Current damage detection methods based on model updating and sensitivity Jacobian matrixes show a low convergence ratio and computational efficiency for online calculations.The aim of this paper is to construct a real...Current damage detection methods based on model updating and sensitivity Jacobian matrixes show a low convergence ratio and computational efficiency for online calculations.The aim of this paper is to construct a real-time automated damage detection method by developing a theory-assisted adaptive mutiagent twin delayed deep deterministic(TA2-MATD3)policy gradient algorithm.First,the theoretical framework of reinforcement-learning-driven damage detection is established.To address the disadvantages of traditional mutiagent twin delayed deep deterministic(MATD3)method,the theory-assisted mechanism and the adaptive experience playback mechanism are introduced.Moreover,a historical residential house built in 1889 was taken as an example,using its 12-month structural health monitoring data.TA2-MATD3 was compared with existing damage detection methods in terms of the convergence ratio,online computing efficiency,and damage detection accuracy.The results show that the computational efficiency of TA2-MATD3 is approximately 117–160 times that of the traditional methods.The convergence ratio of damage detection on the training set is approximately 97%,and that on the test set is in the range of 86.2%–91.9%.In addition,the main apparent damages found in the field survey were identified by TA2-MATD3.The results indicate that the proposed method can significantly improve the online computing efficiency and damage detection accuracy.This research can provide novel perspectives for the use of reinforcement learning methods to conduct damage detection in online structural health monitoring.展开更多
There is a problem of real-time detection difficulty in road surface damage detection. This paper proposes an improved lightweight model based on you only look once version 5(YOLOv5). Firstly, this paper fully utilize...There is a problem of real-time detection difficulty in road surface damage detection. This paper proposes an improved lightweight model based on you only look once version 5(YOLOv5). Firstly, this paper fully utilized the convolutional neural network(CNN) + ghosting bottleneck(G_bneck) architecture to reduce redundant feature maps. Afterwards, we upgraded the original upsampling algorithm to content-aware reassembly of features(CARAFE) and increased the receptive field. Finally, we replaced the spatial pyramid pooling fast(SPPF) module with the basic receptive field block(Basic RFB) pooling module and added dilated convolution. After comparative experiments, we can see that the number of parameters and model size of the improved algorithm in this paper have been reduced by nearly half compared to the YOLOv5s. The frame rate per second(FPS) has been increased by 3.25 times. The mean average precision(m AP@0.5: 0.95) has increased by 8%—17% compared to other lightweight algorithms.展开更多
Drive-by techniques for bridge health monitoring have drawn increasing attention from researchers and practitioners,in the attempt to make bridge condition-based monitoring more cost-efficient.In this work,the authors...Drive-by techniques for bridge health monitoring have drawn increasing attention from researchers and practitioners,in the attempt to make bridge condition-based monitoring more cost-efficient.In this work,the authors propose a drive-by approach that takes advantage from bogie vertical accelerations to assess bridge health status.To do so,continuous wavelet transform is combined with multiple sparse autoencoders that allow for damage detection and localization across bridge span.According to authors’best knowledge,this is the first case in which an unsupervised technique,which relies on the use of sparse autoencoders,is used to localize damages.The bridge considered in this work is a Warren steel truss bridge,whose finite element model is referred to an actual structure,belonging to the Italian railway line.To investigate damage detection and localization performances,different operational variables are accounted for:train weight,forward speed and track irregularity evolution in time.Two configurations for the virtual measuring channels were investigated:as a result,better performances were obtained by exploiting the vertical accelerations of both the bogies of the leading coach instead of using only one single acceleration signal.展开更多
High-speed railway bridges are essential components of any railway transportation system that should keep adequate levels of serviceability and safety.In this context,drive-by methodologies have emerged as a feasible ...High-speed railway bridges are essential components of any railway transportation system that should keep adequate levels of serviceability and safety.In this context,drive-by methodologies have emerged as a feasible and cost-effective monitor-ing solution for detecting damage on railway bridges while minimizing train operation interruptions.Moreover,integrating advanced sensor technologies and machine learning algorithms has significantly enhanced structural health monitoring(SHM)for bridges.Despite being increasingly used in traditional SHM applications,studies using autoencoders within drive-by methodologies are rare,especially in the railway field.This study presents a novel approach for drive-by damage detection in HSR bridges.The methodology relies on acceleration records collected from multiple bridge crossings by an operational train equipped with onboard sensors.Log-Mel spectrogram features derived from the acceleration records are used together with sparse autoencoders for computing statistical distribution-based damage indexes.Numerical simulations were performed on a 3D vehicle-track-bridge interaction system model implemented in Matlab to evaluate the robustness and effectiveness of the proposed approach,considering several damage scenarios,vehicle speeds,and environmental and operational variations,such as multiple track irregularities and varying measurement noise.The results show that the pro-posed approach can successfully detect damages,as well as characterize their severity,especially for very early-stage dam-ages.This demonstrates the high potential of applying Mel-frequency damage-sensitive features associated with machine learning algorithms in the drive-by condition assessment of high-speed railway bridges.展开更多
The current study examines damage detection in fluid-conveying pipes supported on a Pasternak foundation.This study proposes a novel method that uses the matching pursuit(MP)algorithm for damage detection.The governin...The current study examines damage detection in fluid-conveying pipes supported on a Pasternak foundation.This study proposes a novel method that uses the matching pursuit(MP)algorithm for damage detection.The governing equations of motion for the pipe are derived using Hamilton’s principle.The finite element method,combined with the Galerkin approach,is employed to obtain the mass,damping,and stiffness matrices.To identify damage locations through pipe mode-shape decomposition,an index called the“matching pursuit residual”is introduced as a novel contribution of this study.The proposed method facilitates damage detection at various levels and locations under different boundary conditions.The findings demonstrate that the MP residual damage index can accurately localize damage in the pipes.Furthermore,the results of the numerical and experimental tests showcase the efficiency of the proposed method,highlighting that the MP signal approximation algorithm effectively detects damage in structures.展开更多
