliable structural analysis that accounted for existing structural damage and imperfections.The results of the experiment presented herein are the numerical characterization of the structure,along with the structural analysis to assess the degree of damage currently observed on the system.It is noted that the dolphin subjected to ship impacts presents a reduction in stiffness of approximately10%and its global damage level can be monitored from now after new accidents.
基金funded by the Key Program of the National Natural Science Foundation of China(U2341235)Youth Fund for Basic Research Program of Jiangnan University(JUSRP123003)+2 种基金Postgraduate Research&Practice Innovation Program of Jiangsu Province(SJCX23_1237)the National Key R&D Program of China(2018YFA0702800)Key Technologies R&D Program of CNBM(2023SJYL01).
文摘Fatigue damage is a primary contributor to the failure of composite structures,underscoring the critical importance of monitoring its progression to ensure structural safety.This paper introduces an innovative approach to fatigue damage monitoring in composite structures,leveraging a hybrid methodology that integrates the Whale Optimization Algorithm(WOA)-Backpropagation(BP)neural network with an ultrasonic guided wave feature selection algorithm.Initially,a network of piezoelectric ceramic sensors is employed to transmit and capture ultrasonic-guided waves,thereby establishing a signal space that correlates with the structural condition.Subsequently,the Relief-F algorithm is applied for signal feature extraction,culminating in the formation of a feature matrix.This matrix is then utilized to train the WOA-BP neural network,which optimizes the fatigue damage identification model globally.The proposed model’s efficacy in quantifying fatigue damage is tested against fatigue test datasets,with its performance benchmarked against the traditional BP neural network algorithm.The findings demonstrate that the WOA-BP neural network model not only surpasses the BP model in predictive accuracy but also exhibits enhanced global search capabilities.The effect of different sensor-receiver path signals on the model damage recognition results is also discussed.The results of the discussion found that the path directly through the damaged area is more accurate in modeling damage recognition compared to the path signals away from the damaged area.Consequently,the proposed monitoring method in the fatigue test dataset is adept at accurately tracking and recognizing the progression of fatigue damage.
基金funded by the Thailand Science Research and Innovation Fund,Chulalongkorn University(BCG_FF_68_165_2100_027)The first author(Tidarut Jirawattanasomkul)also gratefully acknowledges support from the Grants for Development of New Faculty Staff,Ratchadaphiseksomphot Fund,Chulalongkorn University.The corresponding author(Supasit Srivaranun)acknowledges the Research and Innovation Funding from National Research Council of Thailand(No.N84A680208)+2 种基金the Research Grant from Faculty of Engineering,Kasetsart University(No.67/05/CE)The fourth author(Suched Likitlersuang)acknowledges Thailand Science Research and Innovation Fund Chulalongkorn University(DISF68210001)the National Research Council of Thailand(NRCT):Grant No.N42A670572.
文摘This study developed a digital twin(DT)and structural health monitoring(SHM)system for a balanced cantilever bridge,utilizing advanced measurement techniques to enhance accuracy.Vibration and dynamic strain measurements were obtained using accelerometers and piezo-resistive strain gauges,capturing low-magnitude dynamic strains during operational vibrations.3D-LiDAR scanning and Ultrasonic Pulse Velocity(UPV)tests captured the bridge's as-is geometry and modulus of elasticity.The resulting detailed 3D point cloud model revealed the structure's true state and highlighted discrepancies between the as-designed and as-built conditions.Dynamic properties,including modal frequencies and shapes,were extracted from the strain and acceleration measurements,providing critical insights into the bridge's structural behavior.The neutral axis depth,indicating stress distribution and potential damage,was accurately determined.Good agreement between vibration measurement data and the as-is model results validated the reliability of the digital twin model.Dynamic strain patterns and neutral axis parameters showed strong correlation with model predictions,serving as sensitive indicators of local damage.The baseline digital twin model and measurement results establish a foundation for future bridge inspections and investigations.This study demonstrates the effectiveness of combining digital twin technology with field measurements for real-time monitoring and predictive maintenance,ensuring the sustainability and safety of the bridge infrastructure,thereby enhancing its overall resilience to operational and environmental stressors.
基金National Natural Science Foundation of China(Grant Nos.52408314,52278292)Chongqing Outstanding Youth Science Foundation(Grant No.CSTB2023NSCQ-JQX0029)+1 种基金Science and Technology Project of Sichuan Provincial Transportation Department(Grant No.2023-ZL-03)Science and Technology Project of Guizhou Provincial Transportation Department(Grant No.2024-122-018).
