Anti-slide piles are commonly used to stabilise high and steep slopes in earthquake-prone areas in southwestern China.Herein,we investigate the impact of initial damage on the seismic performance of anti-slide piles.F...Anti-slide piles are commonly used to stabilise high and steep slopes in earthquake-prone areas in southwestern China.Herein,we investigate the impact of initial damage on the seismic performance of anti-slide piles.For this purpose,we selected a representative slope adjacent to the Jiuzhaigou Bridge in the Sichuan–Qinghai Railway;we employed a three-dimensional dynamic finite element method combined with the local stiffness reduction approach to simulate three different initial-damage scenarios:intact,slightly damaged and heavily damaged.The dynamic displacement,bending moment and shear stress responses of the piles were comprehensively analysed.Using wavelet packet energy spectrum(WPES)analysis,we introduced two indices:the damage index(DPERV)and its increment(|△DPERV|).The results showed that both the initial damage and seismic energy control the peak dynamic response of the piles.Specifically,high initial damage accelerates stiffness degradation,leading to large horizontal displacements,whereas intact piles sustain high bending moments and shear forces.The distribution of|△DPERV|along a pile reveals three post-earthquake performance stages(i.e.minor,moderate and severe),which agree well with the observed mechanical response characteristics and form the basis for targeted reinforcement strategies.The main innovation of this study is the combined use of initial-damage simulation with WPES analysis,thereby establishing a quantitative diagnostic framework(DPERV and|△DPERV|)for anti-slide piles.This framework determines the non-linear relationship between seismic response and damage evolution and provides a rapid,usable tool for health monitoring and post-earthquake decision-making in landslide-prone mountainous railway areas.展开更多
The advantages of guided wave detection,such as its ability to propagate over long distances and penetrate deeply,have led to its application in the field of anisotropic damage detection in carbon fiber-reinforced pol...The advantages of guided wave detection,such as its ability to propagate over long distances and penetrate deeply,have led to its application in the field of anisotropic damage detection in carbon fiber-reinforced polymer(CFRP).Due to the anisotropy of CFRP,traditional guided wave-based detection methods have difficulty in precisely locating the defect.In this study,we proposed a novel deep learning-based detection method for CFRP by employing image recognition technology for guided wave field inspection.This method is capable of rapidly and accurately extracting defective features from the structure,thereby facilitating precise damage identification.To avoid time-consuming sample data generation by simulation for CFRP,the steady-state guided wave field of the aluminum plates was simulated instead.The isotropic wave field data were then stretched and applied for neural network training.展开更多
A dual‑task parallel machine learning framework was developed by integrating a convolutional autoencoder(CAE)and a fully connected neural network(FCNN)via the gradient‑coupled mechanism,enabling simultaneous data comp...A dual‑task parallel machine learning framework was developed by integrating a convolutional autoencoder(CAE)and a fully connected neural network(FCNN)via the gradient‑coupled mechanism,enabling simultaneous data compression‑reconstruction and structural damage identification.Under the condition where 40% of the sensor nodes are missing,the model successfully reconstructs the full sensor network with an R^(2) of 0.916 and normalized root mean square error(NRMSE)of 0.0288.Even under significant noise contamination with an SNR of 12 dB,the model maintains strong reconstruction performance,achieving a R^(2) of 0.910 and NRMSE of 0.0253.Forty‑six structural damage scenarios were simulated using the scaled bridge model.The accuracy of spatial localization and quantification of the damage severity using the framework exceeds 99.3%.The proposed framework reduces the training time by 54.4%and iteration counts by 45.5% compared to conventional two‑stage machine learning approaches,demonstrating the efficiency of gradient‑coupled optimization.展开更多
Damage identification of the offshore floating wind turbine by vibration/dynamic signals is one of the important and new research fields in the Structural Health Monitoring（SHM）. In this paper a new damage identific...Damage identification of the offshore floating wind turbine by vibration/dynamic signals is one of the important and new research fields in the Structural Health Monitoring（SHM）. In this paper a new damage identification method is proposed based on meta-heuristic algorithms using the dynamic response of the TLP（Tension-Leg Platform） floating wind turbine structure. The Genetic Algorithms（GA）, Artificial Immune System（AIS）, Particle Swarm Optimization（PSO）, and Artificial Bee Colony（ABC） are chosen for minimizing the object function, defined properly for damage identification purpose. In addition to studying the capability of mentioned algorithms in correctly identifying the damage, the effect of the response type on the results of identification is studied. Also, the results of proposed damage identification are investigated with considering possible uncertainties of the structure. Finally, for evaluating the proposed method in real condition, a 1/100 scaled experimental setup of TLP Floating Wind Turbine（TLPFWT） is provided in a laboratory scale and the proposed damage identification method is applied to the scaled turbine.展开更多
