Damage Assessment Research Articles

Stress Wave Characteristics and Damage Analysis of the Helicopter Tail Drive Shaft with the High-Speed Projectile Impact

The tail drive shaft is easily hit by the ground antiaircraft machine gun in low-altitude helicopter combat, resulting in projectile penetration damage and directly jeopardizing the helicopter’s safety during flight. The projectile penetration damage will cause elastic–plastic deformation and failure damage to the tail drive shaft, and excite high-frequency stress waves suitable for projectile impact monitoring. To examine the effects of different impact parameters on the stress wave characteristics and damage from the tail drive shaft during projectile impact, the LS-DYNA is used to establish a dynamic model of projectile impact on the tail drive shaft. The passive monitoring experiment of projectile impact on the tail drive shaft is carried out based on the PZT piezoelectric sensor. It is concluded that both methods can obtain reliable stress wave signals and damage conditions through the comparison of experimental and simulation results. Then, the wavelet transform and short-time Fourier transform three-dimensional time–frequency are used to select the best frequency band for extracting the stress wave eigenvalues, along with the statistics and analysis of the stress wave characteristic values and damage degree under different impact parameters. The results show that the impact stress waveforms and high-frequency components are significantly different with the change in projectile impact parameters. There are significant differences among different impact parameters on the first trough amplitude, the two peak time intervals, the kurtosis factor, and the bullet hole damage generation laws. The functional relationship between the projectile impact parameters and the characteristic parameters of the stress wave, as well as the damage degree, are established. The research conclusion can provide theoretical reference and data support for projectile impact monitoring and damage assessment of the helicopter tail drive shaft.

Mitochondria-targeted oligomeric α-synuclein induces TOM40 degradation and mitochondrial dysfunction in Parkinson’s disease and parkinsonism-dementia of Guam

Mitochondrial dysfunction is a central aspect of Parkinson’s disease (PD) pathology, yet the underlying mechanisms are not fully understood. This study investigates the link between α-Synuclein (α-Syn) pathology and the loss of translocase of the outer mitochondrial membrane 40 (TOM40), unraveling its implications for mitochondrial dysfunctions in neurons. We discovered that TOM40 protein depletion occurs in the brains of patients with Guam Parkinsonism-Dementia (Guam PD) and cultured neurons expressing α-Syn proteinopathy, notably, without corresponding changes in TOM40 mRNA levels. Cultured neurons expressing α-Syn mutants, with or without a mitochondria-targeting signal (MTS) underscores the role of α-Syn’s mitochondrial localization in inducing TOM40 degradation. PDe-related etiological factors, such as 6-hydroxydopamine or ROS/metal ions stress, which promotes α-Syn oligomerization, exacerbate TOM40 depletion in PD patient-derived cells with SNCA gene triplication. Although α-Syn interacts with both TOM40 and TOM20 in the outer mitochondrial membrane, degradation is selective for TOM40, which occurs via the ubiquitin-proteasome system (UPS) pathway. Our comprehensive analyses using Seahorse technology, mitochondrial DNA sequencing, and damage assessments, demonstrate that mutant α-Syn-induced TOM40 loss results in mitochondrial dysfunction, characterized by reduced membrane potential, accumulation of mtDNA damage, deletion/insertion mutations, and altered oxygen consumption rates. Notably, ectopic supplementation of TOM40 or reducing pathological forms of α-Syn using ADP-ribosylation inhibitors ameliorate these mitochondrial defects, suggesting potential therapeutic avenues. In conclusion, our findings provide crucial mechanistic insights into how α-Syn accumulation leads to TOM40 degradation and mitochondrial dysfunction, offering insights for targeted interventions to alleviate mitochondrial defects in PD.

