Damage Size Research Articles

S100B PROTEIN CONTENT IN ISCHEMIC STROKE AND ITS PROGNOSTIC VALUE IN PATIENTS SUBJECTED TO INVASIVE INTERVENTIONS

The aim of the study is to determine the levels of the S100B protein in patients with ischemic stroke and evaluate its relationship with the size of brain tissue damage, stroke severity, and clinical outcomes. Materials and methods.The study included 113 patients with acute ischemic stroke, hospitalized within the first day after its onset and subjected to invasive treatment. 101 men and 12 women were examined, including 32 at the age of 50-60, 64 at the age of 61-70, 17 at the age of 71-80. parameters and degree of functional deviations according to the NIHSS scale. Results. The content of S100B during the initial determination on average for the group significantly exceeded the level determined in the control by 3.22 times (p=0.025). There was a direct dependence of the content of S100B on the size of the stroke. A moderate increase in the indicator on the 3rd day relative to the determined one on the 1st day and a decrease on the 7th day after the development of the stroke were revealed. There was no significant dependence of S100B content on the presence of comorbidities. The influence of the studied parameter on the degree of neurological deficit was determined only in patients with a large stroke size.

Fuzzy-Genetic based Approach in Decision Making for Repair of Turbochargers using Additive Manufacturing

dditive manufacturing (AM) is an effective technology for repairing and restoring automotive components. However, the effectiveness of additive manufacturing technology in repair and restoration is highly influenced by several factors related to components and process. The objective of this paper is to improve the decision-making in repair and restoration of a turbocharger with AM. In this article, a Fuzzy-Genetic approach was presented as a decision-making tool for repairing a remanufacturable component. Fuzzy logic (FL) is deployed as the method to model the design parameters of a turbocharger, such as design complexity, failure mode, damage size, disassembleability, preprocessing, temperature, durability, pressure ratio and mass flow rate to model the relationship between the inputs and outputs using Mamdani model with their membership functions. Genetic algorithm optimization method was used to optimize the cost of the repairing process once the decision on whether the turbocharger was repairable was determined by the Fuzzy system. The FL approach applied rules affecting the process, the robustness and accuracy of the model increases with a higher number of rules. The work focuses on the dataset related to design information, which represents as a knowledge base for decision parameters on design optimization to automate repair process during remanufacturing. The results showed the effects of the design parameters on repairing and replacement decisions, and how the fuzzy model related the inputs to the outputs based on the generated rules. In conclusion, FGA method can be used to improve the repair and restoration process of a turbocharger through AM technology.

Damage Metrics for Void Detection in Adhesive Single-Lap Joints

Structural Health Monitoring (SHM) techniques, such as Electromechanical Impedance Spectroscopy (EMIS), aim to continuously monitor structures for defects, thus avoiding the need for regular maintenance. While attention has been given to the application of EMIS in the automatic detection of damage in metallic and composite components, integrity monitoring of structural adhesive joints has been comparatively neglected. This paper investigated the use of damage metrics with electrical impedance measurements to detect defects in Single-Lap Joints (SLJs) bonded with a modified epoxy adhesive. Traditional metrics using statistical and distance-based concepts, such as the Root-Mean-Squared Deviation, RMSD, or the Correlation Coefficient, CC, are addressed at detecting voids in the adhesive layer and are applied to five different spectral frequency ranges. Furthermore, new damage metrics have been developed, such as the Average Canberra Distance, ACD, which enables a reduction of possible outliers in damage detection, or the complex Root-Mean-Squared Deviation, cRMSD, which allows for the use of both the real and imaginary components of the impedance, enabling better damage detection in structural adhesive joints. Overall, damage detection is achieved, and for certain spectral conditions, differentiation between certain damage sizes, using specific metrics, such as the RMSD or cRMSD, may be possible. Overall, the RMSD or cRMSD values from damaged SLJs tend to be double the metric values from undamaged joints.

