Flexible wearable optoelectronic devices fabricated fromorganic–inorganic hybrid perovskites significantly accelerate the developmentof portable energy,biomedicine,and sensing fields,but their poor thermal stabilityh...Flexible wearable optoelectronic devices fabricated fromorganic–inorganic hybrid perovskites significantly accelerate the developmentof portable energy,biomedicine,and sensing fields,but their poor thermal stabilityhinders further applications.Conversely,all-inorganic perovskites possessexcellent thermal stability,but black-phase all-inorganic perovskite filmusually requires high-temperature annealing steps,which increases energy consumptionand is not conducive to the fabrication of flexible wearable devices.In this work,an unprecedented low-temperature fabrication of stable blackphaseCsPbI3perovskite films is demonstrated by the in situ hydrolysis reactionof diphenylphosphinic chloride additive.The released diphenyl phosphateand chloride ions during the hydrolysis reaction significantly lower the phasetransition temperature and effectively passivate the defects in the perovskitefilms,yielding high-performance photodetectors with a responsivity of 42.1 AW−1 and a detectivity of 1.3×10^(14)Jones.Furthermore,high-fidelity imageand photoplethysmography sensors are demonstrated based on the fabricated flexible wearable photodetectors.This work provides a newperspective for the low-temperature fabrication of large-area all-inorganic perovskite flexible optoelectronic devices.展开更多
Structural Health Monitoring(SHM)systems play a key role in managing buildings and infrastructure by delivering vital insights into their strength and structural integrity.There is a need for more efficient techniques...Structural Health Monitoring(SHM)systems play a key role in managing buildings and infrastructure by delivering vital insights into their strength and structural integrity.There is a need for more efficient techniques to detect defects,as traditional methods are often prone to human error,and this issue is also addressed through image processing(IP).In addition to IP,automated,accurate,and real-time detection of structural defects,such as cracks,corrosion,and material degradation that conventional inspection techniques may miss,is made possible by Artificial Intelligence(AI)technologies like Machine Learning(ML)and Deep Learning(DL).This review examines the integration of computer vision and AI techniques in Structural Health Monitoring(SHM),investigating their effectiveness in detecting various forms of structural deterioration.Also,it evaluates ML and DL models in SHM for their accuracy in identifying and assessing structural damage,ultimately enhancing safety,durability,and maintenance practices in the field.Key findings reveal that AI-powered approaches,especially those utilizing IP and DL models like CNNs,significantly improve detection efficiency and accuracy,with reported accuracies in various SHM tasks.However,significant research gaps remain,including challenges with the consistency,quality,and environmental resilience of image data,a notable lack of standardized models and datasets for training across diverse structures,and concerns regarding computational costs,model interpretability,and seamless integration with existing systems.Future work should focus on developing more robust models through data augmentation,transfer learning,and hybrid approaches,standardizing protocols,and fostering interdisciplinary collaboration to overcome these limitations and achieve more reliable,scalable,and affordable SHM systems.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
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
Considering the noise problem of the acquisition signals frommechanical transmission systems,a novel denoising method is proposed that combines Variational Mode Decomposition(VMD)with wavelet thresholding.The key inno...Considering the noise problem of the acquisition signals frommechanical transmission systems,a novel denoising method is proposed that combines Variational Mode Decomposition(VMD)with wavelet thresholding.The key innovation of this method lies in the optimization of VMD parameters K and α using the improved Horned Lizard Optimization Algorithm(IHLOA).An inertia weight parameter is introduced into the random walk strategy of HLOA,and the related formula is improved.The acquisition signal can be adaptively decomposed into some Intrinsic Mode Functions(IMFs),and the high-noise IMFs are identified based on a correlation coefficient-variance method.Further noise reduction is achieved using wavelet thresholding.The proposed method is validated using simulated signals and experimental signals,and simulation results indicate that the proposed method surpasses original VMD,Empirical Mode Decomposition(EMD),and wavelet thresholding in terms of Signal-to-Noise Ratio(SNR)and Root Mean Square Error(RMSE),and experimental results indicate that the proposedmethod can effectively remove noise in terms of three evaluationmetrics.Furthermore,comparedwith FeatureModeDecomposition(FMD)andMultichannel Singular Spectrum Analysis(MSSA),this method has a better envelope spectrum.This method not only provides a solution for noise reduction in signal processing but also holds significant potential for applications in structural health monitoring and fault diagnosis.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