A deep-sea riser is a crucial component of the mining system used to lift seafloor mineral resources to the vessel.Even minor damage to the riser can lead to substantial financial losses,environmental impacts,and safe...A deep-sea riser is a crucial component of the mining system used to lift seafloor mineral resources to the vessel.Even minor damage to the riser can lead to substantial financial losses,environmental impacts,and safety hazards.However,identifying modal parameters for structural health monitoring remains a major challenge due to its large deformations and flexibility.Vibration signal-based methods are essential for detecting damage and enabling timely maintenance to minimize losses.However,accurately extracting features from one-dimensional(1D)signals is often hindered by various environmental factors and measurement noises.To address this challenge,a novel approach based on a residual convolutional auto-encoder(RCAE)is proposed for detecting damage in deep-sea mining risers,incorporating a data fusion strategy.First,principal component analysis(PCA)is applied to reduce environmental fluctuations and fuse multisensor strain readings.Subsequently,a 1D-RCAE is used to extract damage-sensitive features(DSFs)from the fused dataset.A Mahalanobis distance indicator is established to compare the DSFs of the testing and healthy risers.The specific threshold for these distances is determined using the 3σcriterion,which is employed to assess whether damage has occurred in the testing riser.The effectiveness and robustness of the proposed approach are verified through numerical simulations of a 500-m riser and experimental tests on a 6-m riser.Moreover,the impact of contaminated noise and environmental fluctuations is examined.Results show that the proposed PCA-1D-RCAE approach can effectively detect damage and is resilient to measurement noise and environmental fluctuations.The accuracy exceeds 98%under noise-free conditions and remains above 90%even with 10 dB noise.This novel approach has the potential to establish a new standard for evaluating the health and integrity of risers during mining operations,thereby reducing the high costs and risks associated with failures.Maintenance activities can be scheduled more efficiently by enabling early and accurate detection of riser damage,minimizing downtime and avoiding catastrophic failures.展开更多
Structural damage detection is hard to conduct in large-scale civil structures due to enormous structural data and insufficient damage features.To improve this situation,a damage detection method based on model reduct...Structural damage detection is hard to conduct in large-scale civil structures due to enormous structural data and insufficient damage features.To improve this situation,a damage detection method based on model reduction and response reconstruction is presented.Based on the framework of two-step model updating including substructure-level localization and element-level detection,the response reconstruction strategy with an improved sensitivity algorithm is presented to conveniently complement modal information and promote the reliability of model updating.In the iteration process,the reconstructed response is involved in the sensitivity algorithm as a reconstruction-related item.Besides,model reduction is applied to reduce computational degrees of freedom(DOFs)in each detection step.A numerical truss bridge is modelled to vindicate the effectiveness and efficiency of the method.The results showed that the presented method reduces the requirement for installed sensors while improving efficiency and ensuring accuracy of damage detection compared to traditional methods.展开更多
Multi-source information fusion （MSIF） is imported into structural damage diagnosis methods to improve the validity of damage detection. After the introduction of the basic theory, the function model, classification...Multi-source information fusion （MSIF） is imported into structural damage diagnosis methods to improve the validity of damage detection. After the introduction of the basic theory, the function model, classifications and mathematical methods of MSIF, a structural damage detection method based on MSIF is presented, which is to fuse two or more damage character vectors from different structural damage diagnosis methods on the character-level. In an experiment of concrete plates, modal information is measured and analyzed. The structural damage detection method based on MSIF is taken to localize cracks of concrete plates and it is proved to be effective. Results of damage detection by the method based on MSIF are compared with those from the modal strain energy method and the flexibility method. Damage, which can hardly be detected by using the single damage identification method, can be diagnosed by the damage detection method based on the character-level MSIF technique. Meanwhile multi-location damage can be identified by the method based on MSIF. This method is sensitive to structural damage and different mathematical methods for MSIF have different preconditions and applicabilities for diversified structures. How to choose mathematical methods for MSIF should be discussed in detail in health monitoring systems of actual structures.展开更多
The primary objective of this paper is to develop output only modal identification and structural damage detection. Identification of multi-degree of freedom （MDOF） linear time invariant （LTI） and linear time vari...The primary objective of this paper is to develop output only modal identification and structural damage detection. Identification of multi-degree of freedom （MDOF） linear time invariant （LTI） and linear time variant （LTV--due to damage） systems based on Time-frequency （TF） techniques--such as short-time Fourier transform （STFT）, empirical mode decomposition （EMD）, and wavelets--is proposed. STFT, EMD, and wavelet methods developed to date are reviewed in detail. In addition a Hilbert transform （HT） approach to determine frequency and damping is also presented. In this paper, STFT, EMD, HT and wavelet techniques are developed for decomposition of free vibration response of MDOF systems into their modal components. Once the modal components are obtained, each one is processed using Hilbert transform to obtain the modal frequency and damping ratios. In addition, the ratio of modal components at different degrees of freedom facilitate determination of mode shape. In cases with output only modal identification using ambient/random response, the random decrement technique is used to obtain free vibration response. The advantage of TF techniques is that they arc signal based; hence, can be used for output only modal identification. A three degree of freedom 1：10 scale model test structure is used to validate the proposed output only modal identification techniques based on STFT, EMD, HT, wavelets. Both measured free vibration and forced vibration （white noise） response are considered. The secondary objective of this paper is to show the relative ease with which the TF techniques can be used for modal identification and their potential for real world applications where output only identification is essential. Recorded ambient vibration data processed using techniques such as the random decrement technique can be used to obtain the free vibration response, so that further processing using TF based modal identification can be performed.展开更多