СОВРЕМЕННЫЕ СРЕДСТВА ОБЕСПЕЧЕНИЯ ОГНЕЗАЩИТЫ СТАЛЬНЫХ КОНСТРУКЦИЙ ОБЪЕКТОВ НЕФТЕГАЗОВОГО КОМПЛЕКСА

Modern polymer materials used in construction, transport and household products characterized, as a rule, by high flammability and combustibility, which has a significant impact on ensuring the required level of fire protection. A variant of the formalization of the choice of a flame retardant coating is proposed and an algorithm for its justification is proposed. The proposed algorithm for choosing a fire-retardant coating assumes a preliminary quantitative assessment of fire danger; analysis of possible scenarios of fire development; the choice of the type of fire protection system taking into account the operating environment, the size of potential damage from fire, technical, economic and human factors, and the rationale for choosing a fire-resistant coating that provides the specified (required) level of fire protection.

Longitudinal wave scattering in thin plates with symmetric damage considering oblique incidence

The interactions between ultrasonic waves and damage provide crucial information for developing effective damage detection techniques in various fields, including non-destructive testing and structural health monitoring, and they can be investigated through wave propagation. Then, this article presents an approach to investigate the longitudinal wave in thin plates with symmetric damage considering oblique incidence. The approach includes a strategy to determine the plate thickness reduction due to the damage presence by considering an oblique incidence of waves generated by the actuator. Modeling is developed to compute each wave packet of reflected and transmitted waves separately, which allows one to describe the wave scattering. Numerical simulations are carried out and the results show that the sensor size can be adjusted to improve the existence of damage detection and the determination of the damage depth. Results from experimental tests are presented to demonstrate the approach considering a circular actuator. The findings contribute to improving the comprehension of the interaction between guided waves and discontinuities, potentially encouraging the use of waves for damage sizing purposes rather than only for detection and improve the current state of the art in wave propagation by investigating the effects of different excitation frequencies and the influence of the damage parameters and sensor sizing on the resulting waves.

A Neural Network Approach to Estimate the Frequency of a Cantilever Beam with Random Multiple Damages.

Natural frequency is an important parameter in the structural health monitoring (SHM) system. Any changes in this parameter indicate structural alteration due to damage. This study provides a neural network (NN) solution as an alternative to the finite element (FE) method to measure the natural frequencies of a cantilever beam with random multiple damage. It is based on a statistical dataset of a free vibration test obtained from the APDL (Ansys parametric design language) simulation using a MATLAB (matrix laboratory) script. The script can generate an unlimited number of possible damage combinations for any given parameters with the help of the Monte Carlo (MC) technique. MC helps to generate a random number of damages in random locations at each simulation. Damage conditions are controlled by three parameters including damage severity and damage size (in terms of the mean and standard deviation of damage). Moreover, the method proposes a curve-fitting equation to validate the predicted natural frequency for the first three modes obtained from the neural network model. Both methods are in good agreement with each other, having minimal errors in the range of 0.2-3% for each mode. The frequency result shows that the beam frequency is 8.6486 Hz if the area reduction is 10%, whereas it comes down to 7.2338 Hz if there is a 30% area reduction. A two-level factorial test shows that damage severity is the most impactful factor compared to the damage sizes on the frequency shift event. This indicates that damage alters the composition of the beam and has an impact on its frequency change with the assumed damage parameters. Therefore, the proposed NN model can estimate the frequency shift for various damage scenarios. It can be utilized in the vibration-based damage identification process to predict the frequency changes of the damaged beam without any computational burden.

Damage Propagation and Residual Strength of Simple Block-Loaded CFRP Plates with Circular Holes under Tension–Tension Fatigue Conditions

Holes and their effects on the fatigue behavior and damage propagation of thin-walled structural components remain objects of research. In this paper, the previously untreated effect of round holes in thin plain-woven carbon fiber-reinforced plastic plates subjected to simple block loading is examined, and the implication on both damage propagation and residual tensile strength is investigated. Using three-dimensional digital image correlation, the damage propagation in the performed experimental tests is acquired, and the damage size is quantified. The evaluations reveal a relationship between the damage propagation and applied load level, for which an empirical model has been previously established by the authors. As the number of cycles increases, a saturation behavior is found. Once the increased load is imposed on the plate, damage propagation resumes, leading to further damage propagation that can be described with the same empirical model as the initial damage propagation, including renewed saturation behavior. The subsequent experimental tests to determine the residual tensile strength reveal a positive effect of the existing damage size, as the ultimate load significantly exceeds the ultimate load of the non-damaged plate.

Can BPA Analogs Affect Cellular and Biochemical Responses in the Microalga Phaeodactylum tricornutum Bohlin?