Monitoring the condition of road infrastructure is crucial for maintaining its structural integrity and ensuring safe transportation.This study proposes a deep learning framework based on Temporal Convolutional Networ...Monitoring the condition of road infrastructure is crucial for maintaining its structural integrity and ensuring safe transportation.This study proposes a deep learning framework based on Temporal Convolutional Networks(TCN)integrated with Adaptive Parametric Rectified Linear Unit(APReLU)to predict future road subbase strain trends.Our model leverages time-series strain data collected from embedded triaxial sensors within a national highway,spanning August 2021 to June 2022,to forecast strain dynamics critical for proactive maintenance planning.The TCN-APReLU architecture combines dilated causal convolutions to capture long-termdependencies and APReLU activation functions to adaptively model nonlinear strain patterns,addressing limitations of traditional ReLU in handling bidirectional strain signals(compressive and tensile).Comparative experiments demonstrate TCN-APReLU’s superior performance.These improvements highlight its enhanced accuracy in predicting strain accumulation under cyclic traffic loads,enabling maintenance teams to prioritize interventions 5-7 days before critical thresholds(e.g.,>100με)are exceeded.This work provides a robust data-driven solution for urban road health monitoring,emphasizing scalability through parallelizable convolutions and adaptability to sensor noise.Future extensions will integrate multi-modal data to further generalize predictions across diverse infrastructure scenarios.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
Millions of people throughout the world struggle with mental health disorders,but the widespread stigma associated with these issues often prevents them from seeking treatment.We propose a novel strategy that integrat...Millions of people throughout the world struggle with mental health disorders,but the widespread stigma associated with these issues often prevents them from seeking treatment.We propose a novel strategy that integrates Internet of Medical Things(IoMT),DAG-based hedera technology,and Artificial Intelligence(AI)to overcome these challenges.We also consider the costs of chronic diseases such as Parkinson’s and Alzheimer’s,which often require 24-hour care.Using smart monitoring tools coupled with AI algorithms that can detect early indicators of deterioration,our system aims to provide low-cost,continuous support.Since IoMT data is large in volume,we need a blockchain network with high transaction throughput without compromising the privacy of patient data.To address this concern,we propose to use Hedera technology to ensure the privacy,and security of personal mental health information,scalability and a faster transaction confirmation rate.Overall,this research paper outlines a holistic approach to mental health monitoring that respects privacy,promotes accessibility,and harnesses the potential of emerging technologies.By combining IoMT,Hedera,and AI,we offer a solution that helps break down the barriers preventing individuals from seeking mental well-being support.Furthermore,comparative analysis shows that our best-performing ML models achieve an accuracy of around 98%,which is more than 30%better than traditional models such as logistic regression。展开更多