The probabilistic damage identification problem with uncertainty in the FE model parameters, external-excitations and measured acceleration responses is studied. The uncertainty in the system is concerned with normall...The probabilistic damage identification problem with uncertainty in the FE model parameters, external-excitations and measured acceleration responses is studied. The uncertainty in the system is concerned with normally distributed random variables with zero mean value and given covariance. Based on the theoretical model and the measured acceleration responses, the probabilistic structural models in undamaged and damaged states are obtained by two-stage model updating, and then the Probabilities of Damage Existence （PDE） of each element are calculated as the damage criterion. The influences of the location of sensors on the damage identification results are also discussed, where one of the optimal sensor placement techniques, the effective independence method, is used to choose the nodes for measurement. The damage identification results by different numbers of measured nodes and different damage criterions are compared in the numerical example.展开更多
Early structural damage identification to obtain an accurate condition assessment can assist in the reprioritization of structural retrofitting schedules in order to guarantee structural safety. Nowadays, seismic isol...Early structural damage identification to obtain an accurate condition assessment can assist in the reprioritization of structural retrofitting schedules in order to guarantee structural safety. Nowadays, seismic isolation technology has been applied in a wide variety of infrastructure, such as buildings, bridges, etc., and the health conditions of these nonlinear hysteretic vibration isolation systems have received considerable attention. To effectively detect structural damage in vibration isolation systems based on vibration data, three time-domain analysis techniques, referred to as the adaptive extended Kalman filter （AEKF）, adaptive sequential nonlinear least-square estimation （ASNLSE） and adaptive quadratic sum-sqnares error （AQSSE）, have been investigated. In this research, these analysis techniques are compared in terms of accuracy, convergence and efficiency, for structural damage detection using experimental data obtained through a series of laboratory tests based on a base-isolated structural model subjected to E1 Centro and Kobe earthquake excitations. The capability of the AEKF, ASNLSE and AQSSE approaches in tracking structural damage is demonstrated and compared.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
To locate and quantify local damage in a simply supported bridge, in this study, we derived a rotational-angle influence line equation of a simply supported beam model with local damage. Using the diagram multiplicati...To locate and quantify local damage in a simply supported bridge, in this study, we derived a rotational-angle influence line equation of a simply supported beam model with local damage. Using the diagram multiplication method, we introduce an analytical formula for a novel damage-identification indicator, namely the diff erence of rotational-angle influence linescurvature(DRAIL-C). If the initial stiff ness of the simply supported beam is known, the analytical formula can be effectively used to determine the extent of damage under certain circumstances. We determined the effectiveness and anti-noise performance of this new damage-identification method using numerical examples of a simply supported beam, a simply supported hollow-slab bridge, and a simply supported truss bridge. The results show that the DRAIL-C is directly proportional to the moving concentrated load and inversely proportional to the distance between the bridge support and the concentrated load and the distance between the damaged truss girder and the angle measuring points. The DRAIL-C indicator is more sensitive to the damage in a steel-truss-bridge bottom chord than it is to the other elements.展开更多
To improve the accuracy and anti-noise ability of the structural damage identification method,a bridge damage identification method is proposed based on a deep belief network(DBN).The output vector is used to establis...To improve the accuracy and anti-noise ability of the structural damage identification method,a bridge damage identification method is proposed based on a deep belief network(DBN).The output vector is used to establish the nonlinear mapping relationship between the mode shape and structural damage.The hidden layer of the DBN is trained through a layer-by-layer pre-training.Finally,the backpropagation algorithm is used to fine-tune the entire network.The method is validated using a numerical model of a steel truss bridge.The results show that under the influence of noise and modeling uncertainty,the damage identification method based on the DBN can identify the accurate damage location and degree identification compared with the traditional damage identification method based on an artificial neural network.展开更多
A new structural damage identification method using limited test static displacement based on grey system theory is proposed in this paper. The grey relation coefficient of displacement curvature is defined and used t...A new structural damage identification method using limited test static displacement based on grey system theory is proposed in this paper. The grey relation coefficient of displacement curvature is defined and used to locate damage in the structure, and an iterative estimation scheme for solving nonlinear optimization programming problems based on the quadratic programming technique is used to identify the damage magnitude. A numerical example of a cantilever beam with single or multiple damages is used to examine the capability of the proposed grey-theory-based method to localize and identify damages. The factors of meas-urement noise and incomplete test data are also discussed. The numerical results showed that the damage in the structure can be localized correctly through using the grey-related coefficient of displacement curvature, and the damage magnitude can be iden-tified with a high degree of accuracy, regardless of the number of measured displacement nodes. This proposed method only requires limited static test data, which is easily available in practice, and has wide applications in structural damage detection.展开更多