Bisphenol A analogs (BPA analogs) are emerging contaminants with a rising production caused by the replacement of BPA with these compounds. The increased production of BPA analogs is leading to their increased release into various ecosystems, including marine ones. The aim of this study was to evaluate the biological effects of BPA analogs on a primary producer, the diatom Phaeodactylum tricornutum Bohlin. Three different BPA analogs (BPAF, BPF, and BPS) and their mixture were tested at the environmental relevant concentration of 300 ng/L. Growth, cell size and several biomarkers of oxidative stress and oxidative damage were measured. Our results indicated that the tested compounds caused a reduced growth rate and induced oxidative stress, altering many antioxidant enzymes in P. tricornutum. However, no oxidative damages were observed.

Composite Panel Damage Classification Based on Guided Waves and Machine Learning: An Experimental Approach

Ultrasonic guided waves (UGW) are widely used in structural health monitoring (SHM) systems due to the sensitivity of their propagation mechanisms to local material changes, i.e., those induced by damage. Post-processing of the signals gathered by piezoelectric sensors, typically used for both the excitation and the sensing of UGW, is a fundamental step to extract all the peculiar features that can be related to both damage location and severity. This research probes the efficacy of machine learning (ML) models in discerning damage location (R-Classification) and size (S-Classification). Seven supervised ML classifiers were examined: Ensemble-Subspace K-Nearest Neighbors (KNN), Ensemble-Bagged Trees, KNN-Fine, Ensemble-Boosted Trees, Support Vector Machine (SVM), Linear Discriminant, and SVM-Quadratic. The experimental dataset comprised measurements from varied reversible damage configurations on a composite panel, represented by wooden cuboids of single and three different sizes. Signal noise was minimized by performing a low-pass filter, and sequence forward selection-aided feature selection. The optimized ensemble classifier proved to be the most precise for R-Classification (95.83% accuracy), while Ensemble-Subspace KNN excelled in S-Classification (98.1% accuracy). This method offers accurate, efficient damage diagnosis and classification in composite structures, promising potential applications in aerospace, automotive, and civil engineering sectors.

Comparison of Dorsoulnar Artery Perforator, Superficial Palmar Branch of the Radial Artery, and Superficial Circumflex Iliac Artery Perforator Flaps in Volar Finger Defects.

Volar finger defects can sometimes be challenging for plastic surgeons. There are many reconstruction options from local flaps to free flaps. Therefore, the aim of this study was to present a functional and cosmetic comparison of the results of using dorsoulnar artery perforator (DUAP), superficial palmar branch of the radial artery (SPBRA), and superficial circumflex iliac artery perforator (SCIP) flaps for repairing volar finger defects. Thirty-two patients were included in the study. The age and sex of the patients, cause of injury, defect location, defect size, and presence or absence of nerve damage were noted. The patients' defects were reconstructed with DUAP, SCIP, or SPBRA flaps. The flap size, flap elevation time, vessels, and early postoperative complications were noted. During the postoperative follow-up period, the Michigan Hand Outcomes Questionnaire scale was used to evaluate hand function, and the modified Vancouver Scar Scale was used to assess aesthetic appearance. Two-point discrimination tests and cold intolerance tests were performed to measure sensory outcomes. Donor site improvements were also noted. There was no significant difference between the groups in terms of age, defect size, flap size, follow-up time, and 2-point discrimination. It was observed that the elevation time was significantly shorter in the SCIP flap group, and the Michigan Hand Outcomes Questionnaire score was significantly lower in the DUAP flap group compared with the other 2 groups (P < 0.01). In addition, the modified Vancouver Scar Scale score was significantly higher in the DUAP flap group (P < 0.01). The findings of this study show that the use of SPBRA flaps has more advantages than the use of DUAP and SCIP flaps in many respects.