Bayesian-based multi-class damage identification on prestressed reinforced concrete bridges

Model-driven structural system identification techniques are effective for comprehensive damage assessment but relying on a single model for structural identification (St-Id) can lead to unreliable results due to the ill-posed nature of the inverse calibration problem. To address this issue, this work explores the potential of multi-class digital twins, derived from sets of competing model classes, each one characterised by a distinct failure mechanism. To accelerate the calibration process, Kriging metamodels are employed, facilitating the fusion of diverse data types, such as modal displacements, frequencies, and static rotations. The Bayesian Information Criterion (BIC) is utilised to identify the most suitable model class. This research specifically targets the damage assessment of prestressed reinforced concrete girder bridges, which represent a significant portion of the global bridge stock. To this end, a numerical model of a representative span is developed, followed by a parametric analysis to evaluate the proposed approach’s effectiveness. Various damage scenarios including stiffness reduction and tendon prestress losses are considered, demonstrating the method’s capability for quasi-real-time multi-class damage identification. The study emphasizes the combined use of modal features and static rotations within a Bayesian framework, underscoring the importance of data fusion in achieving effective damage identification for these structures.

Research on Damage Assessment Method for Reinforced Concrete T-Girder Bridges Under Munitions Strikes

As a transportation hub, the girder bridge is often an important target for both sides in combat, and it is very important for wartime decision-making and subsequent fire planning to carry out assessment on the effect of the damage to the bridge after the fight. The use of images for target damage assessment is more secure and efficient compared with the traditional method of manual inspection. However, the current image-based damage assessment of girder bridges has the problems of neglecting the damage to bridge piers and the poor stability of the damage features. To address these issues, this paper proposes a functional damage assessment method for beam bridges after being hit by ammunition based on visible light images. This method decomposes the girder bridge into two main sub-targets (the abutment and deck), analyzes its functional characteristics, designs the residual bearing capacity of the bridge and the number of passable lanes in the damage features, proposes an algorithm for conversion from image features to damage features and a fusion algorithm for the damage features, and establishes the rules for the evaluation of the damage level accordingly. The MATLAB experiment verifies that the method is able to extract the residual bearing capacity and the number of passable lanes from the image of the post-fighting girder bridge and to obtain the damage assessment results, which provides a new method for the assessment of the effect of damage to girder bridges.

Distinguishing short-term and long-term climate impacts in damage functions

Abstract Climate change presents substantial challenges to global economic stability, with damage functions widely applied to evaluate its potential impacts. However, differing assumptions within damage functions lead to significant variability in estimated climate damages, with limited quantitative investigation into the reasons for these discrepancies. This study addresses this gap by distinguishing between short-term and long-term climate impacts in damage assessments, using the Dynamic Integrated Climate-Economy (DICE) model as a case study, alongside other assessments. Short-term impacts capture immediate economic disruptions, whereas long-term impacts encompass enduring effects on economic growth, such as reductions in capital stock. The results demonstrate that accounting for long-term impacts increases estimated damages by approximately 30% in the DICE model. Comparative analysis across multiple models reveals that different treatments of short-term and long-term climate impacts contribute significantly to variations in projected economic damages. This study emphasizes the urgent need for future damage functions to clarify both long-term and short-term climate damages to inform robust climate policies.

Harnessing UAS and high-resolution satellite imagery to better characterize wind damage and understand tornado behavior

Abstract The severe storms research community continues to strive toward measuring and quantifying wind speeds near the ground in tornadoes as a component of meeting society’s needs of protecting life and property. Current methods to measure and characterize tornadoes and their wind speeds all have limitations, particularly when only vegetation and rural areas are impacted. To address these problems, the National Severe Storms Laboratory (NSSL) and the Cooperative Institute for Severe and High-Impact Weather for Research and Operations (CIWRO) employed Uncrewed Aerial Systems (UAS) technologies and specialized satellites to assess storm damage through finescale mapping of wind damage immediately following storm events. From 2021-2023, NSSL-CIWRO led 48 UAS-based damage assessments spanning the northern High Plains to the Gulf Coast areas. The goals of this work were to improve our knowledge of tornado dynamics as a function of land cover and topography and to better understand mechanisms of wind damage production in convective storms. In parallel, NSSL-CIWRO developed workflows to facilitate rapid sharing of UAS and satellite imagery to National Weather Service Weather Forecast Offices (NWS WFOs) and the Federal Emergency Management Agency (FEMA). Operationally, NSSL UAS imagery and derived products have aided traditional ground- and satellite-based surveys and have provided important clues on tornado intensity by detailing damage to vegetation in inaccessible areas. UAS and high-resolution satellite imagery may become a routine and key component to understand tornado and severe weather behavior through analysis of specialized field observations.