To enhance the accuracy and efficiency of bridge damage identification,a novel data-driven damage identification method was proposed.First,convolutional autoencoder(CAE)was used to extract key features from the accele...To enhance the accuracy and efficiency of bridge damage identification,a novel data-driven damage identification method was proposed.First,convolutional autoencoder(CAE)was used to extract key features from the acceleration signal of the bridge structure through data reconstruction.The extreme gradient boosting tree(XGBoost)was then used to perform analysis on the feature data to achieve damage detection with high accuracy and high performance.The proposed method was applied in a numerical simulation study on a three-span continuous girder and further validated experimentally on a scaled model of a cable-stayed bridge.The numerical simulation results show that the identification errors remain within 2.9%for six single-damage cases and within 3.1%for four double-damage cases.The experimental validation results demonstrate that when the tension in a single cable of the cable-stayed bridge decreases by 20%,the method accurately identifies damage at different cable locations using only sensors installed on the main girder,achieving identification accuracies above 95.8%in all cases.The proposed method shows high identification accuracy and generalization ability across various damage scenarios.展开更多
With Bowytis cinerea and its extracellular macromolecular toxins as the test materials, 30 speiees of plants belonging to 29 genera and 21 families were selected as the test plants to observe the infectivity of B. dne...With Bowytis cinerea and its extracellular macromolecular toxins as the test materials, 30 speiees of plants belonging to 29 genera and 21 families were selected as the test plants to observe the infectivity of B. dnerea and damage status of macromolecular toxins secreted by B. cinerea on plants. The resulsts showed that 17 species of plants were beth infected by B. cinerea and damaged by toxins, accounting for 56.7% of the total plants. Two species of plants could be neither infected by B. cinerea nor damaged by toxins. The study provided the reference for further understanding of pathogenic mechanism of plant pathogenic fungi toxins.展开更多
The computerized tomography technique is applied to study the damage propagation of rock for the first time in this paper. CT values and their distribution regularity of damage propaga-tion of rock are analyzed in det...The computerized tomography technique is applied to study the damage propagation of rock for the first time in this paper. CT values and their distribution regularity of damage propaga-tion of rock are analyzed in detail. The relation between CT values and stresses (strains) of the damage propagation of rock is then discussed. This provides the foundation for establishing the constitutive relation of damage propagation of rock.展开更多
Based on measured natural frequencies and acceleration responses,a non-probabilistic information fusion technique is proposed for the structural damage detection by adopting the set-membership identification（SMI） an...Based on measured natural frequencies and acceleration responses,a non-probabilistic information fusion technique is proposed for the structural damage detection by adopting the set-membership identification（SMI） and twostep model updating procedure.Due to the insufficiency and uncertainty of information obtained from measurements,the uncertain problem of damage identification is addressed with interval variables in this paper.Based on the first-order Taylor series expansion,the interval bounds of the elemental stiffness parameters in undamaged and damaged models are estimated,respectively.The possibility of damage existence（PoDE） in elements is proposed as the quantitative measure of structural damage probability,which is more reasonable in the condition of insufficient measurement data.In comparison with the identification method based on a single kind of information,the SMI method will improve the accuracy in damage identification,which reflects the information fusion concept based on the non-probabilistic set.A numerical example is performed to demonstrate the feasibility and effectiveness of the proposed technique.展开更多
This paper discusses the damage identification in the mooring line system of a floating wind turbine(FWT)exposed to various environmental loads.The proposed method incorporates a non-probabilistic method into artifici...This paper discusses the damage identification in the mooring line system of a floating wind turbine(FWT)exposed to various environmental loads.The proposed method incorporates a non-probabilistic method into artificial neural networks(ANNs).The non-probabilistic method is used to overcome the problem of uncertainties.For this purpose,the interval analysis method is used to calculate the lower and upper bounds of ANNs input data.This data contains some of the natural frequencies utilized to train two different ANNs and predict the output data which is the interval bounds of mooring line stiffness.Additionally,in order to reduce computational time and more importantly,identify damage in various conditions,the proposed method is trained using constant loads(CL)case(deterministic loads,including constant wind speed and airy wave model)and is tested using random loads(RL)case(including Kaimal wind model and JONSWAP wave theory).The superiority of this method is assessed by applying the deterministic method for damage identification.The results demonstrate that the proposed non-probabilistic method identifies the location and severity of damage more accurately compared to a deterministic one.This superiority is getting more remarkable as the difference in uncertainty levels between training and testing data is increasing.展开更多
