Conventional optimal sensor placement(OSP)methods employ the premise that all sensors work perfectly during long-term structural monitoring.However,this premise is often difficult to fulfill in real applications due t...Conventional optimal sensor placement(OSP)methods employ the premise that all sensors work perfectly during long-term structural monitoring.However,this premise is often difficult to fulfill in real applications due to poor manufacturing and material aging of sensors,human damage,and electromagnetic interference.This paper presents a robustness-oriented OSP method that considers sensor failures.The OSP problem is designed with consideration of sensor failures to ensure that both complete vibration data collected by all sensors and incomplete vibration data caused by individual sensor failures can accurately identify structural modal parameters.A dispersion-aggregation firefly algorithm(DAFA),which is derived from the basic firefly algorithm,has been proposed to solve this complicated optimization problem.The dispersion and aggregation operators are designed to prevent falling into local optima and to rapidly converge to the global optima.The proposed methodology is confirmed by extracting the robust sensor configuration for a long-span cable-stayed bridge.The robustness of the optimal sensor configurations against sensor failure is thoroughly explored,and the performance of the proposed DAFA is extensively examined.展开更多
This paper provides a review on the development of structural monitoring in Japan, with an emphasis on the type, strategy, and utilization of monitoring systems. The review focuses on bridge and building structures us...This paper provides a review on the development of structural monitoring in Japan, with an emphasis on the type, strategy, and utilization of monitoring systems. The review focuses on bridge and building structures using vibration-based techniques. Structural monitoring systems in Japan historically started with the objective of evaluating structural responses against extreme events. In the development of structural monitoring, monitoring systems and collected data were used to verify design assumptions, update speci cations, and facilitate the ef cacy of vibration control systems. Strategies and case studies on monitoring for the design veri cation of long-span bridges and tall buildings, the performance of seismic isolation systems in building and bridges, the veri cation of structural retro t, the veri cation of structural control systems (passive, semi-active, and active), structural assessment, and damage detec- tion are described. More recently, the application of monitoring systems has been extended to facilitate ef cient operation and effective maintenance through the rationalization of risk and asset management using monitoring data. This paper also summarizes the lessons learned and feedback obtained from case studies on the structural monitoring of bridges and buildings in Japan.展开更多
Operational modal analysis is a non-destructive structural investigation that considers only the loads resulting from service conditions.This approach allows the measurement of vibrations on a given structure with no ...Operational modal analysis is a non-destructive structural investigation that considers only the loads resulting from service conditions.This approach allows the measurement of vibrations on a given structure with no need to interrupt its use.The present work aims to develop a numerical model to represent the global structural behavior of a vessel breasting dolphin using a technique that is simple and cheap in order to obtain a fast answer about the stiffness of a pier after the collision of ships with capacity up to 400,000 t.To determine the modes of vibration,one accelerometer was installed on the breasting dolphin located on the pier and a frequency domain technic was conducted over recorded data to obtain modal parameters of the structure.In situ measurements were compared to data from a finite element model based on the original structural design in order to adapt the model to accurately represent the actual behavior of the system.This allowed a reliable structural analysis that accounted for existing structural damage and imperfections.The results of the experiment presented herein are the numerical characterization of the structure,along with the structural analysis to assess the degree of damage currently observed on the system.It is noted that the dolphin subjected to ship impacts presents a reduction in stiffness of approximately10%and its global damage level can be monitored from now after new accidents.展开更多
Fatigue damage is a primary contributor to the failure of composite structures,underscoring the critical importance of monitoring its progression to ensure structural safety.This paper introduces an innovative approac...Fatigue damage is a primary contributor to the failure of composite structures,underscoring the critical importance of monitoring its progression to ensure structural safety.This paper introduces an innovative approach to fatigue damage monitoring in composite structures,leveraging a hybrid methodology that integrates the Whale Optimization Algorithm(WOA)-Backpropagation(BP)neural network with an ultrasonic guided wave feature selection algorithm.Initially,a network of piezoelectric ceramic sensors is employed to transmit and capture ultrasonic-guided waves,thereby establishing a signal space that correlates with the structural condition.Subsequently,the Relief-F algorithm is applied for signal feature extraction,culminating in the formation of a feature matrix.This matrix is then utilized to train the WOA-BP neural network,which optimizes the fatigue damage identification model globally.The proposed model’s efficacy in quantifying fatigue damage is tested against fatigue test datasets,with its performance benchmarked against the traditional BP neural network algorithm.The findings demonstrate that the WOA-BP neural network model not only surpasses the BP model in predictive accuracy but also exhibits enhanced global search capabilities.The effect of different sensor-receiver path signals on the model damage recognition results is also discussed.The results of the discussion found that the path directly through the damaged area is more accurate in modeling damage recognition compared to the path signals away from the damaged area.Consequently,the proposed monitoring method in the fatigue test dataset is adept at accurately tracking and recognizing the progression of fatigue damage.展开更多
