On March 28,2025,a catastrophic M 7.9 earthquake shook central Myanmar,causing extensive damage to buildings,significant casualties,and substantial economic loss,making it one of the most severe natural disasters Myan...On March 28,2025,a catastrophic M 7.9 earthquake shook central Myanmar,causing extensive damage to buildings,significant casualties,and substantial economic loss,making it one of the most severe natural disasters Myanmar has faced in recent years.During an on-site investigation that lasted two weeks,widely spreading liquefaction phenomena and associated damage to buildings and other facilities were investigated in 16 villages or districts in the seismic zones,involving hundreds of residential houses,two large bridges,underground storage tanks,etc.Ground fissures generated by liquefaction tore apart houses,which contributed to a significant impact on house damage.The loss of ground bearing capacity is another impact of liquefaction with regard to structural damage,causing the subsidence and tilting of houses.Several consequences of liquefaction were identified,such as lateral spreading,which demolishes houses and causes casualties,as well as damage to two large bridges as a result of lateral spreading.The cases of liquefaction following the Myanmar earthquake reported on herein provide data and lessons for future seismic desigs to help prevent such disasters in Southeast Asia.Through analyzing the phenomena and characteristics of soil liquefaction,the methods and techniques for liquefaction hazard mitigation in other countries also can be improved.展开更多
Reinforced concrete buildings may experience partial damage after earthquakes or some human-induced actions.A decision about the future of those buildings requires detailed analyses,while determining the dynamic chara...Reinforced concrete buildings may experience partial damage after earthquakes or some human-induced actions.A decision about the future of those buildings requires detailed analyses,while determining the dynamic characteristics of a real building in its pre-and post-event situations can guide the analysis.Hence,this study was planned to monitor the dynamic response of an existing six-story,reinforced concrete building with regard to structural damage.The modal characteristics of the original building were initially determined by the use of operational modal analysis.Next,three steps of progressive structural damage were applied to the building.The first damage level peeled off the clear cover of a beam and three columns on a corner of the building’s ground floor.whereas the second and third levels completely razed the damaged columns.Operational modal analysis was repeated at each damage stage to extract the frequencies and detailed mode shapes.Moreover,numerical models based on the finite element method were constructed to confirm the obtained experimental findings.The well-agreed experimental and numerical findings revealed the damage sensitivity of the building’s dynamic response.The quantified amount of frequency change favored a retrofit of the partially damaged buildings rather than their replacement.展开更多
A series of ballistic experiments were performed to investigate the damage behavior of high velocity reactive material projectiles(RMPs) impacting liquid-filled tanks,and the corresponding hydrodynamic ram(HRAM) was s...A series of ballistic experiments were performed to investigate the damage behavior of high velocity reactive material projectiles(RMPs) impacting liquid-filled tanks,and the corresponding hydrodynamic ram(HRAM) was studied in detail.PTFE/Al/W RMPs with steel-like and aluminum-like densities were prepared by a pressing/sintering process.The projectiles impacted a liquid-filled steel tank with front aluminum panel at approximately 1250 m/s.The corresponding cavity evolution characteristics and HRAM pressure were recorded by high-speed camera and pressure acquisition system,and further compared to those of steel and aluminum projectiles.Significantly different from the conical cavity formed by the inert metal projectile,the cavity formed by the RMP appeared as an ellipsoid with a conical front.The RMPs were demonstrated to enhance the radial growth velocity of cavity,the global HRAM pressure amplitude and the front panel damage,indicating the enhanced HRAM and structural damage behavior.Furthermore,combining the impact-induced fragmentation and deflagration characteristics,the cavity evolution of RMPs under the combined effect of kinetic energy impact and chemical energy release was analyzed.The mechanism of enhanced HRAM pressure induced by the RMPs was further revealed based on the theoretical model of the initial impact wave and the impulse analysis.Finally,the linear correlation between the deformation-thickness ratio and the non-dimensional impulse for the front panel was obtained and analyzed.It was determined that the enhanced near-field impulse induced by the RMPs was the dominant reason for the enhanced structural damage behavior.展开更多
Structural damage detection(SDD)remains highly challenging,due to the difficulty in selecting the optimal damage features from a vast amount of information.In this study,a tree model-based method using decision tree a...Structural damage detection(SDD)remains highly challenging,due to the difficulty in selecting the optimal damage features from a vast amount of information.In this study,a tree model-based method using decision tree and random forest was employed for feature selection of vibration response signals in SDD.Signal datasets were obtained by numerical experiments and vibration experiments,respectively.Dataset features extracted using this method were input into a convolutional neural network to determine the location of structural damage.Results indicated a 5%to 10%improvement in detection accuracy compared to using original datasets without feature selection,demonstrating the feasibility of this method.The proposed method,based on tree model and classification,addresses the issue of extracting effective information from numerous vibration response signals in structural health monitoring.展开更多