A huge number of old arch bridges located in rural regions are at the peak of maintenance.The health monitoring technology of the long-span bridge is hardly applicable to the small-span bridge,owing to the absence of ...A huge number of old arch bridges located in rural regions are at the peak of maintenance.The health monitoring technology of the long-span bridge is hardly applicable to the small-span bridge,owing to the absence of technical resources and sufficient funds in rural regions.There is an urgent need for an economical,fast,and accurate damage identification solution.The authors proposed a damage identification system of an old arch bridge implemented with amachine learning algorithm,which took the vehicle-induced response as the excitation.A damage index was defined based on wavelet packet theory,and a machine learning sample database collecting the denoised response was constructed.Through comparing three machine learning algorithms:Back-Propagation Neural Network(BPNN),Support Vector Machine(SVM),and Random Forest(R.F.),the R.F.damage identification model were found to have a better recognition ability.Finally,the Particle Swarm Optimization(PSO)algorithm was used to optimize the number of subtrees and split features of the R.F.model.The PSO optimized R.F.model was capable of the identification of different damage levels of old arch bridges with sensitive damage index.The proposed framework is practical and promising for the old bridge’s structural damage identification in rural regions.展开更多
Too many sensors and data information in structural health monitoring system raise the problem of how to realize multi-sensor information fusion. An experiment on a three-story frame structure was conducted to obtain ...Too many sensors and data information in structural health monitoring system raise the problem of how to realize multi-sensor information fusion. An experiment on a three-story frame structure was conducted to obtain vibration test data in 36damage cases. A coupling neural network (NN) based on multi-sensor information fusion is proposed to achieve identification of damage occurrence, damage localization and damage quantification, respectively. First, wavelet packet transform (WPT) is used to extract features of vibration test data from structure with different damage extent. Then, data fusion is conducted by assembling feature vectors of different type sensors. Finally, three sets of coupling NN are constructed to implement decision fusion and damage identification. The results of experimental study proved the validity and feasibility of the proposed methodology.展开更多
Convolution neural networks in deep learning can solve the problem of damage identification based on vibration acceleration.By combining multiple 1D DenseNet submodels,a new ensemble learning method is proposed to imp...Convolution neural networks in deep learning can solve the problem of damage identification based on vibration acceleration.By combining multiple 1D DenseNet submodels,a new ensemble learning method is proposed to improve identification accuracy.1D DenseNet is built using standard 1D CNN and DenseNet basic blocks,and the acceleration data obtained from multiple sampling points is brought into the 1D DenseNet training to generate submodels after offset sampling.When using submodels for damage identification,the voting method ideas in ensemble learning are used to vote on the results of each submodel,and then vote centrally.Finally,the cantilever damage problem simulated by ABAQUS is selected as a case study to discuss the excellent performance of the proposed method.The results show that the ensemble 1D DenseNet damage identification method outperforms any submodel in terms of accuracy.Furthermore,the submodel is visualized to demonstrate its operation mode.展开更多
As a critical structure of aerospace equipment,aluminum alloy stiffened plate will influence the stability of spacecraft in orbit and the normal operation of the system.In this study,a GWO-ELM algorithm-based impact d...As a critical structure of aerospace equipment,aluminum alloy stiffened plate will influence the stability of spacecraft in orbit and the normal operation of the system.In this study,a GWO-ELM algorithm-based impact damage identification method is proposed for aluminum alloy stiffened panels to monitor and evaluate the damage condition of such stiffened panels of spacecraft.Firstly,together with numerical simulation,the experimental simulation to obtain the damage acoustic emission signals of aluminum alloy reinforced panels is performed,to establish the damage data.Subsequently,the amplitude-frequency characteristics of impact damage signals are extracted and put into an extreme learning machine(ELM)model to identify the impact location and damage degree,and the Gray Wolf Optimization(GWO)algorithm is employed to update the weight parameters of the model.Finally,experiments are conducted on the irregular aluminum alloy stiffened plate with the size of 2200 mm×500 mm×10 mm,the identification accuracy of impact position and damage degree is 98.90% and 99.55% in 68 test areas,respectively.Comparative experiments with ELM and backpropagation neural networks(BPNN)demonstrate that the impact damage identification of aluminum alloy stiffened plate based on GWO-ELM algorithm can serve as an effective way to monitor spacecraft structural damage.展开更多
With the aid of non-contact measurements of vibrating surfaces through laser scanning,operating deflection shapes(ODSs)with high spatial resolutions can be used to graphically characterize damage in plane structures.A...With the aid of non-contact measurements of vibrating surfaces through laser scanning,operating deflection shapes(ODSs)with high spatial resolutions can be used to graphically characterize damage in plane structures.Although numerous damage identification approaches relying on laser-measured ODSs have been developed for plate-type structures,they cannot be directly applied to circular cylinders due to the gap between equations of motions of plates and circular cylinders.To fill this gap,a novel approach is proposed in this study for damage identification of circular cylinders.Damage-induced discontinuities of the derivatives of ODSs can be used to gra-phically manifest the occurrence of the damage,and characterize the location and size of the damage.The approach is experimentally validated on a specimen of the circular cylinder component,whose out-of-plane ODSs in an inspection region are acquired through laser scanning using a scanning laser vibrometer.The results suggest that the occurrence,location,and size of the internal damage of the circular cylinder can be identified.展开更多