A novel structural damage detection method with a new damage index,i.e.,the statistical moment-based damage detection（SMBDD） method in the frequency domain,has been recently proposed.The aim of this study is to exte...A novel structural damage detection method with a new damage index,i.e.,the statistical moment-based damage detection（SMBDD） method in the frequency domain,has been recently proposed.The aim of this study is to extend the SMBDD method in the frequency domain to the time domain for building structures subjected to non-Gaussian and non-stationary excitations.The applicability and effectiveness of the SMBDD method in the time domainis verified both numerically and experimentally.Shear buildings with various damage scenarios are first numerically investigated in the time domain taking into account the effect of measurement noise.The applicability of the proposed method in the time domain to building structures subjected to non-Gaussian and non-stationary excitations is then experimentally investigated through a series of shaking table tests,in which two three-story shear building models with four damage scenarios aretested.The identified damage locations and severities are then compared with the preset values.The comparative results are found to be satisfactory,and the SMBDD method is shown to be feasible and effective for building structures subjected to non-Gaussian and non-stationary excitations.展开更多
The benchmark of a simply supported beam with damage and bending fuzzy stiffness consideration is established to be utilized for damage detection. The explicit expression describing the Rotational Angle Influence Line...The benchmark of a simply supported beam with damage and bending fuzzy stiffness consideration is established to be utilized for damage detection. The explicit expression describing the Rotational Angle Influence Lines(RAIL) of the arbitrary section in the benchmark is presented as the nonlinear relation between the moving load and the RAIL appeared, when the moving load is located on the damage area. The damage detection method is derived based on the Difference of the RAIL Curvature(DRAIL-C) prior to and following arbitrarily section damage in a simply supported beam with bending fuzzy stiffness consideration. The results demonstrate that the damage position can be located by the DRAIL-C graph and the damage extent can be calculated by the DRAIL-C curve peak. The simply supported box girder as a one-dimensional model and the simply supported truss bridge as a three-dimensional model with the bending fuzzy stiffness are simulated for the validity of the proposed method to be verified. The measuring point position and noise intensity effects are discussed in the simply supported box girder example. This paper provides a new consideration and technique for the damage detection of a simply supported bridge with bending fuzzy stiffness consideration.展开更多
Damage detection is a key procedure in maintenance throughout structures′life cycles and post-disaster loss assessment.Due to the complex types of structural damages and the low efficiency and safety of manual detect...Damage detection is a key procedure in maintenance throughout structures′life cycles and post-disaster loss assessment.Due to the complex types of structural damages and the low efficiency and safety of manual detection,detecting damages with high efficiency and accuracy is the most popular research direction in civil engineering.Computer vision(CV)technology and deep learning(DL)algorithms are considered as promising tools to address the aforementioned challenges.The paper aims to systematically summarized the research and applications of DL-based CV technology in the field of damage detection in recent years.The basic concepts of DL-based CV technology are introduced first.The implementation steps of creating a damage detection dataset and some typical datasets are reviewed.CV-based structural damage detection algorithms are divided into three categories,namely,image classification-based(IC-based)algorithms,object detection-based(OD-based)algorithms,and semantic segmentation-based(SS-based)algorithms.Finally,the problems to be solved and future research directions are discussed.The foundation for promoting the deep integration of DL-based CV technology in structural damage detection and structural seismic damage identification has been laid.展开更多
A nonparametric structural damage detection methodology based on neuralnetworks method is presented for health monitoring of structure-unknown systems. In this approachappropriate neural networks are trained by use of...A nonparametric structural damage detection methodology based on neuralnetworks method is presented for health monitoring of structure-unknown systems. In this approachappropriate neural networks are trained by use of the modal test data from a 'healthy' structure.The trained networks which are subsequently fed with vibration measurements from the same structurein different stages have the capability of recognizing the location and the content of structuraldamage and thereby can monitor the health of the structure. A modified back-propagation neuralnetwork is proposed to solve the two practical problems encountered by the traditionalback-propagation method, i.e., slow learning progress and convergence to a false local minimum.Various training algorithms, types of the input layer and numbers of the nodes in the input layerare considered. Numerical example results from a 5-degree-of-freedom spring-mass structure andanalyses on the experimental data of an actual 5-storey-steel-frame demonstrate thatneural-networks-based method is a robust procedure and a practical tool for the detection ofstructural damage, and that the modified back-propagation algorithm could improve the computationalefficiency as well as the accuracy of detection.展开更多
It is well known that in most cases, a reference is necessary for structural health diagnosis, and it is very difficult to obtain such a reference for a given structure. In this paper, a clan member signal method （C...It is well known that in most cases, a reference is necessary for structural health diagnosis, and it is very difficult to obtain such a reference for a given structure. In this paper, a clan member signal method （CMSM） is proposed for use in structures consisting of groups （or clans） that have the same geometry, i.e., the same cross section and length, and identical boundary conditions. It is expected that signals measured on any undamaged member in a clan after an event could be used as a reference for any other members in the clan. To verify the applicability of the proposed method, a steel truss model is tested and the results show that the CMSM is very effective in detecting local damage in structures composed of identical slender members.展开更多
This paper investigates the damage detection based on the propagation of guided wave in bimetal composite pipes,which can identify damage locations in both axial and circumferential directions.The feasibility of the m...This paper investigates the damage detection based on the propagation of guided wave in bimetal composite pipes,which can identify damage locations in both axial and circumferential directions.The feasibility of the method is showed by numerical simulations using FEM code ANSYS. Mode analysis is used to evaluate the guided wave mode and its structure,which can provide the basis of the mode selection in measurements scheme.The guided wave propagation in a damaged pipe is computed by transient analysis.16 nodes around the pipe wall,as probes,are used to record the guided wave signal.When Pseudo Margenau-Hill distribution(PMHD)for each signal is carried out, three types of modes could be found,which are led mode,excited mode and lag mode in sequences. Based on the results,the arrival time of the excited mode could be used to locate damage in axial direction,and the energy distribution around the pipe of lag mode is consistent with the damage in circumferential direction.The simulation illustrated the possibility of detecting damage location in both axial and circumferential directions based on longitudinal ultrasonic guided waves only.展开更多