This paper aims to study a novel smart self-powered wireless lightweight (SPWL) bridge health monitoring sensor, which integrates key technologies such as large-scale, low-power wireless data transmission, environment...This paper aims to study a novel smart self-powered wireless lightweight (SPWL) bridge health monitoring sensor, which integrates key technologies such as large-scale, low-power wireless data transmission, environmental energy self-harvesting, and intelligent perception, and can operate stably for a long time in complex and changing environments. The self-powered system of the sensor can meet the needs of long-term bridge service performance monitoring, significantly improving the coverage and efficiency of monitoring. By optimizing the sensor system design, the maximum energy conversion of the energy harvesting unit is achieved. In order to verify the function and practicality of the new SPWL monitoring sensor, this study combined the actual bridge engineering, carried out a bridge monitoring case study, and developed an SPWL monitoring scheme based on the bridge structure principle. Compared with traditional monitoring methods, this technology significantly improves the sustainability and performance of infrastructure monitoring based on the new SPWL sensor, fully demonstrating the excellent monitoring capabilities of this type of sensor, and providing strong support for the development of intelligent transportation and intelligent infrastructure.展开更多
This research focuses on solving the fault detection and health monitoring of high-power thyristor converter.In terms of the critical role of thyristor converter in nuclear fusion system,a method based on long short-t...This research focuses on solving the fault detection and health monitoring of high-power thyristor converter.In terms of the critical role of thyristor converter in nuclear fusion system,a method based on long short-term memory(LSTM)neural network model is proposed to monitor the operational state of the converter and accurately detect faults as they occur.By sampling and processing a large number of thyristor converter operation data,the LSTM model is trained to identify and detect abnormal state,and the power supply health status is monitored.Compared with traditional methods,LSTM model shows higher accuracy and abnormal state detection ability.The experimental results show that this method can effectively improve the reliability and safety of the thyristor converter,and provide a strong guarantee for the stable operation of the nuclear fusion reactor.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
In order to more effectively assess the health status of a project, the monitoring indices in a project＇s life cycle are divided into quality index, cost index, time index, satisfaction index, and sustainable develop...In order to more effectively assess the health status of a project, the monitoring indices in a project＇s life cycle are divided into quality index, cost index, time index, satisfaction index, and sustainable development index. Based on the feature of qualitative and quantitative indices combining, the PCA-PR （principal component analysis and pattern recognition） model is constructed. The model first analyzes the principal components of the life-cycle indices system constructed above, and picks up those principal component indices that can reflect the health status of a project at any time. Then the pattern recognition model is used to study these principal components, which means that the real time health status of the project can be divided into five lamps from a green lamp to a red one and the health status lamp of the project can be recognized by using the PR model and those principal components. Finally, the process is shown with a real example and a conclusion consistent with the actual situation is drawn. So the validity of the index system and the PCA-PR model can be confirmed.展开更多
The concept of health monitoring is a key aspect of the field of medicine that has been practiced for a long time. A commonly used diagnostic and health monitoring practice is pulse diagnosis, which can be traced back...The concept of health monitoring is a key aspect of the field of medicine that has been practiced for a long time. A commonly used diagnostic and health monitoring practice is pulse diagnosis, which can be traced back approximately five thousand years in the recorded history of China. With advances in the development of modern technology, the concept of health monitoring of a variety of engineering structures in several applications has begun to attract widespread attention. Of particular interest in this study is the health monitoring of civil structures. It seem natural, and even beneficial, that these two health-monitoring methods, one as applies to the human body and the other to civil structures, should be analyzed and compared. In this paper, the basic concepts and theories of the two monitoring methods are first discussed. Similarities are then summarized and commented upon. It is hoped that this correlation analysis may help provide structural engineers with some insights into the intrinsic concept of using pulse diagnosis in human health monitoring, which may of be some benefit in the development of modern structural health monitoring methods.展开更多