The state equation and observation equation of the structural dynamic systems under various analysis scales are derived based on wavelet packet analysis. The time-frequency properties of structural dynamic response un...The state equation and observation equation of the structural dynamic systems under various analysis scales are derived based on wavelet packet analysis. The time-frequency properties of structural dynamic response under various scales are further formulated. The theoretical analysis results reveal that the wavelet packet energy spectrum （WPES） obtained from wavelet packet decomposition of structural dynamic response will detect the presence of structural damage. The sensitivity analysis of the WPES to structural damage and measurement noise is also performed. The transfer properties of the structural system matrix and the observation noise under various analysis scales are formulated, which verify the damage alarming reliability using the proposed WPES with preferable damage sensitivity and noise robusticity.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
Structural damage from sample preparation processes such as cutting and polishing may change the pore structure of rocks.However,changes in pore structure caused by this structural damage from crushing and its effect ...Structural damage from sample preparation processes such as cutting and polishing may change the pore structure of rocks.However,changes in pore structure caused by this structural damage from crushing and its effect on marine continental transitional shale have not been well documented.The changes of microscopic pore structure in marine continental transitional shale during the sample preparation have important research value for subsequent exploration and development of shale gas.In this study,the pore structures of transitional shale samples from the Shanxi-Taiyuan Formation of the Southern North China Basin under different degrees of damage were analyzed through low-temperature N;adsorption experiments,combined with X-ray diffraction,total organic carbon,vitrinite reflectance analysis,and scanning electron microscopy.The results showed that(1)With increasing structural damage,the specific surface area(SSA)changed within relatively tight bounds,while the pore volume(PV)varied significantly,and the growth rate(maximum)exhibited a certain critical value with the crushing mesh number increasing from 20 to 200.(2)The ratio of SSA to PV can be used as a potential proxy for evaluating the influence of changes in the pore structure.(3)Correlation analysis revealed that the microscopic pore structure of marine continental transitional shale from the Shanxi-Taiyuan Formations is mainly controlled by organic matter and clay minerals.Clay minerals play a leading role in the development of microscopic pores and changes in pore structure.展开更多
In this paper, we present a method for simultaneously identifying the vehicular parameters and the structural damage of bridges. By using the dynamic response data of bridge in coupled vibration state and the algorith...In this paper, we present a method for simultaneously identifying the vehicular parameters and the structural damage of bridges. By using the dynamic response data of bridge in coupled vibration state and the algorithm for the inverse problem, the vehicle-bridge coupling model is built through combining the motion equations of both vehicle and the bridge based on their interaction force relationship at contact point. Load shape function method and Newmark iterative method are used to solve the vibration response of the coupled system. Penalty function method and regularization method are interchangeable in the process until the error is less than the allowable value. The proposed method is applied on a single-span girders bridge, and the recognition results verify the feasibility, high accuracy and robustness of the method.展开更多
The motivation for this article is to propose new damage classifiers based on a supervised learning problem for locating and quantifying damage.A new feature extraction approach using time series analysis is introduce...The motivation for this article is to propose new damage classifiers based on a supervised learning problem for locating and quantifying damage.A new feature extraction approach using time series analysis is introduced to extract damage-sensitive features from auto-regressive models.This approach sets out to improve current feature extraction techniques in the context of time series modeling.The coefficients and residuals of the AR model obtained from the proposed approach are selected as the main features and are applied to the proposed supervised learning classifiers that are categorized as coefficient-based and residual-based classifiers.These classifiers compute the relative errors in the extracted features between the undamaged and damaged states.Eventually,the abilities of the proposed methods to localize and quantify single and multiple damage scenarios are verified by applying experimental data for a laboratory frame and a four-story steel structure.Comparative analyses are performed to validate the superiority of the proposed methods over some existing techniques.Results show that the proposed classifiers,with the aid of extracted features from the proposed feature extraction approach,are able to locate and quantify damage;however,the residual-based classifiers yield better results than the coefficient-based classifiers.Moreover,these methods are superior to some classical techniques.展开更多
The existing strategy for evaluating the damage condition of structures mostly focuses on feedback supplied by traditional visualmethods,which may result in an unreliable damage characterization due to inspector subje...The existing strategy for evaluating the damage condition of structures mostly focuses on feedback supplied by traditional visualmethods,which may result in an unreliable damage characterization due to inspector subjectivity or insufficient level of expertise.As a result,a robust,reliable,and repeatable method of damage identification is required.Ensemble learning algorithms for identifying structural damage are evaluated in this article,which use deep convolutional neural networks,including simple averaging,integrated stacking,separate stacking,and hybridweighted averaging ensemble and differential evolution(WAE-DE)ensemblemodels.Damage identification is carried out on three types of damage.The proposed algorithms are used to analyze the damage of 4585 structural images.The effectiveness of the ensemble learning techniques is evaluated using the confusion matrix.For the testing dataset,the confusion matrix achieved an accuracy of 94 percent and a minimum recall of 92 percent for the best model(WAE-DE)in distinguishing damage types as flexural,shear,combined,or undamaged.展开更多