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
Lost acceleration response reconstruction is crucial for assessing structural conditions in structural health monitoring(SHM).However,traditional methods struggle to address the reconstruction of acceleration response...Lost acceleration response reconstruction is crucial for assessing structural conditions in structural health monitoring(SHM).However,traditional methods struggle to address the reconstruction of acceleration responses with complex features,resulting in a lower reconstruction accuracy.This paper addresses this challenge by leveraging the advanced feature extraction and learning capabilities of fully convolutional networks(FCN)to achieve precise reconstruction of acceleration responses.In the designed network architecture,the incorporation of skip connections preserves low-level details of the network,greatly facilitating the flow of information and improving training efficiency and accuracy.Dropout techniques are employed to reduce computational load and enhance feature extraction.The proposed FCN model automatically extracts high-level features from the input data and establishes a nonlinearmapping relationship between the input and output responses.Finally,the accuracy of the FCN for structural response reconstructionwas evaluated using acceleration data from an experimental arch rib and comparedwith several traditional methods.Additionally,this approach was applied to reconstruct actual acceleration responses measured by an SHM system on a long-span bridge.Through parameter analysis,the feasibility and accuracy of aspects such as available response positions,the number of available channels,and multi-channel response reconstruction were explored.The results indicate that this method exhibits high-precision response reconstruction capability in both time and frequency domains.,with performance surpassing that of other networks,confirming its effectiveness in reconstructing responses under various sensor data loss scenarios.展开更多
In the fabrication and monitoring of parts in composite structures,which are being used more and more in a variety of engineering applications,the prediction and fatigue failure detection in composite materials is a d...In the fabrication and monitoring of parts in composite structures,which are being used more and more in a variety of engineering applications,the prediction and fatigue failure detection in composite materials is a difficult problem.This difficulty arises from several factors,such as the lack of a comprehensive investigation of the fatigue failure phenomena,the lack of a well-defined fatigue damage theory used for fatigue damage prediction,and the inhomogeneity of composites because of their multiple internal borders.This study investigates the fatigue behavior of carbon fiber reinforced with epoxy(CFRE)laminated composite plates under spectrum loading utilizing a uniqueDeep LearningNetwork consisting of a convolutional neural network(CNN).Themethod includes establishing Finite Element Model(FEM)in a plate model under a spectrum fatigue loading.Then,a CNN is trained for fatigue behavior prediction.The training phase produces promising results,showing the model’s performance with 94.21%accuracy,92.63%regression,and 91.55%F-score.To evaluate the model’s reliability,a comparison is made between fatigue data from the CNN and the FEM.It was found that the error band for this comparison is less than 0.3878MPa,affirming the accuracy and reliability of the proposed technique.The proposed method results converge with available experimental results in the literature,thus,the study suggests the broad applicability of this method to other different composite structures.展开更多
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
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
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
Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters accordi...Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring(SHM)system,so as to provide a scientific basis for structural damage identification and dynamic model modification.In view of this,this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters.The paper primarily introduces data-driven modal parameter recognition methods(e.g.,time-domain,frequency-domain,and time-frequency-domain methods,etc.),briefly describes damage identification methods based on the variations of modal parameters(e.g.,natural frequency,modal shapes,and curvature modal shapes,etc.)and modal validation methods(e.g.,Stability Diagram and Modal Assurance Criterion,etc.).The current status of the application of artificial intelligence(AI)methods in the direction of modal parameter recognition and damage identification is further discussed.Based on the pre-vious analysis,the main development trends of structural modal parameter recognition and damage identification methods are given to provide scientific references for the optimized design and functional upgrading of SHM systems.展开更多
As a crucial infrastructure in the transport system,the safe operation of bridges is directly related to all aspects of people’s daily lives.The development of bridge structural health monitoring technology and its a...As a crucial infrastructure in the transport system,the safe operation of bridges is directly related to all aspects of people’s daily lives.The development of bridge structural health monitoring technology and its application play an important role in ensuring the safety and extending the service life of bridges.This paper carries out in-depth research and analysis on the related technology of bridge structural health monitoring.Firstly,the existing monitoring technologies at home and abroad are sorted out,and the advantages and problems of various methods are compared and analyzed,including nondestructive testing,stress measurement,vibration characteristic identification,and other commonly used monitoring technologies.Secondly,the key technologies and equipment in the bridge health monitoring system,such as sensor technology,data acquisition,and processing technology,are introduced in detail.Finally,the development trend in the field of bridge health monitoring is prospected from both theoretical research and technical application.In the future,with the development of emerging technologies such as big data,cloud computing,and the Internet of Things,it is expected that bridge health monitoring with intelligent and systematic features will be more widely applied to provide a stronger guarantee for the safe and efficient operation of bridges.展开更多
