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
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
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
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
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
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
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
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
The material mechanical parameters of the dam body and foundation will change when a dam is reinforced during the aging process.This causes significant changes in the structural state of the project and makes it diffi...The material mechanical parameters of the dam body and foundation will change when a dam is reinforced during the aging process.This causes significant changes in the structural state of the project and makes it difficult to ensure its structural safety.In this study,a new deformation warning index for reinforced concrete dams was developed according to the prototype monitoring data,statistical models,three-dimensional finite element model(FEM)numerical simulation,and the critical conditions of the dam structure.A statistical model was established to separate the water pressure component.Then,a three-dimensional FEM of the reinforced concrete dam was constructed to simulate the water pressure component.Furthermore,the deformation components that affected the mechanical parameters of the dam under the same amount of reservoir water level change were separated and quantified accurately.In addition,the method for inversion of comprehensive mechanical parameters after dam reinforcement was used.The influence mechanisms of the deformation behavior of concrete dams under the reservoir water level and temperature changes were investigated.A new deformation warning index was developed by combining the forward-simulated critical water pressure component and temperature component in the period of extreme temperature decrease with the aging component separated by the statistical model.The new deformation warning index considers the structural state of the dam before and after reinforcement and links the structural strength criterion and the deformation evolution mechanisms.It provides a theoretical foundation and decision support for long-term service and operation management of reinforced dams.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
The grating ends bonding fiber Bragg grating(FBG)sensor has been widely used in sensor packages such as substrate type and clamp type for health monitoring of large structures.However,owing to the shear deformation of...The grating ends bonding fiber Bragg grating(FBG)sensor has been widely used in sensor packages such as substrate type and clamp type for health monitoring of large structures.However,owing to the shear deformation of the adhesive layer of FBG,the strain measured by FBG is often different from the strain of actual matrix,which causes strain measurement errors.This investigation aims at improving the measurement accuracy of strain for the grating ends surface-bonded FBG.To fulfill this objective,a strain transfer equation of the grating ends bonding FBG is derived,and a theoretical model of the average strain transfer from the matrix to the optical fiber is developed.Moreover,parameters that influence the average strain transfer rate from the matrix to the optical fiber are analyzed.A selection scheme of bonding parameters by numerical simulation is provided,which is significantly advantageous over that of the grating bonding FBG.The theoretical equation is verified by finite element method(FEM).Compared with the existing model,the proposed model has higher measurement accuracy.Experimental tests are performed to validate the effectiveness of the proposed model on the equalintensity cantilever beam,whose surface is attached to the bare FBG with grating ends bonding and strain gauge by using epoxy glue.The results show that there is a great agreement between the outcome of the bare FBG and that of the strain gauge,and the corrected strain is closer to the true strain.The proposed model provides a theoretical basis for the design of the grating ends surface-bonded FBG strain sensor for health monitoring of large structures.展开更多
Development and testing of a serially multiplexed fiber optic sensor system is described.The sensor differs from conventional fiber optic acoustic systems,as it is capable of sensing AE emissions at several points alo...Development and testing of a serially multiplexed fiber optic sensor system is described.The sensor differs from conventional fiber optic acoustic systems,as it is capable of sensing AE emissions at several points along the length of a single fiber.Multiplexing provides for single channel detection of cracks and their locations in large structural systems. An algorithm was developed for signal recognition and tagging of the AE waveforms for detection of' crack locations,Labora- tory experiments on plain concrete beams and post-tensioned FRP tendons were pcrlormed to evaluate the crack detection capability of the sensor system.The acoustic emission sensor was able to detect initiation,growth and location of the cracks in concrete as well as in the FRP tendons.The AE system is potentially suitable lot applications involving health monitoring of structures following an earthquake.展开更多
Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficient...Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge.This study proposed amodel-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel.Firstly,a dynamic predictive model-based anomaly detectionmethod is proposed,which utilizes a rolling time window for modeling to achieve dynamic prediction.Leveraging the assumption of temporal data similarity,an interval prediction value deviation was employed to determine the abnormality of the data.Subsequently,dynamic predictive models were constructed based on the Autoregressive Integrated Moving Average(ARIMA)and Long Short-Term Memory(LSTM)models.The hyperparameters of these models were optimized and selected using monitoring data from the immersed tunnel,yielding viable static and dynamic predictive models.Finally,the models were applied within the same segment of SHM data,to validate the effectiveness of the anomaly detection approach based on dynamic predictive modeling.A detailed comparative analysis discusses the discrepancies in temporal anomaly detection between the ARIMA-and LSTM-based models.The results demonstrated that the dynamic predictive modelbased anomaly detection approach was effective for dealing with unannotated SHM data.In a comparison between ARIMA and LSTM,it was found that ARIMA demonstrated higher modeling efficiency,rendering it suitable for short-term predictions.In contrast,the LSTM model exhibited greater capacity to capture long-term performance trends and enhanced early warning capabilities,thereby resulting in superior overall performance.展开更多
Structural health monitoring is widely utilized in outdoor environments,especially under harsh conditions,which can introduce noise into the monitoring system.Therefore,designing an effective denoising strategy to enh...Structural health monitoring is widely utilized in outdoor environments,especially under harsh conditions,which can introduce noise into the monitoring system.Therefore,designing an effective denoising strategy to enhance the performance of guided wave damage detection in noisy environments is crucial.This paper introduces a local temporal principal component analysis(PCA)reconstruction approach for denoising guided waves prior to implementing unsupervised damage detection,achieved through novel autoencoder-based reconstruction.Experimental results demonstrate that the proposed denoising method significantly enhances damage detection performance when guided waves are contaminated by noise,with SNR values ranging from 10 to-5 dB.Following the implementation of the proposed denoising approach,the AUC score can elevate from 0.65 to 0.96 when dealing with guided waves corrputed by noise at a level of-5 dB.Additionally,the paper provides guidance on selecting the appropriate number of components used in the denoising PCA reconstruction,aiding in the optimization of the damage detection in noisy conditions.展开更多
A wireless sensor network is proposed to monitor the acceleration of structures for the purpose of structural health monitoring of civil engineering structures. Using commercially available parts, several modules are ...A wireless sensor network is proposed to monitor the acceleration of structures for the purpose of structural health monitoring of civil engineering structures. Using commercially available parts, several modules are constructed and integrated into complete wireless sensors and base stations. The communication protocol is designed and the fusion arithmetic of the temperature and acceleration is embedded in the wireless sensor node so that the measured acceleration values are more accurate. Measures are adopted to finish energy optimization, which is an important issue for a wireless sensor network. The test is perfonned on an offshore platform model, and the experimental results are given to show the feasibility of the designed wireless sensor network .展开更多
To evaluate the fatigue damage reliability of critical members of the Nanjing Yangtze river bridge, according to the stress-number curve and Miner’s rule, the corresponding expressions for calculating the structural ...To evaluate the fatigue damage reliability of critical members of the Nanjing Yangtze river bridge, according to the stress-number curve and Miner’s rule, the corresponding expressions for calculating the structural fatigue damage reliability were derived. Fatigue damage reliability analysis of some critical members of the Nanjing Yangtze river bridge was carried out by using the strain-time histories measured by the structural health monitoring system of the bridge. The corresponding stress spectra were obtained by the real-time rain-flow counting method. Results of fatigue damage were calculated respectively by the reliability method at different reliability and compared with Miner’s rule. The results show that the fatigue damage of critical members of the Nanjing Yangtze river bridge is very small due to its low live-load stress level.展开更多
This study introduces an innovative“Big Model”strategy to enhance Bridge Structural Health Monitoring(SHM)using a Convolutional Neural Network(CNN),time-frequency analysis,and fine element analysis.Leveraging ensemb...This study introduces an innovative“Big Model”strategy to enhance Bridge Structural Health Monitoring(SHM)using a Convolutional Neural Network(CNN),time-frequency analysis,and fine element analysis.Leveraging ensemble methods,collaborative learning,and distributed computing,the approach effectively manages the complexity and scale of large-scale bridge data.The CNN employs transfer learning,fine-tuning,and continuous monitoring to optimize models for adaptive and accurate structural health assessments,focusing on extracting meaningful features through time-frequency analysis.By integrating Finite Element Analysis,time-frequency analysis,and CNNs,the strategy provides a comprehensive understanding of bridge health.Utilizing diverse sensor data,sophisticated feature extraction,and advanced CNN architecture,the model is optimized through rigorous preprocessing and hyperparameter tuning.This approach significantly enhances the ability to make accurate predictions,monitor structural health,and support proactive maintenance practices,thereby ensuring the safety and longevity of critical infrastructure.展开更多
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.