Aiming to know the requirement of penetrating the munition semiconductor bridge detonator under the impact overload environment, the impact overload simulation device and the structural finite element software ANSYS/A...Aiming to know the requirement of penetrating the munition semiconductor bridge detonator under the impact overload environment, the impact overload simulation device and the structural finite element software ANSYS/AUTODYN are used to study the variation of the axial dimension, charge and the chip gap of the semiconductor bridge detonator under the impact overload environment. The typical semiconductor bridge detonator is affected by the acceleration, and the strain increases with the increase of the acceleration. The semiconductor bridge detonator shows axial compression, in which the size becomes smaller, and the structural deformation occurs at the output end of the semiconductor bridge detonator. The typical semiconductor bridge detonator is elastically deformed when the acceleration is less than 40 000 g. When the acceleration is more than40 000 g, the semiconductor bridge detonator housing is plastically deformed. The gap between the drug column and the chip is divided into three stages with the increase of the acceleration. Initially,with the increase of the acceleration, the gap rises rapidly until the acceleration reaches 43 000 g,and when the gap reaches the maximum, the gap decreases rapidly with the increase of the acceleration. When the acceleration reaches 57 000 g, the gap tends to be 0 μm in the initial state, and then the gap does not change with the acceleration to keep tending to 0 μm.展开更多
The focus of this paper is to build the damage identify system, which performs “system identification” to detect the positions and extens of structural damages. The identification of structural damage can be charact...The focus of this paper is to build the damage identify system, which performs “system identification” to detect the positions and extens of structural damages. The identification of structural damage can be characterized as a nonlinear process which linear prediction models such as linear regression are not suitable. However, neural network techniques may provide an effective tool for system identification. The method of damage identification using the radial basis function neural network (RBFNN) is presented in this paper. Using this method, a simple reinforced concrete structure has been tested both in the absence and presence of noise. The results show that the RBFNN identification technology can be used with related success for the solution of dynamic damage identification problems, even in the presence of a noisy identify data. Furthermore, a remote identification system based on that is set up with Java Technologies. Key words RBFNN - inteligent identification - structural damage - Brower/Server (B/S) model CLC number TP 183 Foundation item: Supported by the Natural Science Foundation of Hubei Province in China (2001ABB0778), The Science and Technology Foundation for Wuhan Young Scholar (20015005039)Biography: RAO Wen-bi (1967-), female, Ph. D, associate professor, research direction: artificial intelligence展开更多
This paper presents a new method using the damage induction vector (DIV) and the best achievable vector (BAV) by which the change of modes due to structural damage can be applied to detcrnlinc the location and scale o...This paper presents a new method using the damage induction vector (DIV) and the best achievable vector (BAV) by which the change of modes due to structural damage can be applied to detcrnlinc the location and scale of damage in structures. By the DIV, undamagc elements can be castly identified and the damage detection can be limited to a few domains of the structure. The structural damage is located by conlputing the Euclidean distance betwcen the DIV and its BAV. The loss of both stiffness and mass properties can be located and quantified.The characteristic of this method is less calculation and there is no limitation of damage scale. Finally, the effectiveness of the method is demonstrated by detecting the damages of the shallow arches.展开更多
Structural damage detection and monitoring are vital in product lifecycle management of aeronautic system in space utilization.In this paper,a method based on vibration characteristics and ensemble learning algorithm ...Structural damage detection and monitoring are vital in product lifecycle management of aeronautic system in space utilization.In this paper,a method based on vibration characteristics and ensemble learning algorithm is proposed to achieve damage detection.Based on the small volume of modal frequency data for intact and damage structures,the extreme gradient boosting algorithm enables robust damage localization under noise condition of wing-like structures on numerical data.The method shows satisfactory performance on localizing damage with random geometrical profiles in most cases.展开更多