Structural instability in underground engineering,especially in coal-rock structures,poses significant safety risks.Thus,the development of an accurate monitoring method for the health of coal-rock bodies is crucial.T...Structural instability in underground engineering,especially in coal-rock structures,poses significant safety risks.Thus,the development of an accurate monitoring method for the health of coal-rock bodies is crucial.The focus of this work is on understanding energy evolution patterns in coal-rock bodies under complex conditions by using shear,splitting,and uniaxial compression tests.We examine the changes in energy parameters during various loading stages and the effects of various failure modes,resulting in an innovative energy dissipation-based health evaluation technique for coal.Key results show that coal bodies go through transitions between strain hardening and softening mechanisms during loading,indicated by fluctuations in elastic energy and dissipation energy density.For tensile failure,the energy profile of coal shows a pattern of “high dissipation and low accumulation” before peak stress.On the other hand,shear failure is described by “high accumulation and low dissipation” in energy trends.Different failure modes correlate with an accelerated increase in the dissipation energy before destabilization,and a significant positive correlation is present between the energy dissipation rate and the stress state of the coal samples.A novel mathematical and statistical approach is developed,establishing a dissipation energy anomaly index,W,which categorizes the structural health of coal into different danger levels.This method provides a quantitative standard for early warning systems and is adaptable for monitoring structural health in complex underground engineering environments,contributing to the development of structural health monitoring technology.展开更多
This work elaborates a fast and robust structural health monitoring scheme for copying with aircraft structural fatigue.The type of noise in structural strain signals is determined by using a statistical analysis meth...This work elaborates a fast and robust structural health monitoring scheme for copying with aircraft structural fatigue.The type of noise in structural strain signals is determined by using a statistical analysis method,which can be regarded as a mixture of Gaussian-like(tiny hairy signals)and impulse-like noise(single signals with anomalous movements in peak and valley areas).Based on this,a least squares filtering method is employed to preprocess strain signals.To precisely eliminate noise or outliers in strain signals,we propose a novel variational model to generate step signals instead of strain ones.Expert judgments are employed to classify the generated signals.Based on the classification labels,whether the aircraft is structurally healthy is accurately judged.By taking the generated step count vectors and labels as an input,a discriminative neural network is proposed to realize automatic signal discrimination.The network output means whether the aircraft structure is healthy or not.Experimental results demonstrate that the proposed scheme is effective and efficient,as well as achieves more satisfactory results than other peers.展开更多
The possibility of determining the integrity of a real structure subjected to non-invasive and non-destructive monitoring,such as that carried out by a series of accelerometers placed on the structure,is certainly a g...The possibility of determining the integrity of a real structure subjected to non-invasive and non-destructive monitoring,such as that carried out by a series of accelerometers placed on the structure,is certainly a goal of extreme and current interest.In the present work,the results obtained from the processing of experimental data of a real structure are shown.The analyzed structure is a lattice structure approximately 9 m high,monitored with 18 uniaxial accelerometers positioned in pairs on 9 different levels.The data used refer to continuous monitoring that lasted for a total of 1 year,during which minor damage was caused to the structure by alternatively removing some bracings and repositioning them in the structure.Two methodologies detecting damage based on decomposition techniques of the acquired data were used and tested,as well as a methodology combining the two techniques.The results obtained are extremely interesting,as all the minor damage caused to the structure was identified by the processing methods used,based solely on the monitored data and without any knowledge of the real structure being analyzed.The results use 15 acquisitions in environmental conditions lasting 10 min each,a reasonable amount of time to get immediate feedback on possible damage to the structure.展开更多
Addressing the current challenges in transforming pixel displacement into physical displacement in visual monitoring technologies,as well as the inability to achieve precise full-field monitoring,this paper proposes a...Addressing the current challenges in transforming pixel displacement into physical displacement in visual monitoring technologies,as well as the inability to achieve precise full-field monitoring,this paper proposes a method for identifying the structural dynamic characteristics of wind turbines based on visual monitoring data fusion.Firstly,the Lucas-Kanade Tomasi(LKT)optical flow method and a multi-region of interest(ROI)monitoring structure are employed to track pixel displacements,which are subsequently subjected to band pass filtering and resampling operations.Secondly,the actual displacement time history is derived through double integration of the acquired acceleration data and subsequent band pass filtering.The scale factor is obtained by applying the least squares method to compare the visual displacement with the displacement derived from double integration of the acceleration data.Based on this,the multi-point