The active Lamb wave and piezoelectric transducer(PZT)-based structural health monitoring(SHM)technology is a kind of efficient approach to estimate the health state of aircraft structure.In practical applications,PZT...The active Lamb wave and piezoelectric transducer(PZT)-based structural health monitoring(SHM)technology is a kind of efficient approach to estimate the health state of aircraft structure.In practical applications,PZT networks are needed to monitor large scale structures.Scanning many of the different PZT actuator-sensor channels within these PZT networks to achieve on-line SHM task is important.Based on a peripheral component interconnect extensions for instrumentation(PXI)platform,an active Lamb wave and PZT network-based integrated multi-channel scanning system(PXI-ISS)is developed for the purpose of practical applications of SHM,which is compact and portable,and can scan large numbers of actuator-sensor channels and perform damage assessing automatically.A PXI-based 4 channels gain-programmable charge amplifier,an external scanning module with 276 actuator-sensor channels and integrated SHM software are proposed and discussed in detail.The experimental research on a carbon fiber composite wing box of an unmanned aerial vehicle(UAV)for verifying the functions of the PXI-ISS is mainly discussed,including the design of PZTs layer,the method of excitation frequency selection,functional test of damage imaging,stability test of the PXI-ISS,and the loading effect on signals.The experimental results have verified the stability and damage functions of this system.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
With the rapid technological innovation in materials engineering and device integration,a wide variety of textilebased wearable biosensors have emerged as promising platforms for personalized healthcare,exercise monit...With the rapid technological innovation in materials engineering and device integration,a wide variety of textilebased wearable biosensors have emerged as promising platforms for personalized healthcare,exercise monitoring,and pre-diagnostics.This paper reviews the recent progress in sweat biosensors and sensing systems integrated into textiles for wearable body status monitoring.The mechanisms of biosensors that are commonly adopted for biomarkers analysis are first introduced.The classification,fabrication methods,and applications of textile conductors in different configurations and dimensions are then summarized.Afterward,innovative strategies to achieve efficient sweat collection with textile-based sensing patches are presented,followed by an in-depth discussion on nanoengineering and system integration approaches for the enhancement of sensing performance.Finally,the challenges of textile-based sweat sensing devices associated with the device reusability,washability,stability,and fabrication reproducibility are discussed from the perspective of their practical applications in wearable healthcare.展开更多
基金supported by the National Natural Science Foundation of China(52303257,52321006,T2394480,and T2394484)the National Key R&D Program of China(Grant No.2023YFE0111500)+3 种基金Key Research&Development and Promotion of Special Project(Scientific Problem Tackling)of Henan Province(242102211090)the China Postdoctoral Science Foundation(2023TQ0300,and 2023M743171)the Postdoctoral Fellowship Program(Grade B)of China Postdoctoral Science Foundation(GZB20230666)College Student Innovation and Entrepreneurship Training Program of Zhengzhou University(202410459200)。
文摘Flexible wearable optoelectronic devices fabricated fromorganic–inorganic hybrid perovskites significantly accelerate the developmentof portable energy,biomedicine,and sensing fields,but their poor thermal stabilityhinders further applications.Conversely,all-inorganic perovskites possessexcellent thermal stability,but black-phase all-inorganic perovskite filmusually requires high-temperature annealing steps,which increases energy consumptionand is not conducive to the fabrication of flexible wearable devices.In this work,an unprecedented low-temperature fabrication of stable blackphaseCsPbI3perovskite films is demonstrated by the in situ hydrolysis reactionof diphenylphosphinic chloride additive.The released diphenyl phosphateand chloride ions during the hydrolysis reaction significantly lower the phasetransition temperature and effectively passivate the defects in the perovskitefilms,yielding high-performance photodetectors with a responsivity of 42.1 AW−1 and a detectivity of 1.3×10^(14)Jones.Furthermore,high-fidelity imageand photoplethysmography sensors are demonstrated based on the fabricated flexible wearable photodetectors.This work provides a newperspective for the low-temperature fabrication of large-area all-inorganic perovskite flexible optoelectronic devices.