Fatigue damage is a primary contributor to the failure of composite structures,underscoring the critical importance of monitoring its progression to ensure structural safety.This paper introduces an innovative approac...Fatigue damage is a primary contributor to the failure of composite structures,underscoring the critical importance of monitoring its progression to ensure structural safety.This paper introduces an innovative approach to fatigue damage monitoring in composite structures,leveraging a hybrid methodology that integrates the Whale Optimization Algorithm(WOA)-Backpropagation(BP)neural network with an ultrasonic guided wave feature selection algorithm.Initially,a network of piezoelectric ceramic sensors is employed to transmit and capture ultrasonic-guided waves,thereby establishing a signal space that correlates with the structural condition.Subsequently,the Relief-F algorithm is applied for signal feature extraction,culminating in the formation of a feature matrix.This matrix is then utilized to train the WOA-BP neural network,which optimizes the fatigue damage identification model globally.The proposed model’s efficacy in quantifying fatigue damage is tested against fatigue test datasets,with its performance benchmarked against the traditional BP neural network algorithm.The findings demonstrate that the WOA-BP neural network model not only surpasses the BP model in predictive accuracy but also exhibits enhanced global search capabilities.The effect of different sensor-receiver path signals on the model damage recognition results is also discussed.The results of the discussion found that the path directly through the damaged area is more accurate in modeling damage recognition compared to the path signals away from the damaged area.Consequently,the proposed monitoring method in the fatigue test dataset is adept at accurately tracking and recognizing the progression of fatigue damage.展开更多
Structural integrity is essential for safety in infrastructure,as it can help prevent catastrophic failures and financial losses.The significance of vibration-based damage detection has grown substantially in fields s...Structural integrity is essential for safety in infrastructure,as it can help prevent catastrophic failures and financial losses.The significance of vibration-based damage detection has grown substantially in fields such as civil and mechanical engineering.Concurrently,the advancements in computational capacities have facilitated the integration of machine learning into damage detection processes through post-processing algorithms.Nevertheless,these require extensive data from structure-affixed sensors,raising computational requirements.In an effort to address this challenge,we propose a novel approach utilizing a pre-trained convolutional neural network(CNN)based on images to identify and assess structural damage.This method involves employing wavelet transform and scalograms to convert numerical acceleration data into image data,preserving spatial and temporal information more effectively compared to conventional Fourier transform frequency analysis.Six acceleration data channels are collected from carefully chosen nodes on a mini bridge model and a corresponding finite element bridge model,to train the CNN.The efficiency of training is further enhanced by applying transfer machine learning through two pre-trained CNNs,namely Alexnet and Resnet.We evaluate our method using different damage scenarios,and both Alexnet and Resnet show prediction accuracies over 90%.展开更多
Researchers have achieved notable advancements over the years in exploring ship damage and stability resulting from underwater explosions(UNDEX).However,numerous challenges and open questions remain in this field.In t...Researchers have achieved notable advancements over the years in exploring ship damage and stability resulting from underwater explosions(UNDEX).However,numerous challenges and open questions remain in this field.In this study,the research progress of UNDEX load is first reviewed,which covers the explosion load during the shock wave and bubble pulsation stages.Subsequently,the research progress of ship damage caused by UNDEX is reviewed from two aspects:contact explosion and noncontact explosion.Finally,the research progress of ship navigation stability caused by UNDEX is reviewed from three aspects:natural factors,ship’s internal factors,and explosion factors.Analysis reveals that most existing research has focused on the damage to displacement ships caused by UNDEX.Meanwhile,less attention has been paid to the damage and stability of non-displacement ships caused by UNDEX,which are worthy of discussion.展开更多
The damage distribution of the same type of aircraft in similar service environments should be similar. Based on this assumption, to perform the maintenance and repair of aircraft composite structures, the damage of c...The damage distribution of the same type of aircraft in similar service environments should be similar. Based on this assumption, to perform the maintenance and repair of aircraft composite structures, the damage of composite structures in a certain type of aircraft were investigated. The time-varying damage distribution model was established and verified based on the damage of a 16-aircraft fleet. The results show that the quantitative proportions of structural damage are 74% for skin delamination, 22% for stringer delamination and 3% for stringer-skin interface debonding. The amount of structural damages increases linearly with service time while the proportion of different damages does not change. As the service time increases, the geometric parameter distribution of damage for the same type of aircraft gradually converges, which can be approximated using the same function. There are certain differences in the proportion and geometric parameter distribution of damages among different components and locations, and the differences do not change over time.展开更多
In this study, the relationship between the maximum impact force and velocity of partjcle has been derived on the basis of elastic theory and energy principle. Critical impact force and critical speed which cause init...In this study, the relationship between the maximum impact force and velocity of partjcle has been derived on the basis of elastic theory and energy principle. Critical impact force and critical speed which cause initial damage is anaIVsed and its analytical expression is presented. The impact force for six dlfferent materials was measu red at the same condition to investigate the v8riation of impact pararneter with material properties. The authors provide a simple test method and experimental de vice to imitate the impact of moving particle, A series of experiments on ceramics and gIass were car ried out to study the dependence of residual strength on the impulse.展开更多
