Enhanced structural damage behavior of liquid-filled tank by reactive material projectile impact

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.

Structural Damage Identification Using Ensemble Deep Convolutional Neural Network Models

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.

Theoretical research on structural damage alarming of long-span bridges using wavelet packet analysis

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.

Structural damage detection method based on information fusion technique

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.

Influence of structural damage on evaluation of microscopic pore structure in marine continental transitional shale of the Southern North China Basin:A method based on the low-temperature N2 adsorption experiment

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.

New supervised learning classifiers for structural damage diagnosis using time series features from a new feature extraction technique

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.

Structural Damage Law of Semiconductor Bridge Detonator under Impact and Overload Environment

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.

Remote Intelligent Identification System of Structural Damage

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

Structural Damage Detection with Damage InductionVector and Best Achievable Vector

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.

A Structural Damage Detection Method Using XGBoost Algorithm on Natural Frequencies

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.

Feature Selection Using Tree Model and Classification Through Convolutional Neural Network for Structural Damage Detection

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.

Simultaneous Identification of Vehicular Parameters and Structural Damages in Bridge

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.

Damage assessment of aircraft wing subjected to blast wave with finite element method and artificial neural network tool

Damage assessment of the wing under blast wave is essential to the vulnerability reduction design of aircraft. This paper introduces a critical relative distance prediction method of aircraft wing damage based on the back-propagation artificial neural network(BP-ANN), which is trained by finite element simulation results. Moreover, the finite element method(FEM) for wing blast damage simulation has been validated by ground explosion tests and further used for damage mode determination and damage characteristics analysis. The analysis results indicate that the wing is more likely to be damaged when the root is struck from vertical directions than others for a small charge. With the increase of TNT equivalent charge, the main damage mode of the wing gradually changes from the local skin tearing to overall structural deformation and the overpressure threshold of wing damage decreases rapidly. Compared to the FEM-based damage assessment, the BP-ANN-based method can predict the wing damage under a random blast wave with an average relative error of 4.78%. The proposed method and conclusions can be used as a reference for damage assessment under blast wave and low-vulnerability design of aircraft structures.

Damage from Particle Impact for Structural Ceramics

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.

Damage warning of suspension bridges based on neural networks under changing temperature conditions

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.

Numerical Simulation-Based Analysis of the Impact of Overloading on Segmentally Assembled Bridges

Segmentally assembled bridges are increasinglyfinding engineering applications in recent years due to their unique advantages,especially as urban viaducts.Vehicle loads are one of the most important variable loads acting on bridge structures.Accordingly,the influence of overloaded vehicles on existing assembled bridge structures is an urgent concern at present.This paper establishes thefinite element model of the segmentally assembled bridge based on ABAQUS software and analyzes the influence of vehicle overload on an assembled girder bridge struc-ture.First,afinite element model corresponding to the target bridge is established based on ABAQUS software,and the load is controlled to simulate vehicle movement in each area of the traveling zone at different times.Sec-ond,the key cross-sections of segmental girder bridges are monitored in real time based on the force character-istics of continuous girder bridges,and they are compared with the simulation results.Finally,a material damage ontology model is introduced,and the structural damage caused by different overloading rates is compared and analyzed.Results show that thefinite element modeling method is accurate by comparing with on-site measured data,and it is suitable for the numerical simulation of segmental girder bridges;Dynamic sensors installed at 1/4L,1/2L,and 3/4L of the segmental girder main beams could be used to identify the dynamic response of segmental girder bridges;The bottom plate of the segmental girder bridge is mostly damaged at the position where the length of the precast beam section changes and the midspan position.With the increase in load,damage in the direction of the bridge develops faster than that in the direction of the transverse bridge.Thefindings of this study can guide maintenance departments in the management and maintenance of bridges and vehicles.

EXPERIMENTAL RESEARCH ON EVALUATING STRUCTURE DAMAGE WITH PIEZOELECTRIC DYNAMIC IMPEDANCE

A dynamic impedance-based structural health monitoring technique isintroduced. According to the direct and the converse piezoelectric property of piezoelectricmaterials, the piezoceramic (PZT) can be used as an actuator and a sensor synchronously. If damageslike cracks, holes, debonding or loose connections are presented in the structure, the physicalvariations of the structure will cause the mechanical impedance modified. On the basis ofintroducing the principle and the theory, the experiment and the analysis on some damages of thestructure are studied by means of the dynamic impedance technique. On the view of experiment, kindsof structural damages are evaluated by the information of dynamic impedance in order to validate thefeasibility of the method.

Damage identification of steel truss bridges based on deep belief network

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.

An Improved Damage Identification Method Based on Mode Information

This paper presents a new algorithm to predict locations and severities of damage in structures by changing modal parameters. An existing algorithm of damage detection is reviewed and the new algorithm is formulated to improve the accuracy of damage locating and severity estimation by eliminating the erratic assumptions and limits in the existing algorithm. The damage prediction accuracy is numerically assessed for each algorithm when applied to a two-dimensional frame structure for which pre-damage and post-damage modal parameters are available for only a few modes of vibration. The analysis results illustrate the improved accuracy of the new algorithm when compared to the existing algorithm.

Time Series Analysis for Vibration-Based Structural Health Monitoring:A Review

Structural health monitoring(SHM)is a vast,interdisciplinary research field whose literature spans several decades with focusing on condition assessment of different types of structures including aerospace,mechanical and civil structures.The need for quantitative global damage detection methods that can be applied to complex structures has led to vibration-based inspection.Statistical time series methods for SHM form an important and rapidly evolving category within the broader vibration-based methods.In the literature on the structural damage detection,many time series-based methods have been proposed.When a considered time series model approximates the vibration response of a structure and model coefficients or residual error are obtained,any deviations in these coefficients or residual error can be inferred as an indication of a change or damage in the structure.Depending on the technique employed,various damage sensitive features have been proposed to capture the deviations.This paper reviews the application of time series analysis for SHM.The different types of time series analysis are described,and the basic principles are explained in detail.Then,the literature is reviewed based on how a damage sensitive feature is formed.In addition,some investigations that have attempted to modify and/or combine time series analysis with other approaches for better damage identification are presented.