文摘Structural Health Monitoring(SHM)systems play a key role in managing buildings and infrastructure by delivering vital insights into their strength and structural integrity.There is a need for more efficient techniques to detect defects,as traditional methods are often prone to human error,and this issue is also addressed through image processing(IP).In addition to IP,automated,accurate,and real-time detection of structural defects,such as cracks,corrosion,and material degradation that conventional inspection techniques may miss,is made possible by Artificial Intelligence(AI)technologies like Machine Learning(ML)and Deep Learning(DL).This review examines the integration of computer vision and AI techniques in Structural Health Monitoring(SHM),investigating their effectiveness in detecting various forms of structural deterioration.Also,it evaluates ML and DL models in SHM for their accuracy in identifying and assessing structural damage,ultimately enhancing safety,durability,and maintenance practices in the field.Key findings reveal that AI-powered approaches,especially those utilizing IP and DL models like CNNs,significantly improve detection efficiency and accuracy,with reported accuracies in various SHM tasks.However,significant research gaps remain,including challenges with the consistency,quality,and environmental resilience of image data,a notable lack of standardized models and datasets for training across diverse structures,and concerns regarding computational costs,model interpretability,and seamless integration with existing systems.Future work should focus on developing more robust models through data augmentation,transfer learning,and hybrid approaches,standardizing protocols,and fostering interdisciplinary collaboration to overcome these limitations and achieve more reliable,scalable,and affordable SHM systems.
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.
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supported by Central Guidance on Local Science and Technology Development Fund of Hebei Province(Grant No.226Z1906G)funded by Science Research Project of Hebei Education Department(CXY2024038)+1 种基金funded by Basic Research Project of Shijiazhuang University in Hebei Province(241791157A)National Natural Science Foundation of China(52206224).
文摘Considering the noise problem of the acquisition signals frommechanical transmission systems,a novel denoising method is proposed that combines Variational Mode Decomposition(VMD)with wavelet thresholding.The key innovation of this method lies in the optimization of VMD parameters K and α using the improved Horned Lizard Optimization Algorithm(IHLOA).An inertia weight parameter is introduced into the random walk strategy of HLOA,and the related formula is improved.The acquisition signal can be adaptively decomposed into some Intrinsic Mode Functions(IMFs),and the high-noise IMFs are identified based on a correlation coefficient-variance method.Further noise reduction is achieved using wavelet thresholding.The proposed method is validated using simulated signals and experimental signals,and simulation results indicate that the proposed method surpasses original VMD,Empirical Mode Decomposition(EMD),and wavelet thresholding in terms of Signal-to-Noise Ratio(SNR)and Root Mean Square Error(RMSE),and experimental results indicate that the proposedmethod can effectively remove noise in terms of three evaluationmetrics.Furthermore,comparedwith FeatureModeDecomposition(FMD)andMultichannel Singular Spectrum Analysis(MSSA),this method has a better envelope spectrum.This method not only provides a solution for noise reduction in signal processing but also holds significant potential for applications in structural health monitoring and fault diagnosis.
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Supported by open project fund of National Engineering Research Center of Digital Construction and Evaluation Technology of Urban Rail Transit(2024023).
文摘Monitoring the condition of road infrastructure is crucial for maintaining its structural integrity and ensuring safe transportation.This study proposes a deep learning framework based on Temporal Convolutional Networks(TCN)integrated with Adaptive Parametric Rectified Linear Unit(APReLU)to predict future road subbase strain trends.Our model leverages time-series strain data collected from embedded triaxial sensors within a national highway,spanning August 2021 to June 2022,to forecast strain dynamics critical for proactive maintenance planning.The TCN-APReLU architecture combines dilated causal convolutions to capture long-termdependencies and APReLU activation functions to adaptively model nonlinear strain patterns,addressing limitations of traditional ReLU in handling bidirectional strain signals(compressive and tensile).Comparative experiments demonstrate TCN-APReLU’s superior performance.These improvements highlight its enhanced accuracy in predicting strain accumulation under cyclic traffic loads,enabling maintenance teams to prioritize interventions 5-7 days before critical thresholds(e.g.,>100με)are exceeded.This work provides a robust data-driven solution for urban road health monitoring,emphasizing scalability through parallelizable convolutions and adaptability to sensor noise.Future extensions will integrate multi-modal data to further generalize predictions across diverse infrastructure scenarios.