文摘Lost acceleration response reconstruction is crucial for assessing structural conditions in structural health monitoring(SHM).However,traditional methods struggle to address the reconstruction of acceleration responses with complex features,resulting in a lower reconstruction accuracy.This paper addresses this challenge by leveraging the advanced feature extraction and learning capabilities of fully convolutional networks(FCN)to achieve precise reconstruction of acceleration responses.In the designed network architecture,the incorporation of skip connections preserves low-level details of the network,greatly facilitating the flow of information and improving training efficiency and accuracy.Dropout techniques are employed to reduce computational load and enhance feature extraction.The proposed FCN model automatically extracts high-level features from the input data and establishes a nonlinearmapping relationship between the input and output responses.Finally,the accuracy of the FCN for structural response reconstructionwas evaluated using acceleration data from an experimental arch rib and comparedwith several traditional methods.Additionally,this approach was applied to reconstruct actual acceleration responses measured by an SHM system on a long-span bridge.Through parameter analysis,the feasibility and accuracy of aspects such as available response positions,the number of available channels,and multi-channel response reconstruction were explored.The results indicate that this method exhibits high-precision response reconstruction capability in both time and frequency domains.,with performance surpassing that of other networks,confirming its effectiveness in reconstructing responses under various sensor data loss scenarios.
文摘In the fabrication and monitoring of parts in composite structures,which are being used more and more in a variety of engineering applications,the prediction and fatigue failure detection in composite materials is a difficult problem.This difficulty arises from several factors,such as the lack of a comprehensive investigation of the fatigue failure phenomena,the lack of a well-defined fatigue damage theory used for fatigue damage prediction,and the inhomogeneity of composites because of their multiple internal borders.This study investigates the fatigue behavior of carbon fiber reinforced with epoxy(CFRE)laminated composite plates under spectrum loading utilizing a uniqueDeep LearningNetwork consisting of a convolutional neural network(CNN).Themethod includes establishing Finite Element Model(FEM)in a plate model under a spectrum fatigue loading.Then,a CNN is trained for fatigue behavior prediction.The training phase produces promising results,showing the model’s performance with 94.21%accuracy,92.63%regression,and 91.55%F-score.To evaluate the model’s reliability,a comparison is made between fatigue data from the CNN and the FEM.It was found that the error band for this comparison is less than 0.3878MPa,affirming the accuracy and reliability of the proposed technique.The proposed method results converge with available experimental results in the literature,thus,the study suggests the broad applicability of this method to other different composite structures.
基金the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4-Call for tender No. 3138 of 16/12/2021 of Italian Ministry of University and Research funded by the European Union-Next Generation EU. Award Number: Project code CN00000023Concession Decree No. 1033 of 17/06/2022 adopted by the Italian Ministry of University and Research, CUP D93C22000400001, “Sustainable Mobility Center” (CNMS). Spoke 4-Rail Transportation
文摘Predictive maintenance is essential for the implementation of an innovative and efficient structural health monitoring strategy.Models capable of accurately interpreting new data automatically collected by suitably placed sensors to assess the state of the infrastructure represent a fundamental step,particularly for the railway sector,whose safe and continuous operation plays a strategic role in the well-being and development of nations.In this scenario,the benefits of a digital twin of a bonded insu-lated rail joint(IRJ)with the predictive capabilities of advanced classification algorithms based on artificial intelligence have been explored.The digital model provides an accurate mechanical response of the infrastructure as a pair of wheels passes over the joint.As bolt preload conditions vary,four structural health classes were identified for the joint.Two parameters,i.e.gap value and vertical displacement,which are strongly correlated with bolt preload,are used in different combinations to train and test five predictive classifiers.Their classification effectiveness was assessed using several performance indica-tors.Finally,we compared the IRJ condition predictions of two trained classifiers with the available data,confirming their high accuracy.The approach presented provides an interesting solution for future predictive tools in SHM especially in the case of complex systems such as railways where the vehicle-infrastructure interaction is complex and always time varying.
基金The National High Technology Research and Development Program of China(863Program)(No.2006AA04Z416)
文摘An output-only modal identification method by a combination use of the peak-picking method and the cross spectrum methods are presented. Meanwhile, a novel mode shape optimum method of the deck is proposed. The methods are applied to the operational modal identification system of the Runyang Suspension Bridge, which can be used to obtain the modal parameters of the bridge from out-only data sets collected by its structural health monitoring system （SHMS）. As an example, the vibration response data of the deck, cable and tower recorded during typhoon Matsa excitation are used to illustrate the program application. Some of the modal frequencies observed from deck vibration responses are also found in the vibration responses of the cable and the tower. The results show that some modal shapes of the deck are strongly coupled with the cable and the tower. By comparing the identification results from the operational modal system with those from field measurements, a good agreement between them is achieved, but some modal frequencies identified from the operational modal identification system （OMIS）, such as L1 and L2, obviously decrease compared with those from the field measurements.