Probabilistic residual strength assessment of smart composite aircraft panels using guided waves

Typical prognosis methods in Structural Health Monitoring (SHM) link the degradation mechanisms to the extracted health indicators. Such approaches however do not provide estimations on the current strength loss when a new damage is detected. This study presents a framework that enables the SHM-informed asset integrity management of composites. First, the integrated guided wave based SHM system is used to inform on the existence, location and size of the damage and subsequently, the residual strength of the structure is predicted. The framework combines physics-based and data-driven models to mitigate the incompleteness of the former and address issues relating to the representativeness in the training datasets of the later. Detailed finite element models are used to create a digital representation of the structure while a building-block program is initiated to quantify and propagate the uncertainties observed experimentally. A 1.6 m composite panel with a skin-stringer delamination, equipped with a network of 24 piezoelectric transducers, is used as a case study to demonstrate the prognostic capabilities of the framework. The framework estimated the damage size with a mean absolute percent error (MAPE) of approximately 10% while the residual strength of a destructively tested damaged panel was predicted with a MAPE of 5%.

Experiment and Numerical Simulation on Damage Behavior of Honeycomb Sandwich Composites under Low-Energy Impact

It is well-established that the honeycomb sandwich composite structures are easily prone to damage under low-energy impact. Consequently, it would lead to a dramatic decrease in structural load-bearing capacity and a threat to overall safety. Both experimental and numerical simulations are carried out to investigate the impact damage behavior of honeycomb sandwich composite specimens. The damage mode, damage parameters, and contact force-time curves of three types of panel materials with T300, T700, and T800 are obtained under different impact energies of 10 J, 20 J, and 40 J by the drop-weight impact experiment. Moreover, digital image correlation (DIC) tests are used to measure the deformation and strain of the lower panel. The experimental results reveal that the degree of damage increases with increasing impact energy. Particularly, the T300 panel specimen exhibits visible fiber fracture when subjected to an impact energy of 40 J. The impact process involves matrix cracking, fiber fracture, and delamination of the upper panel occurring first, followed by immediate crush damage to the honeycomb core and, finally, slight fiber damage to the lower panel. Due to its higher strength, the T800 panel specimen exhibits the highest damage resistance compared to the T700 and T300 panel specimens. To consider the microscopic failure criteria and various types of contact during the impact process, a finite element model of honeycomb sandwich composites is established, and numerical simulation analysis of low-energy impact is performed to determine the damage mode, damage size, and contact-force curves. Comparative analysis demonstrates good agreement between the simulation and experimental results. The findings of this study provide valuable technical support for the widespread application of honeycomb sandwich composites in the aviation field.

Equivalent initial damage sizes (EIDS) for type 1C anodised aluminium alloy 7085-T7452 under variable amplitude loading

Aluminium alloy (AA) 7085-T7452 is a high strength, low weight alloy used in large forgings in primary airframe applications. For corrosion protection, airframe components are anodised and this study looks at the influence of type 1C anodising on the fatigue crack growth behaviour in AA 7085-T7452 specimens that were fatigue tested with fighter wing spectrum loading. Surface etch pits formed during essential anodising preparatory processes were the primary cause of fatigue crack nucleation. The severity of the early crack growth caused by those etch pits that nucleated the worst crack in each specimen was evaluated using quantitative fractography-based fatigue crack growth measurements and the equivalent initial damage size (EIDS) of those etch pits was determined. Characterising the EIDS distribution of aircraft alloys with representative surface treatments is a fundamental input into Linear Elastic Fracture Mechanics (LEFM) based fleet failure analysis of aircraft structures. In this study, a notable increase in the average EIDS value for the worst crack present was observed whenever material or loading condition changes led to a significantly increased number of crack nucleation sites. In particular, an increase in the cyclic stress spectrum’s severity increased the number of nucleation sites in a specimen and significantly increased the average EIDS. It appears that due to variable nucleating discontinuity properties (e.g. size, shape and orientation) and material property variations, an increased number of nucleation sites increases the probability that the worst crack present will nucleate at a location that is conducive for relatively fast early crack growth. This will potentially cause shorter overall airframe component fatigue lives and so both the mechanisms and conditions of etch pit fatigue nucleation in this material and material finish shall be discussed for the benefit of failure analysts evaluating unexpected fleet fatigue cracking.