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supported by CHANAKYA Fellowship Program of TIH Foundation for IoT&IoE(TIH-IoT)received by Dr.Vinay Chamola under Project Grant File CFP/2022/027.
文摘Millions of people throughout the world struggle with mental health disorders,but the widespread stigma associated with these issues often prevents them from seeking treatment.We propose a novel strategy that integrates Internet of Medical Things(IoMT),DAG-based hedera technology,and Artificial Intelligence(AI)to overcome these challenges.We also consider the costs of chronic diseases such as Parkinson’s and Alzheimer’s,which often require 24-hour care.Using smart monitoring tools coupled with AI algorithms that can detect early indicators of deterioration,our system aims to provide low-cost,continuous support.Since IoMT data is large in volume,we need a blockchain network with high transaction throughput without compromising the privacy of patient data.To address this concern,we propose to use Hedera technology to ensure the privacy,and security of personal mental health information,scalability and a faster transaction confirmation rate.Overall,this research paper outlines a holistic approach to mental health monitoring that respects privacy,promotes accessibility,and harnesses the potential of emerging technologies.By combining IoMT,Hedera,and AI,we offer a solution that helps break down the barriers preventing individuals from seeking mental well-being support.Furthermore,comparative analysis shows that our best-performing ML models achieve an accuracy of around 98%,which is more than 30%better than traditional models such as logistic regression。
文摘This paper aims to study a novel smart self-powered wireless lightweight (SPWL) bridge health monitoring sensor, which integrates key technologies such as large-scale, low-power wireless data transmission, environmental energy self-harvesting, and intelligent perception, and can operate stably for a long time in complex and changing environments. The self-powered system of the sensor can meet the needs of long-term bridge service performance monitoring, significantly improving the coverage and efficiency of monitoring. By optimizing the sensor system design, the maximum energy conversion of the energy harvesting unit is achieved. In order to verify the function and practicality of the new SPWL monitoring sensor, this study combined the actual bridge engineering, carried out a bridge monitoring case study, and developed an SPWL monitoring scheme based on the bridge structure principle. Compared with traditional monitoring methods, this technology significantly improves the sustainability and performance of infrastructure monitoring based on the new SPWL sensor, fully demonstrating the excellent monitoring capabilities of this type of sensor, and providing strong support for the development of intelligent transportation and intelligent infrastructure.
基金supported by the Open Fund of Magnetic Confinement Fusion Laboratory of Anhui Province(No.2024AMF04003)the Natural Science Foundation of Anhui Province(No.228085ME142)Comprehensive Research Facility for Fusion Technology(No.20180000527301001228)。
文摘This research focuses on solving the fault detection and health monitoring of high-power thyristor converter.In terms of the critical role of thyristor converter in nuclear fusion system,a method based on long short-term memory(LSTM)neural network model is proposed to monitor the operational state of the converter and accurately detect faults as they occur.By sampling and processing a large number of thyristor converter operation data,the LSTM model is trained to identify and detect abnormal state,and the power supply health status is monitored.Compared with traditional methods,LSTM model shows higher accuracy and abnormal state detection ability.The experimental results show that this method can effectively improve the reliability and safety of the thyristor converter,and provide a strong guarantee for the stable operation of the nuclear fusion reactor.
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The Social Science Fund of Hebei Province (No.200607011)the Key Science and Technology Project of Hebei Province(No.07213529)
文摘In order to more effectively assess the health status of a project, the monitoring indices in a project＇s life cycle are divided into quality index, cost index, time index, satisfaction index, and sustainable development index. Based on the feature of qualitative and quantitative indices combining, the PCA-PR （principal component analysis and pattern recognition） model is constructed. The model first analyzes the principal components of the life-cycle indices system constructed above, and picks up those principal component indices that can reflect the health status of a project at any time. Then the pattern recognition model is used to study these principal components, which means that the real time health status of the project can be divided into five lamps from a green lamp to a red one and the health status lamp of the project can be recognized by using the PR model and those principal components. Finally, the process is shown with a real example and a conclusion consistent with the actual situation is drawn. So the validity of the index system and the PCA-PR model can be confirmed.