displacement time histories under physical coordinates are obtained using the vision data and the scale factor.Subsequently,when visual monitoring of displacements becomes impossible due to issues such as image blurring or lens occlusion,the structural vibration equation and boundary condition constraints,among other key parameters,are employed to predict the displacements at unknown monitoring points,thereby enabling full-field displacement monitoring and dynamic characteristic testing of the structure.Finally,a small-scale shaking table test was conducted on a simulated wind turbine structure undergoing shutdown to validate the dynamic characteristics of the proposed method through test verification.The research results indicate that the proposed method achieves a time-domain error within the submillimeter range and a frequency-domain accuracy of over 99%,effectively monitoring the full-field structural dynamic characteristics of wind turbines and providing a basis for the condition assessment of wind turbine structures.展开更多
There are multiple types of risks involved in the service of long-span railway bridges.Classical methods are difficult to provide targeted alarm information according to different situations of load anomalies and stru...There are multiple types of risks involved in the service of long-span railway bridges.Classical methods are difficult to provide targeted alarm information according to different situations of load anomalies and structural anomalies.To accurately alarm different risks of long-span railway bridges by structural health monitoring systems,this paper proposes a cross-cooperative alarm method using principal and secondary indicators during high-wind periods.It provides the prior criterion for monitoring systems under special conditions,defining the principal and secondary indicators,alarm levels,and thresholds based on the relationship between dynamic equilibrium equations and multiple linear regression analysis.Analysis of one-year monitoring data from a longspan railway cable-stayed bridge shows that the 10-min average cross-bridge wind speed(excitation indicator)can be selected as the principal indicator,while lateral displacement(response indicator)can serve as the secondary indicator.The threshold levels of the secondary indicator prioritize the safety of bridge operation(mainly aiming at the safety of trains traversing bridges),with values significantly lower than structural safety thresholds.This approach enhances alarm timeliness and effectively distinguishes between load anomalies,structural anomalies,and equipment failures.Consequently,it improves alarm accuracy and provides timely decision support for bridge maintenance,train traversing,and emergency treatment.展开更多
Construction failures caused by unforeseen circumstances, such as natural disasters, environmental degradation, and structural weaknesses, present significant challenges in achieving durability, safety, and sustainabi...Construction failures caused by unforeseen circumstances, such as natural disasters, environmental degradation, and structural weaknesses, present significant challenges in achieving durability, safety, and sustainability. This research addresses these challenges through the development of advanced emergency rescue systems incorporating wood-derived nanomaterials and IoT-enabled Structural Health Monitoring (SHM) technologies. The use of nanocellulose which demonstrates outstanding mechanical capabilities and biodegradability alongside high resilience allowed developers to design modular rescue systems that function effectively even under challenging conditions while providing real-time failure protection. Experimental data from testing showed that the replacement system strengthened load-bearing limits by 20% while enhancing impact tolerance by 30% and decreasing lifecycle carbon footprints by 60% against conventional methods. FEA results alongside dynamic simulations established that the system maintains its strength across seismic events and thermal variations and environmental conditions. SHM systems that leverage the Internet of Things Platform revealed 95% accuracy rates in detecting anomalies while improving response speed by 30% for predictive maintenance operations. The innovative solutions support the special issue’s direction to push structural transformation through durable designs and creative materials with preventive failure solutions. The proposed solutions work together toward creating resilient infrastructure systems which resist unexpected stressors and environmental damage.展开更多
Curved geostructures,such as tunnels,are commonly encountered in geotechnical engineering and are critical to maintaining structural stability.Ensuring their proper performance through field monitoring during their se...Curved geostructures,such as tunnels,are commonly encountered in geotechnical engineering and are critical to maintaining structural stability.Ensuring their proper performance through field monitoring during their service life is essential for the overall functionality of geotechnical infrastructure.Distributed Brillouin sensing(DBS)is increasingly applied in geotechnical projects due to its ability to acquire spatially continuous strain and temperature distributions over distances of up to 150 km using a single optical fibre.However,limited by the complex operations of distributed optic fibre sensing(DFOS)sensors in curved structures,previous reports about exploiting DBS in geotechnical structural health monitoring(SHM)have mostly been focused on flat surfaces.The lack of suitable DFOS installation methods matched to the spatial characteristics of continuous monitoring is one of the major factors that hinder the further application of this technique in curved structures.This review paper starts with a brief introduction of the fundamental working principle of DBS and the inherent limitations of DBS being used on monitoring curved surfaces.Subsequently,the state-of-the-art installation methods of optical fibres in curved structures are reviewed and compared to address the most suitable scenario of each method and their advantages and disadvantages.The installation challenges of optical fibres that can highly affect measurement accuracy are also discussed in the paper.展开更多
基金The National Natural Science Foundation of China(No.51978243,52578360).