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.
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.
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 the National Natural Science Foundation of China(Grants No.52079049,U2243223,51609074,51739003,and 51579086).
文摘The material mechanical parameters of the dam body and foundation will change when a dam is reinforced during the aging process.This causes significant changes in the structural state of the project and makes it difficult to ensure its structural safety.In this study,a new deformation warning index for reinforced concrete dams was developed according to the prototype monitoring data,statistical models,three-dimensional finite element model(FEM)numerical simulation,and the critical conditions of the dam structure.A statistical model was established to separate the water pressure component.Then,a three-dimensional FEM of the reinforced concrete dam was constructed to simulate the water pressure component.Furthermore,the deformation components that affected the mechanical parameters of the dam under the same amount of reservoir water level change were separated and quantified accurately.In addition,the method for inversion of comprehensive mechanical parameters after dam reinforcement was used.The influence mechanisms of the deformation behavior of concrete dams under the reservoir water level and temperature changes were investigated.A new deformation warning index was developed by combining the forward-simulated critical water pressure component and temperature component in the period of extreme temperature decrease with the aging component separated by the statistical model.The new deformation warning index considers the structural state of the dam before and after reinforcement and links the structural strength criterion and the deformation evolution mechanisms.It provides a theoretical foundation and decision support for long-term service and operation management of reinforced dams.
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.
文摘The grating ends bonding fiber Bragg grating(FBG)sensor has been widely used in sensor packages such as substrate type and clamp type for health monitoring of large structures.However,owing to the shear deformation of the adhesive layer of FBG,the strain measured by FBG is often different from the strain of actual matrix,which causes strain measurement errors.This investigation aims at improving the measurement accuracy of strain for the grating ends surface-bonded FBG.To fulfill this objective,a strain transfer equation of the grating ends bonding FBG is derived,and a theoretical model of the average strain transfer from the matrix to the optical fiber is developed.Moreover,parameters that influence the average strain transfer rate from the matrix to the optical fiber are analyzed.A selection scheme of bonding parameters by numerical simulation is provided,which is significantly advantageous over that of the grating bonding FBG.The theoretical equation is verified by finite element method(FEM).Compared with the existing model,the proposed model has higher measurement accuracy.Experimental tests are performed to validate the effectiveness of the proposed model on the equalintensity cantilever beam,whose surface is attached to the bare FBG with grating ends bonding and strain gauge by using epoxy glue.The results show that there is a great agreement between the outcome of the bare FBG and that of the strain gauge,and the corrected strain is closer to the true strain.The proposed model provides a theoretical basis for the design of the grating ends surface-bonded FBG strain sensor for health monitoring of large structures.
基金National Science Foundation,Grant number CMS-9900338
文摘Development and testing of a serially multiplexed fiber optic sensor system is described.The sensor differs from conventional fiber optic acoustic systems,as it is capable of sensing AE emissions at several points along the length of a single fiber.Multiplexing provides for single channel detection of cracks and their locations in large structural systems. An algorithm was developed for signal recognition and tagging of the AE waveforms for detection of' crack locations,Labora- tory experiments on plain concrete beams and post-tensioned FRP tendons were pcrlormed to evaluate the crack detection capability of the sensor system.The acoustic emission sensor was able to detect initiation,growth and location of the cracks in concrete as well as in the FRP tendons.The AE system is potentially suitable lot applications involving health monitoring of structures following an earthquake.
基金supported by the Research and Development Center of Transport Industry of New Generation of Artificial Intelligence Technology(Grant No.202202H)the National Key R&D Program of China(Grant No.2019YFB1600702)the National Natural Science Foundation of China(Grant Nos.51978600&51808336).