The development of robust damage detection methods for offshore structures is crucial to prevent catastrophes caused by structural failures. In this research, we developed an Improved Modal Strain Energy （IMSE） meth...The development of robust damage detection methods for offshore structures is crucial to prevent catastrophes caused by structural failures. In this research, we developed an Improved Modal Strain Energy （IMSE） method for detecting damage in offshore platform structures based on a traditional modal strain energy method （the Stubbs index method）. The most significant difference from the Stubbs index method was the application of modal frequencies. The goal was to improve the robustness of the traditional method. To demonstrate the effectiveness and practicality of the proposed IMSE method, both numerical and experimental studies were conducted for different damage scenarios using a jacket platform structure. The results demonstrated the effectiveness of the IMSE method in damage location when only limited, spatially incomplete, and noise-polluted modal data is available. Comparative studies showed that the IMSE index outperformed the Stubbs index and exhibited stronger robustness, confirming the superiority of the proposed approach.展开更多
The key parameters for damage detection and localization are eigenfrequencies, related equivalent viscous damping factors and mode shapes. The classical approach is based on the evaluation of these structural paramete...The key parameters for damage detection and localization are eigenfrequencies, related equivalent viscous damping factors and mode shapes. The classical approach is based on the evaluation of these structural parameters before and after a seismic event, but by using a modern approach based on time-frequency transformations it is possible to quantify these parameters throughout the ground shaking phase. In particular with the use of the S-Transform, it is possible to follow the temporal evolution of the structural dynamics parameters before, during and after an earthquake. In this paper, a methodology for damage localization on framed structures subjected to strong motion earthquakes is proposed based on monitoring the modal curvature variation in the natural frequency of a structure. Two examples of application are described to illustrate the technique: Computer simulation of the nonlinear response of a model, and several laboratory(shaking table) tests performed at the University of Basilicata(Italy). Damage detected using the proposed approach and damage revealed via visual inspections in the tests are compared.展开更多
For the purpose of structural health monitoring, a damage detection method combined with optimum sensor placement is proposed in this paper. The back sequential sensor placement （BSSP） algorithm is introduced to opt...For the purpose of structural health monitoring, a damage detection method combined with optimum sensor placement is proposed in this paper. The back sequential sensor placement （BSSP） algorithm is introduced to optimize the sensor locations with the aim of maximizing the 2-norm of information matrix, since the EI method is not suitable for optimum sensor placement based on eigenvector sensitivity analysis. Structural damage detection is carried out based on the respective advantages of mode shape and frequency. The optimized incomplete mode shapes yielded from the optimal sensor locations are used to localize structural damage. After the potential damage elements have been preliminarily identified, an iteration scheme is adopted to estimate the damage extent of the potential damage elements based on the changes in the frequency. The effectiveness of this method is demonstrated using a numerical example of a 31-bar truss structure.展开更多
The functional piezoelectric ceramic smart aggregate(SA) sensors and actuators,based on piezoelectric ceramic materials such as lead zirconium titanate(PZT),were embedded into the reinforced concrete beams with three-...The functional piezoelectric ceramic smart aggregate(SA) sensors and actuators,based on piezoelectric ceramic materials such as lead zirconium titanate(PZT),were embedded into the reinforced concrete beams with three-point bending under static loading for purposes of damage detection.The SA actuators generated the desired sine sweep excitation signals online and the SA sensors received and detected real-time signals before and after damage.The wavelet analysis and statistical characteristics about damage signals were used as a signal processing and analysis tool to extract the optimal damage information and establish a statistical damage detection algorithm.The damage index-based wavelet analysis and damage probability-based probability and statistics were proposed by PZT wavebased theory and active health monitoring technology.The results showed that the existence of cracks inside largely attenuated the amplitude of active monitoring signal after the damage of beam and the attenuation was related to the severity degree of damage.The innovative statistical algorithm of damage pattern detection based PZT-SA can effectively determine damage probability and damage degree,and provide a prediction for the critical damage location of reinforced concrete structures.The developed method can be utilized for the structural health comprehensive monitoring and damage detection on line of various large-scale concrete structures.展开更多
As the top of the pile foundation in high-pile wharf is connected to the superstructure and most of the pile bodies are located below the water surface, traditional damage detection methods are greatly limited in thei...As the top of the pile foundation in high-pile wharf is connected to the superstructure and most of the pile bodies are located below the water surface, traditional damage detection methods are greatly limited in their application to pile foundation in service. In the present study, a new method for pile foundation damage detection is developed based on the curve shape of the curvature mode difference(CMD) before and after damage. In the method, the influence at each node on the overall CMD curve shape is analyzed through a data deletion model, statistical characteristic indexes are established to reflect the difference between damaged and undamaged units, and structural damage is accurately detected. The effectiveness and robustness of the method are verified by a finite element model(FEM) of high-pile wharf under different damage conditions and different intensities of Gaussian white noise. The applicability of the method is then experimentally validated by a physical model of high-pile wharf. Both the FEM and the experimental results show that the method is capable of detecting pile foundation damage in noisy curvature mode and has strong application potential.展开更多
基金supported by National Key Research and Development Program of China(2023YFF0906100)National Natural Science Foundation of China(52408008)Key Research and Development Program of Jiangsu Province(BE2022833).