This paper aims at successive structural damage detection of long-span bridges under changing temperature conditions.First,the frequency-temperature correlation models of bridges are formulated by means of artificial ...This paper aims at successive structural damage detection of long-span bridges under changing temperature conditions.First,the frequency-temperature correlation models of bridges are formulated by means of artificial neural network techniques to eliminate the temperature effects on the measured modal frequencies.Then,the measured modal frequencies under various temperatures are normalized to a reference temperature,based on which the auto-associative network is trained to monitor signal damage occurrences by means of neural-network-based novelty detection techniques.The effectiveness of the proposed approach is examined in the Runyang Suspension Bridge using 236-day health monitoring data.The results reveal that the seasonal change of environmental temperature accounts for variations in the measured modal frequencies with averaged variances of 2.0%.And the approach exhibits good capability for detecting the damage-induced 0.1% variance of modal frequencies and it is suitable for online condition monitoring of suspension bridges.展开更多
基金Scientific Research Fund of Institute of Engineering Mechanics,China Earthquake Administration under Grant No.2024A03。
文摘On March 28,2025,a catastrophic M 7.9 earthquake shook central Myanmar,causing extensive damage to buildings,significant casualties,and substantial economic loss,making it one of the most severe natural disasters Myanmar has faced in recent years.During an on-site investigation that lasted two weeks,widely spreading liquefaction phenomena and associated damage to buildings and other facilities were investigated in 16 villages or districts in the seismic zones,involving hundreds of residential houses,two large bridges,underground storage tanks,etc.Ground fissures generated by liquefaction tore apart houses,which contributed to a significant impact on house damage.The loss of ground bearing capacity is another impact of liquefaction with regard to structural damage,causing the subsidence and tilting of houses.Several consequences of liquefaction were identified,such as lateral spreading,which demolishes houses and causes casualties,as well as damage to two large bridges as a result of lateral spreading.The cases of liquefaction following the Myanmar earthquake reported on herein provide data and lessons for future seismic desigs to help prevent such disasters in Southeast Asia.Through analyzing the phenomena and characteristics of soil liquefaction,the methods and techniques for liquefaction hazard mitigation in other countries also can be improved.
基金supported by the Scientific and Technological Research Council of Türkiye(TUBITAK)under Research Grant 116M254.
文摘Reinforced concrete buildings may experience partial damage after earthquakes or some human-induced actions.A decision about the future of those buildings requires detailed analyses,while determining the dynamic characteristics of a real building in its pre-and post-event situations can guide the analysis.Hence,this study was planned to monitor the dynamic response of an existing six-story,reinforced concrete building with regard to structural damage.The modal characteristics of the original building were initially determined by the use of operational modal analysis.Next,three steps of progressive structural damage were applied to the building.The first damage level peeled off the clear cover of a beam and three columns on a corner of the building’s ground floor.whereas the second and third levels completely razed the damaged columns.Operational modal analysis was repeated at each damage stage to extract the frequencies and detailed mode shapes.Moreover,numerical models based on the finite element method were constructed to confirm the obtained experimental findings.The well-agreed experimental and numerical findings revealed the damage sensitivity of the building’s dynamic response.The quantified amount of frequency change favored a retrofit of the partially damaged buildings rather than their replacement.
基金supported by the Youth Foundation of State Key Laboratory of Explosion Science and Technology (Grant No.QNKT22-12)the State Key Program of National Natural Science Foundation of China (Grant No.12132003)。
文摘A series of ballistic experiments were performed to investigate the damage behavior of high velocity reactive material projectiles(RMPs) impacting liquid-filled tanks,and the corresponding hydrodynamic ram(HRAM) was studied in detail.PTFE/Al/W RMPs with steel-like and aluminum-like densities were prepared by a pressing/sintering process.The projectiles impacted a liquid-filled steel tank with front aluminum panel at approximately 1250 m/s.The corresponding cavity evolution characteristics and HRAM pressure were recorded by high-speed camera and pressure acquisition system,and further compared to those of steel and aluminum projectiles.Significantly different from the conical cavity formed by the inert metal projectile,the cavity formed by the RMP appeared as an ellipsoid with a conical front.The RMPs were demonstrated to enhance the radial growth velocity of cavity,the global HRAM pressure amplitude and the front panel damage,indicating the enhanced HRAM and structural damage behavior.Furthermore,combining the impact-induced fragmentation and deflagration characteristics,the cavity evolution of RMPs under the combined effect of kinetic energy impact and chemical energy release was analyzed.The mechanism of enhanced HRAM pressure induced by the RMPs was further revealed based on the theoretical model of the initial impact wave and the impulse analysis.Finally,the linear correlation between the deformation-thickness ratio and the non-dimensional impulse for the front panel was obtained and analyzed.It was determined that the enhanced near-field impulse induced by the RMPs was the dominant reason for the enhanced structural damage behavior.
基金supported by the Project of Guangdong Province High Level University Construction for Guangdong University of Technology(Grant No.262519003)the College Student Innovation Training Program of Guangdong University of Technology(Grant Nos.S202211845154 and xj2023118450384).