基金Project (No. 2011AA7052011) supported by the National High-Tech R&D (863) Program of China
文摘This paper deals with an improved bonding approach of surface-bonded fiber Bragg grating (FBG) sensors for airship envelope structural health monitoring (SHM) under the strain transfer theory. A theoretical formula is derived from the proposed model to predict the strain transfer relationship between the airship envelope and fiber core. Then theoretical predictions are validated by numerical analysis using the finite element method (FEM). Finally, on the basis of the theoretical approach and numerical validation, parameters that influence the strain transfer rate from the airship envelope to fiber core and the ratio of effective sensing length are analyzed, and some meaningful conclusions are provided.
基金National Natural Science Foundation of China Under Grant No.50725828 & No.50808041PhD Programs Foundation of Ministry of Education of China Under Grant No. 200802861011Scientific Research Foundation of Graduate School of Southeast University Under Grant No.YBJJ0923
文摘This paper focuses on developing an online structural condition assessment technique using long-term monitoring data measured by a structural health monitoring system. The seasonal correlations of frequency-temperature and beam-end displacement-temperature for the Runyang Suspension Bridge are performed, first. Then, a statistical modeling technique using a six-order polynomial is further applied to formulate the correlations of frequency-temperature and displacement-temperature, from which abnormal changes of measured frequencies and displacements are detected using the mean value control chart. Analysis results show that modal frequencies of higher vibration modes and displacements have remarkable seasonal correlations with the environmental temperature and the proposed method exhibits a good capability for detecting the micro damage-induced changes of modal frequencies and displacements. The results demonstrate that the proposed method can effectively eliminate temperature complications from frequency and displacement time series and is well suited for online condition monitoring of long-span suspension bridges.
基金funded by the National Natural Science Foundation of China(51705024,51535002,51675053,61903041,61903042,and 61903041)the National Key Research and Development Program of China(2016YFF0101801)+4 种基金the National Hightech Research and Development Program of China(2015AA042308)the Innovative Equipment Pre-Research Key Fund Project(6140414030101)the Manned Space Pre-Research Project(20184112043)the Beijing Municipal Natural Science Foundation(F7202017 and 4204101)the Beijing Nova Program of Science and Technology(Z191100001119052)。
文摘Structural deformation monitoring of flight vehicles based on optical fiber sensing(OFS)technology has been a focus of research in the field of aerospace.After nearly 30 years of research and development,Chinese and international researchers have made significant advances in the areas of theory and methods,technology and systems,and ground experiments and flight tests.These advances have led to the development of OFS technology from the laboratory research stage to the engineering application stage.However,a few problems encountered in practical applications limit the wider application and further development of this technology,and thus urgently require solutions.This paper reviews the history of research on the deformation monitoring of flight vehicles.It examines various aspects of OFS-based deformation monitoring including the main varieties of OFS technology,technical advantages and disadvantages,suitability in aerospace applications,deformation reconstruction algorithms,and typical applications.This paper points out the key unresolved problems and the main evolution paradigms of engineering applications.It further discusses future development directions from the perspectives of an evolution paradigm,standardization,new materials,intelligentization,and collaboration.
基金supported by the National High Technology Research and Development Program of China (No.2007AA03Z117)the Key Program of National Natural Science Foundation of China (No.50830201)
文摘Many theoretical studies have been developed to study the spectral response of a fiber Bragg grating (FBG) under non-uniform strain distribution along the length of FBG in recent years. However, almost no experiments were designed to obtain the evolution of the spectrum when a FBG is subjected to non-uniform strain. In this paper, the spectral responses of a FBG under non-uniform strain distributions are given and a numerical simulation based on the Runge-Kutta method is introduced to investigate the responses of the FBG under some typical non-uniform transverse strain fields, including both linear strain gradient and quadratic strain field. Experiment is carried out by using loads applied at different locations near the FBG. Good agreements between experimental results and numerical simulations are obtained.
文摘Over the past few years,major investments have been directed toward building new railway lines and upgrading existing ones.Many of these lines include critical infrastructure where operational and safety conditions must be carefully considered throughout their life cycle.Recent advancements in science and technology have enabled more effective structural monitoring of railway systems,largely driven by the adoption of intelligent strategies for inspection,maintenance,monitoring,and risk management.Research continues to expand and deepen the knowledge in this area;however,it remains a challenging field due to factors such as the complexity of railway systems,the high cost of implementation,and the need for reliable long-term data.
基金The authors acknowledge the financial supports from the National Natural Science Foundation of China under grant No.90305005,50135030
文摘Structure health monitoring based on diagnostic Lamb waves has been found to be one of the most promising techniques recently. This paper has a brief review of the new developments on this method including the basic novel of the method, fundamentals and mathematics of Lamb wave propagation, narrowband and wideband Lamb wave excitation methods, optimization of excitation factors and diagnostic Lamb wave interpretation methods.