Acoustic emission characteristics of rock salt under multi-stage cyclic loading

Rock salt is a common sedimentary rock found in the crust and is widely used for underground energy storage and waste disposal. This research conducted multi-stage cyclic loading on impure Huai'an rock salt while monitoring acoustic emissions (AE) in real-time. The results show that as the loading stress increases, the cumulative AE energy increases in a stepwise manner. The frequency distribution displays a band pattern of low, medium, and high frequencies. As the loading stress increases, the proportion of high-frequency signals decreases while that of low-frequency signals increases. The proportion of medium-frequency signals remains consistently the highest. The b-value initially decreases and then increases, with a sharp drop near the failure. Compared to the increased lower limit stress (ILLS), the constant lower limit stress (CLLS) causes higher cumulative AE energy and amplitude, and smaller b-values for the salt samples. These results indicate that the CLLS increases the size of cracks and damage in the salt samples. Based on the RA (risetime/ amplitude)-AF (average frequency) distribution, it is found that the primary mode of failure for rock salt under multi-stage cyclic loading is tensile cracking. Compared to the ILLS, the CLLS promotes the development of shear cracks. The AE test results of the rock salt provide an important basis for explaining the in-situ microseismic signals generated during the operation of salt cavern energy storage.

Multistage damage detection method based on the defect mode for periodically stiffened panels

Periodically stiffened panels are extensively used in engineering structures, but methods for their nondestructive testing and evaluation remain limited. The limitations arise due to the intricate vibration modes and Lamb wave signals reflected by the stiffeners and damage. To handle these complexities, the multistage method proposed in this research has solved an inverse problem utilizing the defect modes to detect damage in periodically stiffened panels. The proper selection of vibration frequency related to the local damage mode is important as local vibrations at damage induce additional bandgaps in stiffened panels. Therefore, a damage-related frequency range was selected by comparing the transmission coefficients of healthy and damaged periodically stiffened panels, constituting the first stage. Subsequently, a vibration-based approach was implemented in the second stage to obtain the defect modes using forced vibrations around the selected frequency range and then the damage location was estimated using a baseline-free damage index. To address the difficulty associated with damage size estimation in vibration-based methods, a Lamb-wave-based technique was employed in the third stage to accurately quantify the size of damage in the localized regions. To depress the dispersive characteristics of the Lamb waves, the complicated signals reflected by stiffeners and damage were processed using continuous wavelet transform based on Gabor wavelet in time domain. The method was tested on an aluminum periodically stiffened panel with debonding and stiffness reduction damage. The results obtained using numerical simulations and experiments validate the accuracy of the proposed method which exhibits a significant potential for various industrial applications.

In utero ethanol exposure induces mitochondrial DNA damage and inhibits mtDNA repair in developing brain.

Mitochondrial dysfunction is postulated to be a central event in fetal alcohol spectrum disorders (FASD). People with the most severe form of FASD, fetal alcohol syndrome (FAS) are estimated to live only 34 years (95% confidence interval, 31 to 37 years), and adults who were born with any form of FASD often develop early aging. Mitochondrial dysfunction and mitochondrial DNA (mtDNA) damage, hallmarks of aging, are postulated central events in FASD. Ethanol (EtOH) can cause mtDNA damage, consequent increased oxidative stress, and changes in the mtDNA repair protein 8-oxoguanine DNA glycosylase-1 (OGG1). Studies of molecular mechanisms are limited by the absence of suitable human models and non-invasive tools. We compared human and rat EtOH-exposed fetal brain tissues and neuronal cultures, and fetal brain-derived exosomes (FB-Es) from maternal blood. Rat FASD was induced by administering a 6.7% alcohol liquid diet to pregnant dams. Human fetal (11-21 weeks) brain tissue was collected and characterized by maternal self-reported EtOH use. mtDNA was amplified by qPCR. OGG1 and Insulin-like growth factor 1 (IGF-1) mRNAs were assayed by qRT-PCR. Exosomal OGG1 was measured by ddPCR. Maternal EtOH exposure increased mtDNA damage in fetal brain tissue and FB-Es. The damaged mtDNA in FB-Es correlated highly with small eye diameter, an anatomical hallmark of FASD. OGG1-mediated mtDNA repair was inhibited in EtOH-exposed fetal brain tissues. IGF-1 rescued neurons from EtOH-mediated mtDNA damage and OGG1 inhibition. The correlation between mtDNA damage and small eye size suggests that the amount of damaged mtDNA in FB-E may serve as a marker to predict which at risk fetuses will be born with FASD. Moreover, IGF-1 might reduce EtOH-caused mtDNA damage and neuronal apoptosis.