文摘Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge.This study proposed amodel-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel.Firstly,a dynamic predictive model-based anomaly detectionmethod is proposed,which utilizes a rolling time window for modeling to achieve dynamic prediction.Leveraging the assumption of temporal data similarity,an interval prediction value deviation was employed to determine the abnormality of the data.Subsequently,dynamic predictive models were constructed based on the Autoregressive Integrated Moving Average(ARIMA)and Long Short-Term Memory(LSTM)models.The hyperparameters of these models were optimized and selected using monitoring data from the immersed tunnel,yielding viable static and dynamic predictive models.Finally,the models were applied within the same segment of SHM data,to validate the effectiveness of the anomaly detection approach based on dynamic predictive modeling.A detailed comparative analysis discusses the discrepancies in temporal anomaly detection between the ARIMA-and LSTM-based models.The results demonstrated that the dynamic predictive modelbased anomaly detection approach was effective for dealing with unannotated SHM data.In a comparison between ARIMA and LSTM,it was found that ARIMA demonstrated higher modeling efficiency,rendering it suitable for short-term predictions.In contrast,the LSTM model exhibited greater capacity to capture long-term performance trends and enhanced early warning capabilities,thereby resulting in superior overall performance.
基金National Science Foundation of Zhejiang under Contract(LY23E010001)。
文摘Structural health monitoring is widely utilized in outdoor environments,especially under harsh conditions,which can introduce noise into the monitoring system.Therefore,designing an effective denoising strategy to enhance the performance of guided wave damage detection in noisy environments is crucial.This paper introduces a local temporal principal component analysis(PCA)reconstruction approach for denoising guided waves prior to implementing unsupervised damage detection,achieved through novel autoencoder-based reconstruction.Experimental results demonstrate that the proposed denoising method significantly enhances damage detection performance when guided waves are contaminated by noise,with SNR values ranging from 10 to-5 dB.Following the implementation of the proposed denoising approach,the AUC score can elevate from 0.65 to 0.96 when dealing with guided waves corrputed by noise at a level of-5 dB.Additionally,the paper provides guidance on selecting the appropriate number of components used in the denoising PCA reconstruction,aiding in the optimization of the damage detection in noisy conditions.
基金Supported by the High Technology Research and Development Programme of China （ No. 2003AA602230） and the National Natural Science Foundation of China（No. 50308007）.
文摘A wireless sensor network is proposed to monitor the acceleration of structures for the purpose of structural health monitoring of civil engineering structures. Using commercially available parts, several modules are constructed and integrated into complete wireless sensors and base stations. The communication protocol is designed and the fusion arithmetic of the temperature and acceleration is embedded in the wireless sensor node so that the measured acceleration values are more accurate. Measures are adopted to finish energy optimization, which is an important issue for a wireless sensor network. The test is perfonned on an offshore platform model, and the experimental results are given to show the feasibility of the designed wireless sensor network .
基金Project(2001G025) supported by the Foundation of the Science and Technology Section of Ministry of Rail way of Chinaproject(2005) supported by the Postdoctoral Foundation of Central South University
文摘To evaluate the fatigue damage reliability of critical members of the Nanjing Yangtze river bridge, according to the stress-number curve and Miner’s rule, the corresponding expressions for calculating the structural fatigue damage reliability were derived. Fatigue damage reliability analysis of some critical members of the Nanjing Yangtze river bridge was carried out by using the strain-time histories measured by the structural health monitoring system of the bridge. The corresponding stress spectra were obtained by the real-time rain-flow counting method. Results of fatigue damage were calculated respectively by the reliability method at different reliability and compared with Miner’s rule. The results show that the fatigue damage of critical members of the Nanjing Yangtze river bridge is very small due to its low live-load stress level.
文摘This study introduces an innovative“Big Model”strategy to enhance Bridge Structural Health Monitoring(SHM)using a Convolutional Neural Network(CNN),time-frequency analysis,and fine element analysis.Leveraging ensemble methods,collaborative learning,and distributed computing,the approach effectively manages the complexity and scale of large-scale bridge data.The CNN employs transfer learning,fine-tuning,and continuous monitoring to optimize models for adaptive and accurate structural health assessments,focusing on extracting meaningful features through time-frequency analysis.By integrating Finite Element Analysis,time-frequency analysis,and CNNs,the strategy provides a comprehensive understanding of bridge health.Utilizing diverse sensor data,sophisticated feature extraction,and advanced CNN architecture,the model is optimized through rigorous preprocessing and hyperparameter tuning.This approach significantly enhances the ability to make accurate predictions,monitor structural health,and support proactive maintenance practices,thereby ensuring the safety and longevity of critical infrastructure.