文摘Conventional optimal sensor placement(OSP)methods employ the premise that all sensors work perfectly during long-term structural monitoring.However,this premise is often difficult to fulfill in real applications due to poor manufacturing and material aging of sensors,human damage,and electromagnetic interference.This paper presents a robustness-oriented OSP method that considers sensor failures.The OSP problem is designed with consideration of sensor failures to ensure that both complete vibration data collected by all sensors and incomplete vibration data caused by individual sensor failures can accurately identify structural modal parameters.A dispersion-aggregation firefly algorithm(DAFA),which is derived from the basic firefly algorithm,has been proposed to solve this complicated optimization problem.The dispersion and aggregation operators are designed to prevent falling into local optima and to rapidly converge to the global optima.The proposed methodology is confirmed by extracting the robust sensor configuration for a long-span cable-stayed bridge.The robustness of the optimal sensor configurations against sensor failure is thoroughly explored,and the performance of the proposed DAFA is extensively examined.
文摘This paper provides a review on the development of structural monitoring in Japan, with an emphasis on the type, strategy, and utilization of monitoring systems. The review focuses on bridge and building structures using vibration-based techniques. Structural monitoring systems in Japan historically started with the objective of evaluating structural responses against extreme events. In the development of structural monitoring, monitoring systems and collected data were used to verify design assumptions, update speci cations, and facilitate the ef cacy of vibration control systems. Strategies and case studies on monitoring for the design veri cation of long-span bridges and tall buildings, the performance of seismic isolation systems in building and bridges, the veri cation of structural retro t, the veri cation of structural control systems (passive, semi-active, and active), structural assessment, and damage detec- tion are described. More recently, the application of monitoring systems has been extended to facilitate ef cient operation and effective maintenance through the rationalization of risk and asset management using monitoring data. This paper also summarizes the lessons learned and feedback obtained from case studies on the structural monitoring of bridges and buildings in Japan.
文摘Operational modal analysis is a non-destructive structural investigation that considers only the loads resulting from service conditions.This approach allows the measurement of vibrations on a given structure with no need to interrupt its use.The present work aims to develop a numerical model to represent the global structural behavior of a vessel breasting dolphin using a technique that is simple and cheap in order to obtain a fast answer about the stiffness of a pier after the collision of ships with capacity up to 400,000 t.To determine the modes of vibration,one accelerometer was installed on the breasting dolphin located on the pier and a frequency domain technic was conducted over recorded data to obtain modal parameters of the structure.In situ measurements were compared to data from a finite element model based on the original structural design in order to adapt the model to accurately represent the actual behavior of the system.This allowed a reliable structural analysis that accounted for existing structural damage and imperfections.The results of the experiment presented herein are the numerical characterization of the structure,along with the structural analysis to assess the degree of damage currently observed on the system.It is noted that the dolphin subjected to ship impacts presents a reduction in stiffness of approximately10%and its global damage level can be monitored from now after new accidents.
基金funded by the Key Program of the National Natural Science Foundation of China(U2341235)Youth Fund for Basic Research Program of Jiangnan University(JUSRP123003)+2 种基金Postgraduate Research&Practice Innovation Program of Jiangsu Province(SJCX23_1237)the National Key R&D Program of China(2018YFA0702800)Key Technologies R&D Program of CNBM(2023SJYL01).
文摘Fatigue damage is a primary contributor to the failure of composite structures,underscoring the critical importance of monitoring its progression to ensure structural safety.This paper introduces an innovative approach to fatigue damage monitoring in composite structures,leveraging a hybrid methodology that integrates the Whale Optimization Algorithm(WOA)-Backpropagation(BP)neural network with an ultrasonic guided wave feature selection algorithm.Initially,a network of piezoelectric ceramic sensors is employed to transmit and capture ultrasonic-guided waves,thereby establishing a signal space that correlates with the structural condition.Subsequently,the Relief-F algorithm is applied for signal feature extraction,culminating in the formation of a feature matrix.This matrix is then utilized to train the WOA-BP neural network,which optimizes the fatigue damage identification model globally.The proposed model’s efficacy in quantifying fatigue damage is tested against fatigue test datasets,with its performance benchmarked against the traditional BP neural network algorithm.The findings demonstrate that the WOA-BP neural network model not only surpasses the BP model in predictive accuracy but also exhibits enhanced global search capabilities.The effect of different sensor-receiver path signals on the model damage recognition results is also discussed.The results of the discussion found that the path directly through the damaged area is more accurate in modeling damage recognition compared to the path signals away from the damaged area.Consequently,the proposed monitoring method in the fatigue test dataset is adept at accurately tracking and recognizing the progression of fatigue damage.