文摘Current damage detection methods based on model updating and sensitivity Jacobian matrixes show a low convergence ratio and computational efficiency for online calculations.The aim of this paper is to construct a real-time automated damage detection method by developing a theory-assisted adaptive mutiagent twin delayed deep deterministic(TA2-MATD3)policy gradient algorithm.First,the theoretical framework of reinforcement-learning-driven damage detection is established.To address the disadvantages of traditional mutiagent twin delayed deep deterministic(MATD3)method,the theory-assisted mechanism and the adaptive experience playback mechanism are introduced.Moreover,a historical residential house built in 1889 was taken as an example,using its 12-month structural health monitoring data.TA2-MATD3 was compared with existing damage detection methods in terms of the convergence ratio,online computing efficiency,and damage detection accuracy.The results show that the computational efficiency of TA2-MATD3 is approximately 117–160 times that of the traditional methods.The convergence ratio of damage detection on the training set is approximately 97%,and that on the test set is in the range of 86.2%–91.9%.In addition,the main apparent damages found in the field survey were identified by TA2-MATD3.The results indicate that the proposed method can significantly improve the online computing efficiency and damage detection accuracy.This research can provide novel perspectives for the use of reinforcement learning methods to conduct damage detection in online structural health monitoring.
基金supported by the Shanghai Sailing Program,China (No.20YF1447600)the Research Start-Up Project of Shanghai Institute of Technology (No.YJ2021-60)+1 种基金the Collaborative Innovation Project of Shanghai Institute of Technology (No.XTCX2020-12)the Science and Technology Talent Development Fund for Young and Middle-Aged Teachers at Shanghai Institute of Technology (No.ZQ2022-6)。
文摘There is a problem of real-time detection difficulty in road surface damage detection. This paper proposes an improved lightweight model based on you only look once version 5(YOLOv5). Firstly, this paper fully utilized the convolutional neural network(CNN) + ghosting bottleneck(G_bneck) architecture to reduce redundant feature maps. Afterwards, we upgraded the original upsampling algorithm to content-aware reassembly of features(CARAFE) and increased the receptive field. Finally, we replaced the spatial pyramid pooling fast(SPPF) module with the basic receptive field block(Basic RFB) pooling module and added dilated convolution. After comparative experiments, we can see that the number of parameters and model size of the improved algorithm in this paper have been reduced by nearly half compared to the YOLOv5s. The frame rate per second(FPS) has been increased by 3.25 times. The mean average precision(m AP@0.5: 0.95) has increased by 8%—17% compared to other lightweight algorithms.
文摘Drive-by techniques for bridge health monitoring have drawn increasing attention from researchers and practitioners,in the attempt to make bridge condition-based monitoring more cost-efficient.In this work,the authors propose a drive-by approach that takes advantage from bogie vertical accelerations to assess bridge health status.To do so,continuous wavelet transform is combined with multiple sparse autoencoders that allow for damage detection and localization across bridge span.According to authors’best knowledge,this is the first case in which an unsupervised technique,which relies on the use of sparse autoencoders,is used to localize damages.The bridge considered in this work is a Warren steel truss bridge,whose finite element model is referred to an actual structure,belonging to the Italian railway line.To investigate damage detection and localization performances,different operational variables are accounted for:train weight,forward speed and track irregularity evolution in time.Two configurations for the virtual measuring channels were investigated:as a result,better performances were obtained by exploiting the vertical accelerations of both the bogies of the leading coach instead of using only one single acceleration signal.
基金support of CNPq(Brazilian Ministry of Science and Technology Agency),of CAPES(Higher Education Improvement Agency),of FAPESP(São Paulo Research Foundation)under grant#2022/13045-1,of VALE Catedra Under Rail and of Base Funding-UIDB/04708/2020Programmatic Funding-UIDP/04708/2020 of the CONSTRUCT-“Instituto de I&D em Estruturas e Construções”.
文摘High-speed railway bridges are essential components of any railway transportation system that should keep adequate levels of serviceability and safety.In this context,drive-by methodologies have emerged as a feasible and cost-effective monitor-ing solution for detecting damage on railway bridges while minimizing train operation interruptions.Moreover,integrating advanced sensor technologies and machine learning algorithms has significantly enhanced structural health monitoring(SHM)for bridges.Despite being increasingly used in traditional SHM applications,studies using autoencoders within drive-by methodologies are rare,especially in the railway field.This study presents a novel approach for drive-by damage detection in HSR bridges.The methodology relies on acceleration records collected from multiple bridge crossings by an operational train equipped with onboard sensors.Log-Mel spectrogram features derived from the acceleration records are used together with sparse autoencoders for computing statistical distribution-based damage indexes.Numerical simulations were performed on a 3D vehicle-track-bridge interaction system model implemented in Matlab to evaluate the robustness and effectiveness of the proposed approach,considering several damage scenarios,vehicle speeds,and environmental and operational variations,such as multiple track irregularities and varying measurement noise.The results show that the pro-posed approach can successfully detect damages,as well as characterize their severity,especially for very early-stage dam-ages.This demonstrates the high potential of applying Mel-frequency damage-sensitive features associated with machine learning algorithms in the drive-by condition assessment of high-speed railway bridges.
文摘The current study examines damage detection in fluid-conveying pipes supported on a Pasternak foundation.This study proposes a novel method that uses the matching pursuit(MP)algorithm for damage detection.The governing equations of motion for the pipe are derived using Hamilton’s principle.The finite element method,combined with the Galerkin approach,is employed to obtain the mass,damping,and stiffness matrices.To identify damage locations through pipe mode-shape decomposition,an index called the“matching pursuit residual”is introduced as a novel contribution of this study.The proposed method facilitates damage detection at various levels and locations under different boundary conditions.The findings demonstrate that the MP residual damage index can accurately localize damage in the pipes.Furthermore,the results of the numerical and experimental tests showcase the efficiency of the proposed method,highlighting that the MP signal approximation algorithm effectively detects damage in structures.