文摘Structural damage detection(SDD)remains highly challenging,due to the difficulty in selecting the optimal damage features from a vast amount of information.In this study,a tree model-based method using decision tree and random forest was employed for feature selection of vibration response signals in SDD.Signal datasets were obtained by numerical experiments and vibration experiments,respectively.Dataset features extracted using this method were input into a convolutional neural network to determine the location of structural damage.Results indicated a 5%to 10%improvement in detection accuracy compared to using original datasets without feature selection,demonstrating the feasibility of this method.The proposed method,based on tree model and classification,addresses the issue of extracting effective information from numerous vibration response signals in structural health monitoring.
文摘The state equation and observation equation of the structural dynamic systems under various analysis scales are derived based on wavelet packet analysis. The time-frequency properties of structural dynamic response under various scales are further formulated. The theoretical analysis results reveal that the wavelet packet energy spectrum （WPES） obtained from wavelet packet decomposition of structural dynamic response will detect the presence of structural damage. The sensitivity analysis of the WPES to structural damage and measurement noise is also performed. The transfer properties of the structural system matrix and the observation noise under various analysis scales are formulated, which verify the damage alarming reliability using the proposed WPES with preferable damage sensitivity and noise robusticity.
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the financial support by the National Natural Science Foundation of China(Grant No.41927801)。
文摘Structural damage from sample preparation processes such as cutting and polishing may change the pore structure of rocks.However,changes in pore structure caused by this structural damage from crushing and its effect on marine continental transitional shale have not been well documented.The changes of microscopic pore structure in marine continental transitional shale during the sample preparation have important research value for subsequent exploration and development of shale gas.In this study,the pore structures of transitional shale samples from the Shanxi-Taiyuan Formation of the Southern North China Basin under different degrees of damage were analyzed through low-temperature N;adsorption experiments,combined with X-ray diffraction,total organic carbon,vitrinite reflectance analysis,and scanning electron microscopy.The results showed that(1)With increasing structural damage,the specific surface area(SSA)changed within relatively tight bounds,while the pore volume(PV)varied significantly,and the growth rate(maximum)exhibited a certain critical value with the crushing mesh number increasing from 20 to 200.(2)The ratio of SSA to PV can be used as a potential proxy for evaluating the influence of changes in the pore structure.(3)Correlation analysis revealed that the microscopic pore structure of marine continental transitional shale from the Shanxi-Taiyuan Formations is mainly controlled by organic matter and clay minerals.Clay minerals play a leading role in the development of microscopic pores and changes in pore structure.
基金Supported by the National Natural Science Foundation of China(41402271)Guizhou Science and Technology Cooperation Project(LH[2016]7043)Young Science and Technology Talents Growth Project of Guizhou Provincial Department of Education(KY-[2016]-282)
文摘In this paper, we present a method for simultaneously identifying the vehicular parameters and the structural damage of bridges. By using the dynamic response data of bridge in coupled vibration state and the algorithm for the inverse problem, the vehicle-bridge coupling model is built through combining the motion equations of both vehicle and the bridge based on their interaction force relationship at contact point. Load shape function method and Newmark iterative method are used to solve the vibration response of the coupled system. Penalty function method and regularization method are interchangeable in the process until the error is less than the allowable value. The proposed method is applied on a single-span girders bridge, and the recognition results verify the feasibility, high accuracy and robustness of the method.
文摘The motivation for this article is to propose new damage classifiers based on a supervised learning problem for locating and quantifying damage.A new feature extraction approach using time series analysis is introduced to extract damage-sensitive features from auto-regressive models.This approach sets out to improve current feature extraction techniques in the context of time series modeling.The coefficients and residuals of the AR model obtained from the proposed approach are selected as the main features and are applied to the proposed supervised learning classifiers that are categorized as coefficient-based and residual-based classifiers.These classifiers compute the relative errors in the extracted features between the undamaged and damaged states.Eventually,the abilities of the proposed methods to localize and quantify single and multiple damage scenarios are verified by applying experimental data for a laboratory frame and a four-story steel structure.Comparative analyses are performed to validate the superiority of the proposed methods over some existing techniques.Results show that the proposed classifiers,with the aid of extracted features from the proposed feature extraction approach,are able to locate and quantify damage;however,the residual-based classifiers yield better results than the coefficient-based classifiers.Moreover,these methods are superior to some classical techniques.
文摘The existing strategy for evaluating the damage condition of structures mostly focuses on feedback supplied by traditional visualmethods,which may result in an unreliable damage characterization due to inspector subjectivity or insufficient level of expertise.As a result,a robust,reliable,and repeatable method of damage identification is required.Ensemble learning algorithms for identifying structural damage are evaluated in this article,which use deep convolutional neural networks,including simple averaging,integrated stacking,separate stacking,and hybridweighted averaging ensemble and differential evolution(WAE-DE)ensemblemodels.Damage identification is carried out on three types of damage.The proposed algorithms are used to analyze the damage of 4585 structural images.The effectiveness of the ensemble learning techniques is evaluated using the confusion matrix.For the testing dataset,the confusion matrix achieved an accuracy of 94 percent and a minimum recall of 92 percent for the best model(WAE-DE)in distinguishing damage types as flexural,shear,combined,or undamaged.
