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.

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.

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.

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.

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.

Damage Characterization of Composite Structures Using Difference Peak Signal-to-Noise Ratio as a Function of Variable Wavelets (ΔPSNR-ΔW)

A novel approach employs the principles of medical image analysis using Wavelet Transform (WT) and Difference Peak Signal-to-Noise Ratio (ΔPSNR). Both techniques are combined as a function of different decomposing levels of wavelets and various image search through and slicing levels, which is implemented under MATLAB environment. In this new approach, the structural change due to damage in the component or the presence of foreign bodies appearing in an image taken for a specific structure is uncovered with its extent determined after applying the search through algorithm. Such alteration of the composite structure, which could be masked by the presence of noise, is accounted for using combined WT and PSNR. Effect of Artifacts and Blurring caused by different wavelet types is investigated before choosing an appropriate wavelet, namely Sym8. This new approach, which also reduces the required layers of search within an image, produces a pattern matrix per damaged area and is an excellent way in tracing and modeling damage in structures with ability to predict effects of further damage on components and further application to artificial limbs that could suffer damage and affect users mobility.

Damage Assessment of Reinforced Concrete Structures through Damage Indices:A State-of-the-Art Review

Due to the developments of computer science and technology in recent years,computer models and numerical simulations for large and complicated structures can be done.Among the vast information and results obtained from the analysis and simulations,the damage performance is of great importance since this damage might cause enormous losses for society and humanity,notably in cases of severe damage occurring.One of the most effective tools to handle the results about the damage performance of the structure is the damage index(DI)together with the damage states,which are used to correlate the damage indices with the damage that occurred in the actual structures.Numbers of damage indices proposed and developed rely on the fact that the damage causes noticeable changes in the structural and dynamic properties of the structural components or the whole structure.Therefore,this study presents a comprehensive review of the damage assessment of Reinforced Concrete(RC)structures.It presents step by step the development of the damage indices that are most widely used to estimate the performance of structural components in the structure and subsequently assess the damage degree of such these structures either based on the structural properties or dynamic properties of the structure.Also,several damage states have been introduced to estimate the performance level of the structure.Finally,case studies,methodologies,and applications on the damage assessment of RC structures are reviewed and presented.

Damage Effects of Overload Axes on Pavement Structures

This paper established the axle load distribution model of overload axes by practical axle-meter investigations. To study the effects of overload axes on pavement distress, deflection and deflection basin tests with axle load from 60kN to 190kN were conducted on different pavement structures. The relationship between axle load and its deflection as well as its deflection basin curvature was obtained by statistical analyses. A methodology for deriving the equivalent conversion factors of overload axes to equivalent standard axle loads (ESAL) of 100kN is developed, obtaining the relationship between the equivalent conversion factors and the axle loads. Comparing the calculated defiections with the measured deflections, that elastic layered system theory is suitable for analyzing overload vehicles was verified. Consequently, the stresses and strains caused by overload axes were calculated by elastic layered system theory. The results showed that overload axes led to greater stresses and strains causing premature pavement fatigue distress. To guarantee the expected performance in overload axes pavement, the structure thickness needed increasing was obtained. The results are of referential values in the control of semi-rigid pavement overloadings.

Influence of Soil-Structure Interaction Models on the Dynamic Responses of An Offshore Wind Turbine Under Environmental Loads

Offshore wind turbines(OWTs) suffer wind, wave and earthquake loads. The investigation of OWTs' dynamic response under environmental loads is essential for structural safety assessment. The soil-structure interaction(SSI)significantly affects the responses of OWT under environmental loads. However, there is few systematic research about the difference in the dynamic response of different SSI models under environmental loads. In order to solve the problem, the OWT is modeled by shell element, and several SSI models are built. The wind, wave and earthquake loads are taken into account. Moreover, the dynamic response, fatigue and buckling analysis are performed by ANSYS. The results indicate that SSI cannot be ignored in the dynamic response of the OWT under wind and wave loads. The SSI can decrease the displacement response of the OWT by 19% under wind and wave loads and reduce the fatigue damage of the pile. Multi-layer SSI can strongly influence the OWT's dynamic response under wind and wave loads or earthquake-only load. The vertical earthquake load increases the dynamic response in three directions.Besides, in order to simulate real environment, multi-layer SSI, soil damping and vertical SSI must be considered to evaluate the displacement response of the OWT under wind, wave and earthquake loads. The earthquake and gravity loads can cause more obvious response of the OWT than that of only wind and wave loads. The top and bottom of the tower are prone to occur buckling.

Analysis of Concrete Crack Treatment Technologies in Buildings

With the continuous development of civil engineering,concrete crack treatment technology has become an important research field.This paper proposes treatment techniques for different types of cracks,including the prevention and repair of surface cracks,the reinforcement and grouting of structural cracks,and the design and construction of controlled cracks through the analysis of the causes and classification of concrete cracks.The methods and suggestions proposed in this paper are practical and can improve the quality and safety of buildings.