基金financially supported by the National Key R&D Program of China(No.2024YFC3012701)the National Natural Science Foundation of China(No.42307269)+1 种基金the Growth of Young Scientific and Technological Talents of Guizhou Educational Commission[Qianjiaoji,Grant No.[2024]348]the Foundation Research Project of Kaili University(No.YTH-TD20253I)。
文摘Anti-slide piles are commonly used to stabilise high and steep slopes in earthquake-prone areas in southwestern China.Herein,we investigate the impact of initial damage on the seismic performance of anti-slide piles.For this purpose,we selected a representative slope adjacent to the Jiuzhaigou Bridge in the Sichuan–Qinghai Railway;we employed a three-dimensional dynamic finite element method combined with the local stiffness reduction approach to simulate three different initial-damage scenarios:intact,slightly damaged and heavily damaged.The dynamic displacement,bending moment and shear stress responses of the piles were comprehensively analysed.Using wavelet packet energy spectrum(WPES)analysis,we introduced two indices:the damage index(DPERV)and its increment(|△DPERV|).The results showed that both the initial damage and seismic energy control the peak dynamic response of the piles.Specifically,high initial damage accelerates stiffness degradation,leading to large horizontal displacements,whereas intact piles sustain high bending moments and shear forces.The distribution of|△DPERV|along a pile reveals three post-earthquake performance stages(i.e.minor,moderate and severe),which agree well with the observed mechanical response characteristics and form the basis for targeted reinforcement strategies.The main innovation of this study is the combined use of initial-damage simulation with WPES analysis,thereby establishing a quantitative diagnostic framework(DPERV and|△DPERV|)for anti-slide piles.This framework determines the non-linear relationship between seismic response and damage evolution and provides a rapid,usable tool for health monitoring and post-earthquake decision-making in landslide-prone mountainous railway areas.
基金supported by the National Natural Science Foundation of China(Grant Nos.:11702118,12232001,and 52475190).
文摘The advantages of guided wave detection,such as its ability to propagate over long distances and penetrate deeply,have led to its application in the field of anisotropic damage detection in carbon fiber-reinforced polymer(CFRP).Due to the anisotropy of CFRP,traditional guided wave-based detection methods have difficulty in precisely locating the defect.In this study,we proposed a novel deep learning-based detection method for CFRP by employing image recognition technology for guided wave field inspection.This method is capable of rapidly and accurately extracting defective features from the structure,thereby facilitating precise damage identification.To avoid time-consuming sample data generation by simulation for CFRP,the steady-state guided wave field of the aluminum plates was simulated instead.The isotropic wave field data were then stretched and applied for neural network training.
基金The National Natural Science Foundation of China(No.52361165658,U24A20169).
文摘A dual‑task parallel machine learning framework was developed by integrating a convolutional autoencoder(CAE)and a fully connected neural network(FCNN)via the gradient‑coupled mechanism,enabling simultaneous data compression‑reconstruction and structural damage identification.Under the condition where 40% of the sensor nodes are missing,the model successfully reconstructs the full sensor network with an R^(2) of 0.916 and normalized root mean square error(NRMSE)of 0.0288.Even under significant noise contamination with an SNR of 12 dB,the model maintains strong reconstruction performance,achieving a R^(2) of 0.910 and NRMSE of 0.0253.Forty‑six structural damage scenarios were simulated using the scaled bridge model.The accuracy of spatial localization and quantification of the damage severity using the framework exceeds 99.3%.The proposed framework reduces the training time by 54.4%and iteration counts by 45.5% compared to conventional two‑stage machine learning approaches,demonstrating the efficiency of gradient‑coupled optimization.
文摘Damage identification of the offshore floating wind turbine by vibration/dynamic signals is one of the important and new research fields in the Structural Health Monitoring（SHM）. In this paper a new damage identification method is proposed based on meta-heuristic algorithms using the dynamic response of the TLP（Tension-Leg Platform） floating wind turbine structure. The Genetic Algorithms（GA）, Artificial Immune System（AIS）, Particle Swarm Optimization（PSO）, and Artificial Bee Colony（ABC） are chosen for minimizing the object function, defined properly for damage identification purpose. In addition to studying the capability of mentioned algorithms in correctly identifying the damage, the effect of the response type on the results of identification is studied. Also, the results of proposed damage identification are investigated with considering possible uncertainties of the structure. Finally, for evaluating the proposed method in real condition, a 1/100 scaled experimental setup of TLP Floating Wind Turbine（TLPFWT） is provided in a laboratory scale and the proposed damage identification method is applied to the scaled turbine.