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National Natural Science Foundation of China(Grant Nos.52408314,52278292)Chongqing Outstanding Youth Science Foundation(Grant No.CSTB2023NSCQ-JQX0029)+1 种基金Science and Technology Project of Sichuan Provincial Transportation Department(Grant No.2023-ZL-03)Science and Technology Project of Guizhou Provincial Transportation Department(Grant No.2024-122-018).
文摘Lost acceleration response reconstruction is crucial for assessing structural conditions in structural health monitoring(SHM).However,traditional methods struggle to address the reconstruction of acceleration responses with complex features,resulting in a lower reconstruction accuracy.This paper addresses this challenge by leveraging the advanced feature extraction and learning capabilities of fully convolutional networks(FCN)to achieve precise reconstruction of acceleration responses.In the designed network architecture,the incorporation of skip connections preserves low-level details of the network,greatly facilitating the flow of information and improving training efficiency and accuracy.Dropout techniques are employed to reduce computational load and enhance feature extraction.The proposed FCN model automatically extracts high-level features from the input data and establishes a nonlinearmapping relationship between the input and output responses.Finally,the accuracy of the FCN for structural response reconstructionwas evaluated using acceleration data from an experimental arch rib and comparedwith several traditional methods.Additionally,this approach was applied to reconstruct actual acceleration responses measured by an SHM system on a long-span bridge.Through parameter analysis,the feasibility and accuracy of aspects such as available response positions,the number of available channels,and multi-channel response reconstruction were explored.The results indicate that this method exhibits high-precision response reconstruction capability in both time and frequency domains.,with performance surpassing that of other networks,confirming its effectiveness in reconstructing responses under various sensor data loss scenarios.
文摘In the fabrication and monitoring of parts in composite structures,which are being used more and more in a variety of engineering applications,the prediction and fatigue failure detection in composite materials is a difficult problem.This difficulty arises from several factors,such as the lack of a comprehensive investigation of the fatigue failure phenomena,the lack of a well-defined fatigue damage theory used for fatigue damage prediction,and the inhomogeneity of composites because of their multiple internal borders.This study investigates the fatigue behavior of carbon fiber reinforced with epoxy(CFRE)laminated composite plates under spectrum loading utilizing a uniqueDeep LearningNetwork consisting of a convolutional neural network(CNN).Themethod includes establishing Finite Element Model(FEM)in a plate model under a spectrum fatigue loading.Then,a CNN is trained for fatigue behavior prediction.The training phase produces promising results,showing the model’s performance with 94.21%accuracy,92.63%regression,and 91.55%F-score.To evaluate the model’s reliability,a comparison is made between fatigue data from the CNN and the FEM.It was found that the error band for this comparison is less than 0.3878MPa,affirming the accuracy and reliability of the proposed technique.The proposed method results converge with available experimental results in the literature,thus,the study suggests the broad applicability of this method to other different composite structures.
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 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.
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.
基金supported by the Innovation Foundation of Provincial Education Department of Gansu(2024B-005)the Gansu Province National Science Foundation(22YF7GA182)the Fundamental Research Funds for the Central Universities(No.lzujbky2022-kb01)。
文摘Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring(SHM)system,so as to provide a scientific basis for structural damage identification and dynamic model modification.In view of this,this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters.The paper primarily introduces data-driven modal parameter recognition methods(e.g.,time-domain,frequency-domain,and time-frequency-domain methods,etc.),briefly describes damage identification methods based on the variations of modal parameters(e.g.,natural frequency,modal shapes,and curvature modal shapes,etc.)and modal validation methods(e.g.,Stability Diagram and Modal Assurance Criterion,etc.).The current status of the application of artificial intelligence(AI)methods in the direction of modal parameter recognition and damage identification is further discussed.Based on the pre-vious analysis,the main development trends of structural modal parameter recognition and damage identification methods are given to provide scientific references for the optimized design and functional upgrading of SHM systems.
文摘As a crucial infrastructure in the transport system,the safe operation of bridges is directly related to all aspects of people’s daily lives.The development of bridge structural health monitoring technology and its application play an important role in ensuring the safety and extending the service life of bridges.This paper carries out in-depth research and analysis on the related technology of bridge structural health monitoring.Firstly,the existing monitoring technologies at home and abroad are sorted out,and the advantages and problems of various methods are compared and analyzed,including nondestructive testing,stress measurement,vibration characteristic identification,and other commonly used monitoring technologies.Secondly,the key technologies and equipment in the bridge health monitoring system,such as sensor technology,data acquisition,and processing technology,are introduced in detail.Finally,the development trend in the field of bridge health monitoring is prospected from both theoretical research and technical application.In the future,with the development of emerging technologies such as big data,cloud computing,and the Internet of Things,it is expected that bridge health monitoring with intelligent and systematic features will be more widely applied to provide a stronger guarantee for the safe and efficient operation of bridges.