External blast load factors for dome structures based on reliability

Dome structures have been used extensively for industrial,residential,and military infrastructure.Therefore,it is necessary to understand the damage risk potential for such structures for blast-resistant design considerations.This paper investigates the effect of blast load variability on the design value and the structural dynamic response.Therefore,the sources of uncertainty in the external blast load on dome structures were discussed firstly.Then based on the probabilistic blast load model for the dome,the rationality of a deterministic mass-increase safety method was assessed.It was found that previous deterministic design method cannot provide a consistent and sound assurance factor or reliability index on the entire dome roof.In addition,it was also proved that the assurance-based load method fails to ensure compliance with structural safety design standards on the dome roof when compared with the reliability-based blast method.A sensitivity analysis on the probabilistic blast load was conducted,and the results indicate that stand-off distance and explosive mass both act as dominant sources to influence the mean and variability of blast load.Therefore,based on the Latin hypercube sampling method,a reliability-based external blast load factor technique was proposed.This technique was further used to estimate structural damage levels of a single-layer reticulated dome under different reliability requirements,associated with a low,medium,and high level of protection grades for a specific explosion scenario,and it indicated that this technique can be useful in the building design to achieve a higher structural anti-explosion capacity.This study herein can serve as a reference for the calculation method of designed blast load.

The Importance of Design Spectra and Site Class for the Design of High-Rise Buildings

On November 26,2019 a strong earthquake of magnitude M6.4 occurred close to the City of Durrës,Albania(15.6 km WSW(west southwest)of Mamurras and 22 km SSW(south southwest)of Durrës),causing fatalities and considerable damages in many buildings.In this article we present and analyze,by means of observational data and numerical simulation,the behavior under this earthquake of an 8-floor RC(reinforcement concrete)building,by using design spectra referring to KTP-N.2-89 and Eurocode 8.The main purpose of the authors is to better understand and evaluate the seismic performance of high-rise buildings under the design spectra with a period of soil oscillation close to the fundamental period of the structure.

EndoN treatment allows neuroblasts to leave the rostral migratory stream and migrate towards a lesion within the prefrontal cortex of rats

The binding properties of neural cell adhesion molecule are modulated by a polysialic acid moiety. This plays an important role in the migration of adult born neuroblasts from their area of origin, the subventricular zone, towards the olfactory bulb. Polysialisation increases the migration speed of the cells and helps to prevent the neuroblasts from leaving their migration route, the rostral migratory stream. Here, we evaluated the potential of intraventricular application of endoneuraminidase-N, an enzyme that specifically cleaves polysialic acid from neural cell adhesion molecule, in a rat model for structural prefrontal cortex damage. As expected, endoneuraminidase-N caused the rostral migratory stream to become wider, with a less uniform cellular orientation. Furthermore, endoneuraminidase-N treatment caused the neuroblasts to leave the rostral migratory stream and migrate towards the lesioned tissue. Despite the neuroblasts not being differentiated into neurons after a survival time of three weeks, this technique provides a solid animal model for future work on the migration and differentiation of relocated neuroblasts and might provide a basis for a future endogenous stem cell-based therapy for structural brain damage. The experiments were approved by the local animal care committee(522-27-11/02-00, 115;Senatorin für Wissenschaft, Gesundheit und Verbraucherschutz, Bremen, Germany) on February 10, 2016.

Identification of crack in a structural member using improved radial basis function(IRBF)neural networks

Purpose–The purpose of this paper is to provide an effective and simple technique to structural damage identification,particularly to identify a crack in a structure.Artificial neural networks approach is an alternative to identify the extent and location of the damage over the classical methods.Radial basis function(RBF)networks are good at function mapping and generalization ability among the various neural network approaches.RBF neural networks are chosen for the present study of crack identification.Design/methodology/approach–Analyzing the vibration response of a structure is an effective way to monitor its health and even to detect the damage.A novel two-stage improved radial basis function(IRBF)neural network methodology with conventional RBF in the first stage and a reduced search space moving technique in the second stage is proposed to identify the crack in a cantilever beam structure in the frequency domain.Latin hypercube sampling(LHS)technique is used in both stages to sample the frequency modal patterns to train the proposed network.Study is also conducted with and without addition of 5%white noise to the input patterns to simulate the experimental errors.Findings–The results show a significant improvement in identifying the location and magnitude of a crack by the proposed IRBF method,in comparison with conventional RBF method and other classical methods.In case of crack location in a beam,the average identification error over 12 test cases was 0.69 per cent by IRBF network compared to 4.88 per cent by conventional RBF.Similar improvements are reported when compared to hybrid CPN BPN networks.It also requires much less computational effort as compared to other hybrid neural network approaches and classical methods.Originality/value–The proposed novel IRBF crack identification technique is unique in originality and not reported elsewhere.It can identify the crack location and crack depth with very good accuracy,less computational effort and ease of implementation.

Machine Learning Based Quantitative Damage Monitoring of Composite Structure

Composite materials have been widely used in many industries due to their excellent mechanical properties.It is difficult to analyze the integrity and durability of composite structures because of their own characteristics and the complexity of load and environments.Structural health monitoring(SHM)based on built-in sensor net-works has been widely evaluated as a method to improve the safety and reliability of composite structures and reduce the operational cost.With the rapid development of machine learning,a large number of machine learning algorithms have been applied in many disciplines,and also are being applied in the field of SHM to avoid the limitations resulting from the need of physical models.In this paper,the damage monitoring technologies often used for composite structures are briefly outlined,and the applications of machine learning in damage monitoring of composite structures are concisely reviewed.Then,challenges and solutions for quanti-tative damage monitoring of composite structures based on machine learning are discussed,focusing on the complete acquisi-tion of monitoring data,deep analysis of the correlation between sensor signal eigenvalues and composite structure states,and quantitative intelligent identification of composite delamination damage.Finally,the development trend of machine learningbased SHM for composite structures is discussed.