基金Project supported by the National Nature Science Foundation of China(No.11372025)the Aeronautical Science Foundation of China(No.2012ZA51010)Defense Industrial Technology Development Program(Nos.A2120110001 and B2120110011)
文摘The probabilistic damage identification problem with uncertainty in the FE model parameters, external-excitations and measured acceleration responses is studied. The uncertainty in the system is concerned with normally distributed random variables with zero mean value and given covariance. Based on the theoretical model and the measured acceleration responses, the probabilistic structural models in undamaged and damaged states are obtained by two-stage model updating, and then the Probabilities of Damage Existence （PDE） of each element are calculated as the damage criterion. The influences of the location of sensors on the damage identification results are also discussed, where one of the optimal sensor placement techniques, the effective independence method, is used to choose the nodes for measurement. The damage identification results by different numbers of measured nodes and different damage criterions are compared in the numerical example.
基金National Natural Science Foundation of China under Grant No.11172128US National Science Foundation under Grant No.CMMI-0853395+2 种基金the Funds for International Cooperation and Exchange of the National Natural Science Foundation of China under Grant No.61161120323the Jiangsu Foundation for Excellent Talent of China under Grant No.2010-JZ-004the Jiangsu Graduate Training Innovation Project under Grant No.CXLX11_0171
文摘Early structural damage identification to obtain an accurate condition assessment can assist in the reprioritization of structural retrofitting schedules in order to guarantee structural safety. Nowadays, seismic isolation technology has been applied in a wide variety of infrastructure, such as buildings, bridges, etc., and the health conditions of these nonlinear hysteretic vibration isolation systems have received considerable attention. To effectively detect structural damage in vibration isolation systems based on vibration data, three time-domain analysis techniques, referred to as the adaptive extended Kalman filter （AEKF）, adaptive sequential nonlinear least-square estimation （ASNLSE） and adaptive quadratic sum-sqnares error （AQSSE）, have been investigated. In this research, these analysis techniques are compared in terms of accuracy, convergence and efficiency, for structural damage detection using experimental data obtained through a series of laboratory tests based on a base-isolated structural model subjected to E1 Centro and Kobe earthquake excitations. The capability of the AEKF, ASNLSE and AQSSE approaches in tracking structural damage is demonstrated and compared.
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supported by the National Natural Science Foundation of China(Nos.51608245 and 51568041)Natural Science Foundation of Gansu Province(Nos.148RJZA026 and 2014GS02269)
文摘To locate and quantify local damage in a simply supported bridge, in this study, we derived a rotational-angle influence line equation of a simply supported beam model with local damage. Using the diagram multiplication method, we introduce an analytical formula for a novel damage-identification indicator, namely the diff erence of rotational-angle influence linescurvature(DRAIL-C). If the initial stiff ness of the simply supported beam is known, the analytical formula can be effectively used to determine the extent of damage under certain circumstances. We determined the effectiveness and anti-noise performance of this new damage-identification method using numerical examples of a simply supported beam, a simply supported hollow-slab bridge, and a simply supported truss bridge. The results show that the DRAIL-C is directly proportional to the moving concentrated load and inversely proportional to the distance between the bridge support and the concentrated load and the distance between the damaged truss girder and the angle measuring points. The DRAIL-C indicator is more sensitive to the damage in a steel-truss-bridge bottom chord than it is to the other elements.
基金The National Natural Science Foundation of China(No.51378104)。
文摘To improve the accuracy and anti-noise ability of the structural damage identification method,a bridge damage identification method is proposed based on a deep belief network(DBN).The output vector is used to establish the nonlinear mapping relationship between the mode shape and structural damage.The hidden layer of the DBN is trained through a layer-by-layer pre-training.Finally,the backpropagation algorithm is used to fine-tune the entire network.The method is validated using a numerical model of a steel truss bridge.The results show that under the influence of noise and modeling uncertainty,the damage identification method based on the DBN can identify the accurate damage location and degree identification compared with the traditional damage identification method based on an artificial neural network.
基金Project supported by the Natural Science Foundation of China(No. 50378041) and the Specialized Research Fund for the Doc-toral Program of Higher Education (No. 20030487016), China
文摘A new structural damage identification method using limited test static displacement based on grey system theory is proposed in this paper. The grey relation coefficient of displacement curvature is defined and used to locate damage in the structure, and an iterative estimation scheme for solving nonlinear optimization programming problems based on the quadratic programming technique is used to identify the damage magnitude. A numerical example of a cantilever beam with single or multiple damages is used to examine the capability of the proposed grey-theory-based method to localize and identify damages. The factors of meas-urement noise and incomplete test data are also discussed. The numerical results showed that the damage in the structure can be localized correctly through using the grey-related coefficient of displacement curvature, and the damage magnitude can be iden-tified with a high degree of accuracy, regardless of the number of measured displacement nodes. This proposed method only requires limited static test data, which is easily available in practice, and has wide applications in structural damage detection.