文摘Aiming to know the requirement of penetrating the munition semiconductor bridge detonator under the impact overload environment, the impact overload simulation device and the structural finite element software ANSYS/AUTODYN are used to study the variation of the axial dimension, charge and the chip gap of the semiconductor bridge detonator under the impact overload environment. The typical semiconductor bridge detonator is affected by the acceleration, and the strain increases with the increase of the acceleration. The semiconductor bridge detonator shows axial compression, in which the size becomes smaller, and the structural deformation occurs at the output end of the semiconductor bridge detonator. The typical semiconductor bridge detonator is elastically deformed when the acceleration is less than 40 000 g. When the acceleration is more than40 000 g, the semiconductor bridge detonator housing is plastically deformed. The gap between the drug column and the chip is divided into three stages with the increase of the acceleration. Initially,with the increase of the acceleration, the gap rises rapidly until the acceleration reaches 43 000 g,and when the gap reaches the maximum, the gap decreases rapidly with the increase of the acceleration. When the acceleration reaches 57 000 g, the gap tends to be 0 μm in the initial state, and then the gap does not change with the acceleration to keep tending to 0 μm.
文摘The focus of this paper is to build the damage identify system, which performs “system identification” to detect the positions and extens of structural damages. The identification of structural damage can be characterized as a nonlinear process which linear prediction models such as linear regression are not suitable. However, neural network techniques may provide an effective tool for system identification. The method of damage identification using the radial basis function neural network (RBFNN) is presented in this paper. Using this method, a simple reinforced concrete structure has been tested both in the absence and presence of noise. The results show that the RBFNN identification technology can be used with related success for the solution of dynamic damage identification problems, even in the presence of a noisy identify data. Furthermore, a remote identification system based on that is set up with Java Technologies. Key words RBFNN - inteligent identification - structural damage - Brower/Server (B/S) model CLC number TP 183 Foundation item: Supported by the Natural Science Foundation of Hubei Province in China (2001ABB0778), The Science and Technology Foundation for Wuhan Young Scholar (20015005039)Biography: RAO Wen-bi (1967-), female, Ph. D, associate professor, research direction: artificial intelligence
文摘This paper presents a new method using the damage induction vector (DIV) and the best achievable vector (BAV) by which the change of modes due to structural damage can be applied to detcrnlinc the location and scale of damage in structures. By the DIV, undamagc elements can be castly identified and the damage detection can be limited to a few domains of the structure. The structural damage is located by conlputing the Euclidean distance betwcen the DIV and its BAV. The loss of both stiffness and mass properties can be located and quantified.The characteristic of this method is less calculation and there is no limitation of damage scale. Finally, the effectiveness of the method is demonstrated by detecting the damages of the shallow arches.
文摘Structural damage detection and monitoring are vital in product lifecycle management of aeronautic system in space utilization.In this paper,a method based on vibration characteristics and ensemble learning algorithm is proposed to achieve damage detection.Based on the small volume of modal frequency data for intact and damage structures,the extreme gradient boosting algorithm enables robust damage localization under noise condition of wing-like structures on numerical data.The method shows satisfactory performance on localizing damage with random geometrical profiles in most cases.
基金funded by the Key Program of the National Natural Science Foundation of China(U2341235)Youth Fund for Basic Research Program of Jiangnan University(JUSRP123003)+2 种基金Postgraduate Research&Practice Innovation Program of Jiangsu Province(SJCX23_1237)the National Key R&D Program of China(2018YFA0702800)Key Technologies R&D Program of CNBM(2023SJYL01).
文摘Fatigue damage is a primary contributor to the failure of composite structures,underscoring the critical importance of monitoring its progression to ensure structural safety.This paper introduces an innovative approach to fatigue damage monitoring in composite structures,leveraging a hybrid methodology that integrates the Whale Optimization Algorithm(WOA)-Backpropagation(BP)neural network with an ultrasonic guided wave feature selection algorithm.Initially,a network of piezoelectric ceramic sensors is employed to transmit and capture ultrasonic-guided waves,thereby establishing a signal space that correlates with the structural condition.Subsequently,the Relief-F algorithm is applied for signal feature extraction,culminating in the formation of a feature matrix.This matrix is then utilized to train the WOA-BP neural network,which optimizes the fatigue damage identification model globally.The proposed model’s efficacy in quantifying fatigue damage is tested against fatigue test datasets,with its performance benchmarked against the traditional BP neural network algorithm.The findings demonstrate that the WOA-BP neural network model not only surpasses the BP model in predictive accuracy but also exhibits enhanced global search capabilities.The effect of different sensor-receiver path signals on the model damage recognition results is also discussed.The results of the discussion found that the path directly through the damaged area is more accurate in modeling damage recognition compared to the path signals away from the damaged area.Consequently,the proposed monitoring method in the fatigue test dataset is adept at accurately tracking and recognizing the progression of fatigue damage.