基金the National Key Research and Development Program of China(No.2023 YFC2811600)the National Natural Science Foundation of China(Nos.52301349,52088102)+1 种基金the Major Science and Technology Innovation Program of Qingdao(No.223-3-hygg-10-hy)the Qingdao Science Foundation for Post-doctoral Scientists(Nos.QDBSH20220202070,QDBSH20220201015)。
文摘A deep-sea riser is a crucial component of the mining system used to lift seafloor mineral resources to the vessel.Even minor damage to the riser can lead to substantial financial losses,environmental impacts,and safety hazards.However,identifying modal parameters for structural health monitoring remains a major challenge due to its large deformations and flexibility.Vibration signal-based methods are essential for detecting damage and enabling timely maintenance to minimize losses.However,accurately extracting features from one-dimensional(1D)signals is often hindered by various environmental factors and measurement noises.To address this challenge,a novel approach based on a residual convolutional auto-encoder(RCAE)is proposed for detecting damage in deep-sea mining risers,incorporating a data fusion strategy.First,principal component analysis(PCA)is applied to reduce environmental fluctuations and fuse multisensor strain readings.Subsequently,a 1D-RCAE is used to extract damage-sensitive features(DSFs)from the fused dataset.A Mahalanobis distance indicator is established to compare the DSFs of the testing and healthy risers.The specific threshold for these distances is determined using the 3σcriterion,which is employed to assess whether damage has occurred in the testing riser.The effectiveness and robustness of the proposed approach are verified through numerical simulations of a 500-m riser and experimental tests on a 6-m riser.Moreover,the impact of contaminated noise and environmental fluctuations is examined.Results show that the proposed PCA-1D-RCAE approach can effectively detect damage and is resilient to measurement noise and environmental fluctuations.The accuracy exceeds 98%under noise-free conditions and remains above 90%even with 10 dB noise.This novel approach has the potential to establish a new standard for evaluating the health and integrity of risers during mining operations,thereby reducing the high costs and risks associated with failures.Maintenance activities can be scheduled more efficiently by enabling early and accurate detection of riser damage,minimizing downtime and avoiding catastrophic failures.
基金Projects(51925808,52078504)supported by the National Natural Science Foundation of ChinaProject(2022JJ10082)supported by the Natural Science Fund for Distinguished Young Scholar of Hunan Province,ChinaProject(2021RC3016)supported by the Science and Technology Innovation Program of Hunan Province,China。
文摘Structural damage detection is hard to conduct in large-scale civil structures due to enormous structural data and insufficient damage features.To improve this situation,a damage detection method based on model reduction and response reconstruction is presented.Based on the framework of two-step model updating including substructure-level localization and element-level detection,the response reconstruction strategy with an improved sensitivity algorithm is presented to conveniently complement modal information and promote the reliability of model updating.In the iteration process,the reconstructed response is involved in the sensitivity algorithm as a reconstruction-related item.Besides,model reduction is applied to reduce computational degrees of freedom(DOFs)in each detection step.A numerical truss bridge is modelled to vindicate the effectiveness and efficiency of the method.The results showed that the presented method reduces the requirement for installed sensors while improving efficiency and ensuring accuracy of damage detection compared to traditional methods.
基金The National High Technology Research and Develop-ment Program of China(863Program)(No.2006AA04Z416)the Na-tional Science Fund for Distinguished Young Scholars(No.50725828)the Excellent Dissertation Program for Doctoral Degree of Southeast University(No.0705)
文摘Multi-source information fusion （MSIF） is imported into structural damage diagnosis methods to improve the validity of damage detection. After the introduction of the basic theory, the function model, classifications and mathematical methods of MSIF, a structural damage detection method based on MSIF is presented, which is to fuse two or more damage character vectors from different structural damage diagnosis methods on the character-level. In an experiment of concrete plates, modal information is measured and analyzed. The structural damage detection method based on MSIF is taken to localize cracks of concrete plates and it is proved to be effective. Results of damage detection by the method based on MSIF are compared with those from the modal strain energy method and the flexibility method. Damage, which can hardly be detected by using the single damage identification method, can be diagnosed by the damage detection method based on the character-level MSIF technique. Meanwhile multi-location damage can be identified by the method based on MSIF. This method is sensitive to structural damage and different mathematical methods for MSIF have different preconditions and applicabilities for diversified structures. How to choose mathematical methods for MSIF should be discussed in detail in health monitoring systems of actual structures.
基金National Science Foundation Grant NSF CMS CAREER Under Grant No.9996290NSF CMMI Under Grant No.0830391
文摘The primary objective of this paper is to develop output only modal identification and structural damage detection. Identification of multi-degree of freedom （MDOF） linear time invariant （LTI） and linear time variant （LTV--due to damage） systems based on Time-frequency （TF） techniques--such as short-time Fourier transform （STFT）, empirical mode decomposition （EMD）, and wavelets--is proposed. STFT, EMD, and wavelet methods developed to date are reviewed in detail. In addition a Hilbert transform （HT） approach to determine frequency and damping is also presented. In this paper, STFT, EMD, HT and wavelet techniques are developed for decomposition of free vibration response of MDOF systems into their modal components. Once the modal components are obtained, each one is processed using Hilbert transform to obtain the modal frequency and damping ratios. In addition, the ratio of modal components at different degrees of freedom facilitate determination of mode shape. In cases with output only modal identification using ambient/random response, the random decrement technique is used to obtain free vibration response. The advantage of TF techniques is that they arc signal based; hence, can be used for output only modal identification. A three degree of freedom 1：10 scale model test structure is used to validate the proposed output only modal identification techniques based on STFT, EMD, HT, wavelets. Both measured free vibration and forced vibration （white noise） response are considered. The secondary objective of this paper is to show the relative ease with which the TF techniques can be used for modal identification and their potential for real world applications where output only identification is essential. Recorded ambient vibration data processed using techniques such as the random decrement technique can be used to obtain the free vibration response, so that further processing using TF based modal identification can be performed.