基金The National Natural Science Foundation of China(No.52361165658,52378318,52078459).
文摘To enhance the accuracy and efficiency of bridge damage identification,a novel data-driven damage identification method was proposed.First,convolutional autoencoder(CAE)was used to extract key features from the acceleration signal of the bridge structure through data reconstruction.The extreme gradient boosting tree(XGBoost)was then used to perform analysis on the feature data to achieve damage detection with high accuracy and high performance.The proposed method was applied in a numerical simulation study on a three-span continuous girder and further validated experimentally on a scaled model of a cable-stayed bridge.The numerical simulation results show that the identification errors remain within 2.9%for six single-damage cases and within 3.1%for four double-damage cases.The experimental validation results demonstrate that when the tension in a single cable of the cable-stayed bridge decreases by 20%,the method accurately identifies damage at different cable locations using only sensors installed on the main girder,achieving identification accuracies above 95.8%in all cases.The proposed method shows high identification accuracy and generalization ability across various damage scenarios.
基金Supported by National Natural Science Foundation of China(31260062,30860121)Talent Introduction Project of Kunming University(YJL11017)
文摘With Bowytis cinerea and its extracellular macromolecular toxins as the test materials, 30 speiees of plants belonging to 29 genera and 21 families were selected as the test plants to observe the infectivity of B. dnerea and damage status of macromolecular toxins secreted by B. cinerea on plants. The resulsts showed that 17 species of plants were beth infected by B. cinerea and damaged by toxins, accounting for 56.7% of the total plants. Two species of plants could be neither infected by B. cinerea nor damaged by toxins. The study provided the reference for further understanding of pathogenic mechanism of plant pathogenic fungi toxins.
文摘The computerized tomography technique is applied to study the damage propagation of rock for the first time in this paper. CT values and their distribution regularity of damage propaga-tion of rock are analyzed in detail. The relation between CT values and stresses (strains) of the damage propagation of rock is then discussed. This provides the foundation for establishing the constitutive relation of damage propagation of rock.
基金supported by the Specialized Research Fund for the Doctoral Program of Higher Education (20091102120023)the Aeronautical Science Foundation of China (2012ZA51010)+1 种基金the National Natural Science Foundation of China (11002013)Defense Industrial Technology Development Program (A2120110001 and B2120110011)
文摘Based on measured natural frequencies and acceleration responses,a non-probabilistic information fusion technique is proposed for the structural damage detection by adopting the set-membership identification（SMI） and twostep model updating procedure.Due to the insufficiency and uncertainty of information obtained from measurements,the uncertain problem of damage identification is addressed with interval variables in this paper.Based on the first-order Taylor series expansion,the interval bounds of the elemental stiffness parameters in undamaged and damaged models are estimated,respectively.The possibility of damage existence（PoDE） in elements is proposed as the quantitative measure of structural damage probability,which is more reasonable in the condition of insufficient measurement data.In comparison with the identification method based on a single kind of information,the SMI method will improve the accuracy in damage identification,which reflects the information fusion concept based on the non-probabilistic set.A numerical example is performed to demonstrate the feasibility and effectiveness of the proposed technique.
文摘This paper discusses the damage identification in the mooring line system of a floating wind turbine(FWT)exposed to various environmental loads.The proposed method incorporates a non-probabilistic method into artificial neural networks(ANNs).The non-probabilistic method is used to overcome the problem of uncertainties.For this purpose,the interval analysis method is used to calculate the lower and upper bounds of ANNs input data.This data contains some of the natural frequencies utilized to train two different ANNs and predict the output data which is the interval bounds of mooring line stiffness.Additionally,in order to reduce computational time and more importantly,identify damage in various conditions,the proposed method is trained using constant loads(CL)case(deterministic loads,including constant wind speed and airy wave model)and is tested using random loads(RL)case(including Kaimal wind model and JONSWAP wave theory).The superiority of this method is assessed by applying the deterministic method for damage identification.The results demonstrate that the proposed non-probabilistic method identifies the location and severity of damage more accurately compared to a deterministic one.This superiority is getting more remarkable as the difference in uncertainty levels between training and testing data is increasing.
基金supported by the Elite Scholar Program of Northwest A&F University (Grant No.Z111022001)the Research Fund of Department of Transport of Shannxi Province (Grant No.22-23K)the Student Innovation and Entrepreneurship Training Program of China (Project Nos.S202110712555 and S202110712534).