基金financially supported by the National Natural Science Foundation of China(Nos.52011530037 and 51904019)。
文摘Structural instability in underground engineering,especially in coal-rock structures,poses significant safety risks.Thus,the development of an accurate monitoring method for the health of coal-rock bodies is crucial.The focus of this work is on understanding energy evolution patterns in coal-rock bodies under complex conditions by using shear,splitting,and uniaxial compression tests.We examine the changes in energy parameters during various loading stages and the effects of various failure modes,resulting in an innovative energy dissipation-based health evaluation technique for coal.Key results show that coal bodies go through transitions between strain hardening and softening mechanisms during loading,indicated by fluctuations in elastic energy and dissipation energy density.For tensile failure,the energy profile of coal shows a pattern of “high dissipation and low accumulation” before peak stress.On the other hand,shear failure is described by “high accumulation and low dissipation” in energy trends.Different failure modes correlate with an accelerated increase in the dissipation energy before destabilization,and a significant positive correlation is present between the energy dissipation rate and the stress state of the coal samples.A novel mathematical and statistical approach is developed,establishing a dissipation energy anomaly index,W,which categorizes the structural health of coal into different danger levels.This method provides a quantitative standard for early warning systems and is adaptable for monitoring structural health in complex underground engineering environments,contributing to the development of structural health monitoring technology.
文摘This work elaborates a fast and robust structural health monitoring scheme for copying with aircraft structural fatigue.The type of noise in structural strain signals is determined by using a statistical analysis method,which can be regarded as a mixture of Gaussian-like(tiny hairy signals)and impulse-like noise(single signals with anomalous movements in peak and valley areas).Based on this,a least squares filtering method is employed to preprocess strain signals.To precisely eliminate noise or outliers in strain signals,we propose a novel variational model to generate step signals instead of strain ones.Expert judgments are employed to classify the generated signals.Based on the classification labels,whether the aircraft is structurally healthy is accurately judged.By taking the generated step count vectors and labels as an input,a discriminative neural network is proposed to realize automatic signal discrimination.The network output means whether the aircraft structure is healthy or not.Experimental results demonstrate that the proposed scheme is effective and efficient,as well as achieves more satisfactory results than other peers.
基金The author N.I.Giannoccaro received funds from the Department of Innovation Engineering,University of Salento,for acquiring the tool Structural Health Monitoring.
文摘The possibility of determining the integrity of a real structure subjected to non-invasive and non-destructive monitoring,such as that carried out by a series of accelerometers placed on the structure,is certainly a goal of extreme and current interest.In the present work,the results obtained from the processing of experimental data of a real structure are shown.The analyzed structure is a lattice structure approximately 9 m high,monitored with 18 uniaxial accelerometers positioned in pairs on 9 different levels.The data used refer to continuous monitoring that lasted for a total of 1 year,during which minor damage was caused to the structure by alternatively removing some bracings and repositioning them in the structure.Two methodologies detecting damage based on decomposition techniques of the acquired data were used and tested,as well as a methodology combining the two techniques.The results obtained are extremely interesting,as all the minor damage caused to the structure was identified by the processing methods used,based solely on the monitored data and without any knowledge of the real structure being analyzed.The results use 15 acquisitions in environmental conditions lasting 10 min each,a reasonable amount of time to get immediate feedback on possible damage to the structure.
基金supported by the National Science Foundation of China(Grant Nos.52068049 and 51908266)the Science Fund for Distinguished Young Scholars of Gansu Province(No.21JR7RA267)Hongliu Outstanding Young Talents Program of Lanzhou University of Technology.