基金The Hong Kong Polytechnic University through a PhD studentship for the first authorthe Research Grants Council of Hong Kong (PolyU 5319/10E) for the second author
文摘A novel structural damage detection method with a new damage index,i.e.,the statistical moment-based damage detection（SMBDD） method in the frequency domain,has been recently proposed.The aim of this study is to extend the SMBDD method in the frequency domain to the time domain for building structures subjected to non-Gaussian and non-stationary excitations.The applicability and effectiveness of the SMBDD method in the time domainis verified both numerically and experimentally.Shear buildings with various damage scenarios are first numerically investigated in the time domain taking into account the effect of measurement noise.The applicability of the proposed method in the time domain to building structures subjected to non-Gaussian and non-stationary excitations is then experimentally investigated through a series of shaking table tests,in which two three-story shear building models with four damage scenarios aretested.The identified damage locations and severities are then compared with the preset values.The comparative results are found to be satisfactory,and the SMBDD method is shown to be feasible and effective for building structures subjected to non-Gaussian and non-stationary excitations.
基金the National Natural Science Foundation of China(Nos.51608245 and 51568041)the Natural Science Foundation of Gansu Province(No.148RJZA026)
文摘The benchmark of a simply supported beam with damage and bending fuzzy stiffness consideration is established to be utilized for damage detection. The explicit expression describing the Rotational Angle Influence Lines(RAIL) of the arbitrary section in the benchmark is presented as the nonlinear relation between the moving load and the RAIL appeared, when the moving load is located on the damage area. The damage detection method is derived based on the Difference of the RAIL Curvature(DRAIL-C) prior to and following arbitrarily section damage in a simply supported beam with bending fuzzy stiffness consideration. The results demonstrate that the damage position can be located by the DRAIL-C graph and the damage extent can be calculated by the DRAIL-C curve peak. The simply supported box girder as a one-dimensional model and the simply supported truss bridge as a three-dimensional model with the bending fuzzy stiffness are simulated for the validity of the proposed method to be verified. The measuring point position and noise intensity effects are discussed in the simply supported box girder example. This paper provides a new consideration and technique for the damage detection of a simply supported bridge with bending fuzzy stiffness consideration.
基金National Key R&D Program of China under Grant No.2017YFC1500606,National Natural Science Foundation of China under Grant No.52020105002Heilongjiang Touyan Innovation Team Program。
文摘Damage detection is a key procedure in maintenance throughout structures′life cycles and post-disaster loss assessment.Due to the complex types of structural damages and the low efficiency and safety of manual detection,detecting damages with high efficiency and accuracy is the most popular research direction in civil engineering.Computer vision(CV)technology and deep learning(DL)algorithms are considered as promising tools to address the aforementioned challenges.The paper aims to systematically summarized the research and applications of DL-based CV technology in the field of damage detection in recent years.The basic concepts of DL-based CV technology are introduced first.The implementation steps of creating a damage detection dataset and some typical datasets are reviewed.CV-based structural damage detection algorithms are divided into three categories,namely,image classification-based(IC-based)algorithms,object detection-based(OD-based)algorithms,and semantic segmentation-based(SS-based)algorithms.Finally,the problems to be solved and future research directions are discussed.The foundation for promoting the deep integration of DL-based CV technology in structural damage detection and structural seismic damage identification has been laid.
基金the National Natural Science Foundation of China (No.59908003)the Natural Science Foundation of Hubei Province (No.99J035)
文摘A nonparametric structural damage detection methodology based on neuralnetworks method is presented for health monitoring of structure-unknown systems. In this approachappropriate neural networks are trained by use of the modal test data from a 'healthy' structure.The trained networks which are subsequently fed with vibration measurements from the same structurein different stages have the capability of recognizing the location and the content of structuraldamage and thereby can monitor the health of the structure. A modified back-propagation neuralnetwork is proposed to solve the two practical problems encountered by the traditionalback-propagation method, i.e., slow learning progress and convergence to a false local minimum.Various training algorithms, types of the input layer and numbers of the nodes in the input layerare considered. Numerical example results from a 5-degree-of-freedom spring-mass structure andanalyses on the experimental data of an actual 5-storey-steel-frame demonstrate thatneural-networks-based method is a robust procedure and a practical tool for the detection ofstructural damage, and that the modified back-propagation algorithm could improve the computationalefficiency as well as the accuracy of detection.
基金Chinese Ministry of Science and Technology and National Natural Science Foundation Under Grant No. 2006DFB71680
文摘It is well known that in most cases, a reference is necessary for structural health diagnosis, and it is very difficult to obtain such a reference for a given structure. In this paper, a clan member signal method （CMSM） is proposed for use in structures consisting of groups （or clans） that have the same geometry, i.e., the same cross section and length, and identical boundary conditions. It is expected that signals measured on any undamaged member in a clan after an event could be used as a reference for any other members in the clan. To verify the applicability of the proposed method, a steel truss model is tested and the results show that the CMSM is very effective in detecting local damage in structures composed of identical slender members.