基金the National Science Foundation through the International Collaboration Supplement of Grant No.CMS-0202320the HongKong Research Grants Council via the Competitive Earmarked Research Grant HKUST6220/01E
文摘The concept of health monitoring is a key aspect of the field of medicine that has been practiced for a long time. A commonly used diagnostic and health monitoring practice is pulse diagnosis, which can be traced back approximately five thousand years in the recorded history of China. With advances in the development of modern technology, the concept of health monitoring of a variety of engineering structures in several applications has begun to attract widespread attention. Of particular interest in this study is the health monitoring of civil structures. It seem natural, and even beneficial, that these two health-monitoring methods, one as applies to the human body and the other to civil structures, should be analyzed and compared. In this paper, the basic concepts and theories of the two monitoring methods are first discussed. Similarities are then summarized and commented upon. It is hoped that this correlation analysis may help provide structural engineers with some insights into the intrinsic concept of using pulse diagnosis in human health monitoring, which may of be some benefit in the development of modern structural health monitoring methods.
基金National High-tech Research and Development Program of China(2007AA03Z117)National Natural Science Foundation of China(50830201)Graduate Education Innovation Project of Nanjing University of Aeronautics and Astronautics of China(BCXJ09-01).
文摘The active Lamb wave and piezoelectric transducer(PZT)-based structural health monitoring(SHM)technology is a kind of efficient approach to estimate the health state of aircraft structure.In practical applications,PZT networks are needed to monitor large scale structures.Scanning many of the different PZT actuator-sensor channels within these PZT networks to achieve on-line SHM task is important.Based on a peripheral component interconnect extensions for instrumentation(PXI)platform,an active Lamb wave and PZT network-based integrated multi-channel scanning system(PXI-ISS)is developed for the purpose of practical applications of SHM,which is compact and portable,and can scan large numbers of actuator-sensor channels and perform damage assessing automatically.A PXI-based 4 channels gain-programmable charge amplifier,an external scanning module with 276 actuator-sensor channels and integrated SHM software are proposed and discussed in detail.The experimental research on a carbon fiber composite wing box of an unmanned aerial vehicle(UAV)for verifying the functions of the PXI-ISS is mainly discussed,including the design of PZTs layer,the method of excitation frequency selection,functional test of damage imaging,stability test of the PXI-ISS,and the loading effect on signals.The experimental results have verified the stability and damage functions of this system.
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supported by the National Natural Science Foundation of China(62201243)Fundamental and Applied Research Grant of Guangdong Province(2021A1515110627)+3 种基金Southern University of Science and Technology(Y01796108,Y01796208)RGC Senior Research Fellow Scheme of Hong Kong(SRFS2122-5S04)the Hong Kong Polytechnic University(1-ZVQM),RI-Wear of PolyU(1-CD44)Shenzhen Science and Technology Innovation Committee(SGDX20210823103403033).
文摘With the rapid technological innovation in materials engineering and device integration,a wide variety of textilebased wearable biosensors have emerged as promising platforms for personalized healthcare,exercise monitoring,and pre-diagnostics.This paper reviews the recent progress in sweat biosensors and sensing systems integrated into textiles for wearable body status monitoring.The mechanisms of biosensors that are commonly adopted for biomarkers analysis are first introduced.The classification,fabrication methods,and applications of textile conductors in different configurations and dimensions are then summarized.Afterward,innovative strategies to achieve efficient sweat collection with textile-based sensing patches are presented,followed by an in-depth discussion on nanoengineering and system integration approaches for the enhancement of sensing performance.Finally,the challenges of textile-based sweat sensing devices associated with the device reusability,washability,stability,and fabrication reproducibility are discussed from the perspective of their practical applications in wearable healthcare.