文摘A huge number of old arch bridges located in rural regions are at the peak of maintenance.The health monitoring technology of the long-span bridge is hardly applicable to the small-span bridge,owing to the absence of technical resources and sufficient funds in rural regions.There is an urgent need for an economical,fast,and accurate damage identification solution.The authors proposed a damage identification system of an old arch bridge implemented with amachine learning algorithm,which took the vehicle-induced response as the excitation.A damage index was defined based on wavelet packet theory,and a machine learning sample database collecting the denoised response was constructed.Through comparing three machine learning algorithms:Back-Propagation Neural Network(BPNN),Support Vector Machine(SVM),and Random Forest(R.F.),the R.F.damage identification model were found to have a better recognition ability.Finally,the Particle Swarm Optimization(PSO)algorithm was used to optimize the number of subtrees and split features of the R.F.model.The PSO optimized R.F.model was capable of the identification of different damage levels of old arch bridges with sensitive damage index.The proposed framework is practical and promising for the old bridge’s structural damage identification in rural regions.
文摘Too many sensors and data information in structural health monitoring system raise the problem of how to realize multi-sensor information fusion. An experiment on a three-story frame structure was conducted to obtain vibration test data in 36damage cases. A coupling neural network (NN) based on multi-sensor information fusion is proposed to achieve identification of damage occurrence, damage localization and damage quantification, respectively. First, wavelet packet transform (WPT) is used to extract features of vibration test data from structure with different damage extent. Then, data fusion is conducted by assembling feature vectors of different type sensors. Finally, three sets of coupling NN are constructed to implement decision fusion and damage identification. The results of experimental study proved the validity and feasibility of the proposed methodology.
文摘Convolution neural networks in deep learning can solve the problem of damage identification based on vibration acceleration.By combining multiple 1D DenseNet submodels,a new ensemble learning method is proposed to improve identification accuracy.1D DenseNet is built using standard 1D CNN and DenseNet basic blocks,and the acceleration data obtained from multiple sampling points is brought into the 1D DenseNet training to generate submodels after offset sampling.When using submodels for damage identification,the voting method ideas in ensemble learning are used to vote on the results of each submodel,and then vote centrally.Finally,the cantilever damage problem simulated by ABAQUS is selected as a case study to discuss the excellent performance of the proposed method.The results show that the ensemble 1D DenseNet damage identification method outperforms any submodel in terms of accuracy.Furthermore,the submodel is visualized to demonstrate its operation mode.
基金supported by National Key Research and Development Project(2020YFE0204900)National Natural Science Foundation of China(Grant Nos.61903224,62073193,61873333)Key Research and Development Plan of Shandong Province(Grant Nos.2019TSLH0301,2021CXGC010204).
文摘As a critical structure of aerospace equipment,aluminum alloy stiffened plate will influence the stability of spacecraft in orbit and the normal operation of the system.In this study,a GWO-ELM algorithm-based impact damage identification method is proposed for aluminum alloy stiffened panels to monitor and evaluate the damage condition of such stiffened panels of spacecraft.Firstly,together with numerical simulation,the experimental simulation to obtain the damage acoustic emission signals of aluminum alloy reinforced panels is performed,to establish the damage data.Subsequently,the amplitude-frequency characteristics of impact damage signals are extracted and put into an extreme learning machine(ELM)model to identify the impact location and damage degree,and the Gray Wolf Optimization(GWO)algorithm is employed to update the weight parameters of the model.Finally,experiments are conducted on the irregular aluminum alloy stiffened plate with the size of 2200 mm×500 mm×10 mm,the identification accuracy of impact position and damage degree is 98.90% and 99.55% in 68 test areas,respectively.Comparative experiments with ELM and backpropagation neural networks(BPNN)demonstrate that the impact damage identification of aluminum alloy stiffened plate based on GWO-ELM algorithm can serve as an effective way to monitor spacecraft structural damage.
基金The authors are grateful for the supports from the Changzhou Policy Guidance Plan-International Science and Technology Cooperation(No.CZ20200003)the Anhui International Joint Research Center of Data Diagnosis and Smart Maintenance on Bridge Structures(No.2021AHGHYB01)+1 种基金the Nantong Science and Technology Opening Cooperation Project in 2021(No.BW2021001)the Key R&D Project of Anhui Science and Technology Department(202004b11020026).
文摘With the aid of non-contact measurements of vibrating surfaces through laser scanning,operating deflection shapes(ODSs)with high spatial resolutions can be used to graphically characterize damage in plane structures.Although numerous damage identification approaches relying on laser-measured ODSs have been developed for plate-type structures,they cannot be directly applied to circular cylinders due to the gap between equations of motions of plates and circular cylinders.To fill this gap,a novel approach is proposed in this study for damage identification of circular cylinders.Damage-induced discontinuities of the derivatives of ODSs can be used to gra-phically manifest the occurrence of the damage,and characterize the location and size of the damage.The approach is experimentally validated on a specimen of the circular cylinder component,whose out-of-plane ODSs in an inspection region are acquired through laser scanning using a scanning laser vibrometer.The results suggest that the occurrence,location,and size of the internal damage of the circular cylinder can be identified.