文摘This paper investigates the damage detection based on the propagation of guided wave in bimetal composite pipes,which can identify damage locations in both axial and circumferential directions.The feasibility of the method is showed by numerical simulations using FEM code ANSYS. Mode analysis is used to evaluate the guided wave mode and its structure,which can provide the basis of the mode selection in measurements scheme.The guided wave propagation in a damaged pipe is computed by transient analysis.16 nodes around the pipe wall,as probes,are used to record the guided wave signal.When Pseudo Margenau-Hill distribution(PMHD)for each signal is carried out, three types of modes could be found,which are led mode,excited mode and lag mode in sequences. Based on the results,the arrival time of the excited mode could be used to locate damage in axial direction,and the energy distribution around the pipe of lag mode is consistent with the damage in circumferential direction.The simulation illustrated the possibility of detecting damage location in both axial and circumferential directions based on longitudinal ultrasonic guided waves only.
基金Supported by the National Natural Science Foundation of China （51209189, 51379196）, and the Natural Science Foundation of Shandong Province （ZR2013 EEQ006, ZR2011 EL049）
文摘The development of robust damage detection methods for offshore structures is crucial to prevent catastrophes caused by structural failures. In this research, we developed an Improved Modal Strain Energy （IMSE） method for detecting damage in offshore platform structures based on a traditional modal strain energy method （the Stubbs index method）. The most significant difference from the Stubbs index method was the application of modal frequencies. The goal was to improve the robustness of the traditional method. To demonstrate the effectiveness and practicality of the proposed IMSE method, both numerical and experimental studies were conducted for different damage scenarios using a jacket platform structure. The results demonstrated the effectiveness of the IMSE method in damage location when only limited, spatially incomplete, and noise-polluted modal data is available. Comparative studies showed that the IMSE index outperformed the Stubbs index and exhibited stronger robustness, confirming the superiority of the proposed approach.
基金Italian Civil Protection within the Projects DPC-RELUIS 2010-2013(Task 3.1)DPC-RELUIS 2014(Special Project"Monitoraggio")
文摘The key parameters for damage detection and localization are eigenfrequencies, related equivalent viscous damping factors and mode shapes. The classical approach is based on the evaluation of these structural parameters before and after a seismic event, but by using a modern approach based on time-frequency transformations it is possible to quantify these parameters throughout the ground shaking phase. In particular with the use of the S-Transform, it is possible to follow the temporal evolution of the structural dynamics parameters before, during and after an earthquake. In this paper, a methodology for damage localization on framed structures subjected to strong motion earthquakes is proposed based on monitoring the modal curvature variation in the natural frequency of a structure. Two examples of application are described to illustrate the technique: Computer simulation of the nonlinear response of a model, and several laboratory(shaking table) tests performed at the University of Basilicata(Italy). Damage detected using the proposed approach and damage revealed via visual inspections in the tests are compared.
基金Project supported by the National Basic Research Program of China(973 Program)(No.2011CB13804)
文摘For the purpose of structural health monitoring, a damage detection method combined with optimum sensor placement is proposed in this paper. The back sequential sensor placement （BSSP） algorithm is introduced to optimize the sensor locations with the aim of maximizing the 2-norm of information matrix, since the EI method is not suitable for optimum sensor placement based on eigenvector sensitivity analysis. Structural damage detection is carried out based on the respective advantages of mode shape and frequency. The optimized incomplete mode shapes yielded from the optimal sensor locations are used to localize structural damage. After the potential damage elements have been preliminarily identified, an iteration scheme is adopted to estimate the damage extent of the potential damage elements based on the changes in the frequency. The effectiveness of this method is demonstrated using a numerical example of a 31-bar truss structure.
基金Supported by Science and Technology Project of Ministry of Housing and Urban-Rural Development(No.2011k211)"11th Five-Year" Science and Technology Research Project of Education Department,Jilin Province(No.200925)Liaoning Structure Engineering Key Laboratory 2009 Open Fund(JG2009 2007-08)
文摘The functional piezoelectric ceramic smart aggregate(SA) sensors and actuators,based on piezoelectric ceramic materials such as lead zirconium titanate(PZT),were embedded into the reinforced concrete beams with three-point bending under static loading for purposes of damage detection.The SA actuators generated the desired sine sweep excitation signals online and the SA sensors received and detected real-time signals before and after damage.The wavelet analysis and statistical characteristics about damage signals were used as a signal processing and analysis tool to extract the optimal damage information and establish a statistical damage detection algorithm.The damage index-based wavelet analysis and damage probability-based probability and statistics were proposed by PZT wavebased theory and active health monitoring technology.The results showed that the existence of cracks inside largely attenuated the amplitude of active monitoring signal after the damage of beam and the attenuation was related to the severity degree of damage.The innovative statistical algorithm of damage pattern detection based PZT-SA can effectively determine damage probability and damage degree,and provide a prediction for the critical damage location of reinforced concrete structures.The developed method can be utilized for the structural health comprehensive monitoring and damage detection on line of various large-scale concrete structures.
基金financially supported by the National Natural Science Foundation of China (Grant Nos. 51709093 and 51679081)Fujian Provincial Department of Transportation Science and Technology Development Project (Grant No. 201708)Hohai University Student Innovation and Entrepreneurship Training Project (Grant No. 201910294014Z)。
文摘As the top of the pile foundation in high-pile wharf is connected to the superstructure and most of the pile bodies are located below the water surface, traditional damage detection methods are greatly limited in their application to pile foundation in service. In the present study, a new method for pile foundation damage detection is developed based on the curve shape of the curvature mode difference(CMD) before and after damage. In the method, the influence at each node on the overall CMD curve shape is analyzed through a data deletion model, statistical characteristic indexes are established to reflect the difference between damaged and undamaged units, and structural damage is accurately detected. The effectiveness and robustness of the method are verified by a finite element model(FEM) of high-pile wharf under different damage conditions and different intensities of Gaussian white noise. The applicability of the method is then experimentally validated by a physical model of high-pile wharf. Both the FEM and the experimental results show that the method is capable of detecting pile foundation damage in noisy curvature mode and has strong application potential.