基金supported by the ATC+Program(20014127,Development of a smart monitoring system integrating 3D printed battery-free antenna sensor technology with AI optimization)funded by the Ministry of Trade,Industry&Energy(MOTIE,Korea).
文摘Structural integrity is essential for safety in infrastructure,as it can help prevent catastrophic failures and financial losses.The significance of vibration-based damage detection has grown substantially in fields such as civil and mechanical engineering.Concurrently,the advancements in computational capacities have facilitated the integration of machine learning into damage detection processes through post-processing algorithms.Nevertheless,these require extensive data from structure-affixed sensors,raising computational requirements.In an effort to address this challenge,we propose a novel approach utilizing a pre-trained convolutional neural network(CNN)based on images to identify and assess structural damage.This method involves employing wavelet transform and scalograms to convert numerical acceleration data into image data,preserving spatial and temporal information more effectively compared to conventional Fourier transform frequency analysis.Six acceleration data channels are collected from carefully chosen nodes on a mini bridge model and a corresponding finite element bridge model,to train the CNN.The efficiency of training is further enhanced by applying transfer machine learning through two pre-trained CNNs,namely Alexnet and Resnet.We evaluate our method using different damage scenarios,and both Alexnet and Resnet show prediction accuracies over 90%.
基金Supported by the Key R&D Program of Heilongjiang Province(Grant No.JD22A024)the Science Fund for Excellent Youth Foundation of Heilongjiang Province of China(Grant No.YQ2021E010).
文摘Researchers have achieved notable advancements over the years in exploring ship damage and stability resulting from underwater explosions(UNDEX).However,numerous challenges and open questions remain in this field.In this study,the research progress of UNDEX load is first reviewed,which covers the explosion load during the shock wave and bubble pulsation stages.Subsequently,the research progress of ship damage caused by UNDEX is reviewed from two aspects:contact explosion and noncontact explosion.Finally,the research progress of ship navigation stability caused by UNDEX is reviewed from three aspects:natural factors,ship’s internal factors,and explosion factors.Analysis reveals that most existing research has focused on the damage to displacement ships caused by UNDEX.Meanwhile,less attention has been paid to the damage and stability of non-displacement ships caused by UNDEX,which are worthy of discussion.
文摘The damage distribution of the same type of aircraft in similar service environments should be similar. Based on this assumption, to perform the maintenance and repair of aircraft composite structures, the damage of composite structures in a certain type of aircraft were investigated. The time-varying damage distribution model was established and verified based on the damage of a 16-aircraft fleet. The results show that the quantitative proportions of structural damage are 74% for skin delamination, 22% for stringer delamination and 3% for stringer-skin interface debonding. The amount of structural damages increases linearly with service time while the proportion of different damages does not change. As the service time increases, the geometric parameter distribution of damage for the same type of aircraft gradually converges, which can be approximated using the same function. There are certain differences in the proportion and geometric parameter distribution of damages among different components and locations, and the differences do not change over time.
文摘In this study, the relationship between the maximum impact force and velocity of partjcle has been derived on the basis of elastic theory and energy principle. Critical impact force and critical speed which cause initial damage is anaIVsed and its analytical expression is presented. The impact force for six dlfferent materials was measu red at the same condition to investigate the v8riation of impact pararneter with material properties. The authors provide a simple test method and experimental de vice to imitate the impact of moving particle, A series of experiments on ceramics and gIass were car ried out to study the dependence of residual strength on the impulse.
基金The National Natural Science Foundation of China(No.50725828,50808041)the Natural Science Foundation of Jiangsu Province(No.BK2008312)the Ph.D.Programs Foundation of Ministry of Education of China(No.200802861011)
文摘This paper aims at successive structural damage detection of long-span bridges under changing temperature conditions.First,the frequency-temperature correlation models of bridges are formulated by means of artificial neural network techniques to eliminate the temperature effects on the measured modal frequencies.Then,the measured modal frequencies under various temperatures are normalized to a reference temperature,based on which the auto-associative network is trained to monitor signal damage occurrences by means of neural-network-based novelty detection techniques.The effectiveness of the proposed approach is examined in the Runyang Suspension Bridge using 236-day health monitoring data.The results reveal that the seasonal change of environmental temperature accounts for variations in the measured modal frequencies with averaged variances of 2.0%.And the approach exhibits good capability for detecting the damage-induced 0.1% variance of modal frequencies and it is suitable for online condition monitoring of suspension bridges.