文摘Addressing the current challenges in transforming pixel displacement into physical displacement in visual monitoring technologies,as well as the inability to achieve precise full-field monitoring,this paper proposes a method for identifying the structural dynamic characteristics of wind turbines based on visual monitoring data fusion.Firstly,the Lucas-Kanade Tomasi(LKT)optical flow method and a multi-region of interest(ROI)monitoring structure are employed to track pixel displacements,which are subsequently subjected to band pass filtering and resampling operations.Secondly,the actual displacement time history is derived through double integration of the acquired acceleration data and subsequent band pass filtering.The scale factor is obtained by applying the least squares method to compare the visual displacement with the displacement derived from double integration of the acceleration data.Based on this,the multi-point displacement time histories under physical coordinates are obtained using the vision data and the scale factor.Subsequently,when visual monitoring of displacements becomes impossible due to issues such as image blurring or lens occlusion,the structural vibration equation and boundary condition constraints,among other key parameters,are employed to predict the displacements at unknown monitoring points,thereby enabling full-field displacement monitoring and dynamic characteristic testing of the structure.Finally,a small-scale shaking table test was conducted on a simulated wind turbine structure undergoing shutdown to validate the dynamic characteristics of the proposed method through test verification.The research results indicate that the proposed method achieves a time-domain error within the submillimeter range and a frequency-domain accuracy of over 99%,effectively monitoring the full-field structural dynamic characteristics of wind turbines and providing a basis for the condition assessment of wind turbine structures.
基金supported by the National Natural Science Foundation of China(Grants U23A20660,52008099,and 52378288)the Major Science and Technology Project of Yunnan Province,China(Grant 202502AD080007)the China Railway Engineering Corporation Science and Technology Research and Development Project(Grant 2022-Key-44).
文摘There are multiple types of risks involved in the service of long-span railway bridges.Classical methods are difficult to provide targeted alarm information according to different situations of load anomalies and structural anomalies.To accurately alarm different risks of long-span railway bridges by structural health monitoring systems,this paper proposes a cross-cooperative alarm method using principal and secondary indicators during high-wind periods.It provides the prior criterion for monitoring systems under special conditions,defining the principal and secondary indicators,alarm levels,and thresholds based on the relationship between dynamic equilibrium equations and multiple linear regression analysis.Analysis of one-year monitoring data from a longspan railway cable-stayed bridge shows that the 10-min average cross-bridge wind speed(excitation indicator)can be selected as the principal indicator,while lateral displacement(response indicator)can serve as the secondary indicator.The threshold levels of the secondary indicator prioritize the safety of bridge operation(mainly aiming at the safety of trains traversing bridges),with values significantly lower than structural safety thresholds.This approach enhances alarm timeliness and effectively distinguishes between load anomalies,structural anomalies,and equipment failures.Consequently,it improves alarm accuracy and provides timely decision support for bridge maintenance,train traversing,and emergency treatment.
文摘Construction failures caused by unforeseen circumstances, such as natural disasters, environmental degradation, and structural weaknesses, present significant challenges in achieving durability, safety, and sustainability. This research addresses these challenges through the development of advanced emergency rescue systems incorporating wood-derived nanomaterials and IoT-enabled Structural Health Monitoring (SHM) technologies. The use of nanocellulose which demonstrates outstanding mechanical capabilities and biodegradability alongside high resilience allowed developers to design modular rescue systems that function effectively even under challenging conditions while providing real-time failure protection. Experimental data from testing showed that the replacement system strengthened load-bearing limits by 20% while enhancing impact tolerance by 30% and decreasing lifecycle carbon footprints by 60% against conventional methods. FEA results alongside dynamic simulations established that the system maintains its strength across seismic events and thermal variations and environmental conditions. SHM systems that leverage the Internet of Things Platform revealed 95% accuracy rates in detecting anomalies while improving response speed by 30% for predictive maintenance operations. The innovative solutions support the special issue’s direction to push structural transformation through durable designs and creative materials with preventive failure solutions. The proposed solutions work together toward creating resilient infrastructure systems which resist unexpected stressors and environmental damage.
基金support provided by Science Foundation Ireland Frontiers for the Future Programme,21/FFP-P/10090.
文摘Curved geostructures,such as tunnels,are commonly encountered in geotechnical engineering and are critical to maintaining structural stability.Ensuring their proper performance through field monitoring during their service life is essential for the overall functionality of geotechnical infrastructure.Distributed Brillouin sensing(DBS)is increasingly applied in geotechnical projects due to its ability to acquire spatially continuous strain and temperature distributions over distances of up to 150 km using a single optical fibre.However,limited by the complex operations of distributed optic fibre sensing(DFOS)sensors in curved structures,previous reports about exploiting DBS in geotechnical structural health monitoring(SHM)have mostly been focused on flat surfaces.The lack of suitable DFOS installation methods matched to the spatial characteristics of continuous monitoring is one of the major factors that hinder the further application of this technique in curved structures.This review paper starts with a brief introduction of the fundamental working principle of DBS and the inherent limitations of DBS being used on monitoring curved surfaces.Subsequently,the state-of-the-art installation methods of optical fibres in curved structures are reviewed and compared to address the most suitable scenario of each method and their advantages and disadvantages.The installation challenges of optical fibres that can highly affect measurement accuracy are also discussed in the paper.