基金National Hi-Tech Research and Development Program of China (863 Program) (No. 2006AA04Z416)the National Natural Science Foundation of China Under Grant No. 50538020
文摘During the service life of civil engineering structures such as long-span bridges, local damage at key positions may continually accumulate, and may finally result in their sudden failure. One core issue of global vibration-based health monitoring methods is to seek some damage indices that are sensitive to structural damage, This paper proposes an online structural health monitoring method for long-span suspension bridges using wavelet packet transform （WPT）. The WPT- based method is based on the energy variations of structural ambient vibration responses decomposed using wavelet packet analysis. The main feature of this method is that the proposed wavelet packet energy spectrum （WPES） has the ability to detect structural damage from ambient vibration tests of a long-span suspension bridge. As an example application, the WPES-based health monitoring system is used on the Runyang Suspension Bridge under daily environmental conditions. The analysis reveals that changes in environmental temperature have a long-term influence on the WPES, while the effect of traffic loadings on the measured WPES of the bridge presents instantaneous changes because of the nonstationary properties of the loadings. The condition indication indices VD reflect the influences of environmental temperature on the dynamic properties of the Runyang Suspension Bridge. The field tests demonstrate that the proposed WPES-based condition indication index VD is a good candidate index for health monitoring of long-span suspension bridges under ambient excitations.
文摘In order to evaluate objectively and accurately the integrity, safety and operating conditions in real time for the Nanjing Yangtze River Bridge, a large structural safety monitoring system was described. The monitoring system is composed of three parts: sensor system, signal sampling and processing system, and safety monitoring and assessment system. Combining theoretical analysis with measured data analysis, main monitoring contents and layout of measuring points were determined. The vibration response monitoring was significantly investigated. The main contents of safety monitoring on vibration response monitoring are vibration of the main body of the Nanjing Yangtze river bridge, collision avoidance of the bridge piers, vibration of girders on high piers for the bridge approach and earthquake. As a field laboratory, the safety monitorying system also provides information to investigate the unknown and indeterminate problems on bridge structures and specific environment around bridges.
基金the National High Technology Research and Development Program (863) of China(No. 2011AA7052011)the National Natural Science Foundation of China (No. 51205253)
文摘Fiber Bragg grating (FBG) based sensors offer important advantages over traditional instrumentation with regards to real-time structural health monitoring (SHM) of composite materials and structures in recent years. FBG sensors, integrated into existing structures or embedded into new ones, have played a major role in assessing the safety and integrity of engineering structures. In this paper, a review on the latest research of the FBG-based SHM technique for composite field is presented. Firstly, the FBG sensing principle is briefly discussed and FBG and several other optical fiber sensors (OFSs) for SHM are performance-compared. Then, several examples of the use of FBG sensors in composite SHM are illustrated, including those from the field of cure monitoring, civil engineering, aviation, aerospace, marine and offshore platform. Finally, some existing problems are pointed out and some proposals for further researches are provided.
基金Project(2001G025) supported by the Foundation of the Science and Technology Section of Ministry of Railway of ChinaProject(2006FJ4233) supported by Hunan Postdoctoral Scientific Program of ChinaProject(2006) supported by the Postdoctoral Foundation of Central South University,China
文摘Based on the physical meaning of sensitivity,a new finite element(FE) model updating method was proposed. In this method,a three-dimensional FE model of the Nanjing Yangtze River Bridge(NYRB) with ANSYS program was established and updated by modifying some design parameters. To further validate the updated FE model,the analytical stress-time histories responses of main members induced by a moving train were compared with the measured ones. The results show that the relative error of maximum stress is 2.49% and the minimum relative coefficient of analytical stress-time histories responses is 0.793. The updated model has a good agreement between the calculated data and the tested data,and provides a current baseline FE model for long-term health monitoring and condition assessment of the NYRB. At the same time,the model is validated by stress-time histories responses to be feasible and practical for railway steel bridge model updating.
基金Supported by:Federal Highway Administration,United States Department of Transportation
文摘A joint effort between the Connecticut Department of Transportation and the University of Connecticut has been underway for more than 20 years to utilize various structural monitoring approaches to assess different bridges in Connecticut. This has been done to determine the performance of existing bridges, refine techniques needed to evaluate different bridge components, and develop approaches that can be used to provide a continuous status of a bridge＇s structural integrity. This paper briefly introduces the background of these studies, with emphasis on recent research and the development of structural health monitoring concepts. This paper presents the results from three different bridge types： a post-tensioned curved concrete box girder bridge, a curved steel box-girder bridge, and a steel multi-girder bridge. The structural health monitoring approaches to be discussed have been successfully tested using field data collected during multi-year monitoring periods, and are based on vibrations, rotations and strains. The goal has been to develop cost-effective strategies to provide critical information needed to manage the State of Connecticut＇s bridge infrastructure.