Artificial neural network-based damage detection of composite material using laser ultrasonic technology

Composite materials are widely used in various fields and non-destructive testing (NDT) is an important issue for these. Laser ultrasonic technology (LUT) can achieve the non-contact online NDT with high precision for composite materials. And the damage location depends on the differentiation of signals on defect and normal paths. However, manual interpretation of test images can easily lead to miss cause by limited accuracy. Based on the above background, a damage detection method based on artificial neural network (ANN) is proposed. First, the signals collected by LUT system are converted into energy ratios of components at different frequency by wavelet packet decomposition (WPD). Then, the preprocessed energy ratios are fed into a three-layer ANN to distinguish the damage. This algorithm has the characteristics of faster scanning, less calculation and greater accuracy. Results show that the detection accuracy of ANN is 99.6% for crack. Furthermore, the damage location and size can be accurately measured.

A Machine Learning Based-Guided Wave Approach for Damage Detection and Assessment in Composite Overwrapped Pressure Vessels

The applications of composite overwrapped pressure vessels (COPVs) in extreme conditions, such as storing hydrogen gases at very high pressure, impose new requirements related to the system's integrity and safety. The development of a structural health monitoring (SHM) system that allows for continuous monitoring of the COPVs provides rich information about the structural integrity of the component. Furthermore, the collected data can be used for different purposes such as increasing the periodic inspection intervals, providing a remaining lifetime prognosis, and also ensuring optimal operating conditions. Ultimately this information can be complementary to the development of the envisioned digital twin of the monitored COPVs. Guided waves (GWs) are preferred to be used in continuous SHM given their ability to travel in complex structures for long distances. However, obtained GW signals are complex and require advanced processing techniques. Machine learning (ML) is increasingly utilized as the main part of the processing pipeline to automatically detect anomalies in the system's integrity. Hence, in this study, we are scrutinizing the potential of using ML to provide continuous monitoring of COPVs based on ultrasonic GW data. Data is collected from a network of sensors consisting of fifteen Piezoelectric (PZT) wafers that were surface mounted on the COPV. Two ML algorithms are used in the automated evaluation procedure (i) a long short-term memory (LSTM) autoencoder for anomaly detection (defects/impact), and (ii) a convolutional neural network (CNN) model for feature extraction and classification of the artificial damage sizes and locations. Additional data augmentation steps are introduced such as modification and addition of random noise to original signals to enhance the model's robustness to uncertainties. Overall, it was shown that the ML algorithms used were able to detect and classify the simulated damage with high accuracy.

Experimental investigation of LPG-releasing processes with varied damage sizes on a pressurized vessel

Experimental investigation of LPG-releasing processes with varied damage sizes on a pressurized vessel

Reconstruction of radial pseudo-forces on cylinders via mono-laser scanning: Concept and application to characterization of damage

Damage in a plate can cause perturbation to the equation of out-of-plane motion, which can be equivalently regarded as the transverse pseudo-force applied on the damage region. Thereby, the characterization of damage in the plate can be achieved by identifying the pseudo-force. Nevertheless, it is difficult to formulate the pseudo-force for the characterization of damage in a cylinder because its longitudinal, circumferential, and radial motions are coupled. Moreover, a conventional scanning laser vibrometer equipped with a single scanning head can measure the out-of-plane motions of a plate but is insufficient for measuring the 3D motions of a cylinder. The aforementioned limitation leads to a noticeable barrier to extending the pseudo-force approach from a plate to a cylinder. To overcome this barrier, a new concept of radial pseudo-force (RPF) is formulated to represent the damage-induced perturbation to the radial equilibrium of a cylinder, whose motions are dominated by its radial components. The RPF is applied on the lateral surface of the cylinder and is an ideal damage indicator because it appears in the damage region only and almost vanishes at intact locations. Furthermore, a novel method for reconstructing RPFs via mono-laser scanning is proposed. A correction matrix is formed to correct an undeflected-laser-axis mode shape component (MSC) that can be directly measured via mono-laser scanning, whereby the radial MSC of the cylinder can be obtained for the reconstruction of the RPF on the cylinder. The concept of the RPF is numerically proved using the finite element method. In addition, the applicability of the RPF approach is experimentally validated on a cylinder with internal damage. The numerical and experimental results reveal that the RPF approach can graphically characterize the occurrence, location, and approximate plane size of damage in cylinders.