Toxic effects of exposure to Polymethyl methacrylate and polyvinyl chloride microplastics in Pacific oysters (Crassostrea gigas)

Increasing attention has been directed toward the toxic effects of microplastics (MP) on marine mollusks in recent years. To evaluate these effects, Pacific oysters (Crassostrea gigas) were acclimated and cultured in a 140-liter container, where two types of MP, polymethyl methacrylate (PMMA) and polyvinyl chloride (PVC), were introduced into their feed. MP concentrations in the water were maintained at 300 μg/L, 600 μg/L, and 900 μg/L to assess oxidative stress, DNA damage, and metabolic disorders in these organisms. Significant alterations in antioxidant enzyme activities were detected in C. gigas exposed to these pollutants. After 30 days of exposure to high concentrations of PMMA, superoxide dismutase (SOD) activity in the adductor muscle was reduced by 59% compared to the control group, while catalase (CAT) activity increased by 67%. DNA damage assessments revealed that NF-κB expression levels reached a maximum value of 2.46 in the high-concentration PMMA group after 30 days, the highest among all experimental groups. Additionally, metabolic pathway alterations in the hepatopancreas of C. gigas were observed, including reduced expression levels of uridine and methylmalonic acid (MMA), alongside significantly elevated expression levels of glutamic acid and asparagine. This study offers essential toxicological data for understanding and quantifying the impacts of PMMA and PVC MP on marine mollusks.

Fragility Modeling of Power Grid Infrastructure for Addressing Climate Change Risks and Adaptation

ABSTRACTThe resilience of electric power grids is threatened by natural hazards. Climate‐related hazards are becoming more frequent and intense due to climate change. Statistical analyses clearly demonstrate a rise in the number of incidents (power failures) and their consequences in recent years. Therefore, it is of utmost importance to understand and quantify the resilience of the infrastructure to external stressors, which is essential for developing efficient climate change adaptation strategies. To accomplish this, robust fragility and other vulnerability models are necessary. These models are employed to assess the level of asset damage and to quantify losses for given hazard intensity measures. In this context, a comprehensive literature review is carried out to shed light on existing fragility models specific to the transmission network, distribution network, and substations. The review is organized into three main sections: damage assessment, fragility curves, and recommendations for climate change adaptation. The first section provides a comprehensive review of past incidents, their causes, and failure modes. The second section reviews analytical and empirical fragility models, emphasizing the need for further research on compound and non‐compound hazards, especially windstorms, floods, lightning, and wildfires. Finally, the third section examines risk mitigation and adaptation strategies in the context of climate change. This review aims to improve the understanding of approaches to enhance the resilience of power grid assets in the face of climate change. These insights are valuable to various stakeholders, including risk analysts and policymakers, who are involved in risk modeling and developing adaptation strategies.

A novel framework for fatigue hotspot localization and damage assessment of multi-defect marine structures under random vibration

In ocean engineering, assessing vibration-induced fatigue under random loads is critical, particularly for ocean structures with multiple defects, as fatigue is highly sensitive to local structural flaws. Recently, incorporating structural dynamic characteristics into vibration fatigue assessments has emerged as a significant trend and formidable challenge, involving three key obstacles: precise localization of fatigue hotspots, extraction of key modes, and determination and integration of modal damage contributions. This paper introduces a novel framework for fatigue hotspot localization and damage assessment in the context of random vibration fatigue in ocean structures. By initially identifying fatigue hotspots and hot regions using stress mode shapes, refining the finite element mesh in hot regions, and conducting reanalysis, the exact locations of fatigue hotspots can be determined, enabling structural dimensionality reduction. The introduction of a modal damage contribution factor allows for the evaluation of each mode's damage contribution and the identification of key modes, further facilitating modal reduction. For the precisely localized fatigue hotspots, the proposed method combines the damage contributions of key modes, enabling rapid and accurate assessment of fatigue damage under random vibration loads. Finite element case studies and vibration fatigue test results demonstrate that the proposed framework effectively addresses the challenges of random vibration fatigue evaluation, achieving structural and modal reduction while significantly enhancing the efficiency of damage assessment without compromising accuracy.

Damage assessments of designed sandwich cores against an idealized impact loading of ship accidents: experiment-based validated nonlinear finite element approach

PurposeThe high rate of ship casualties demands serious attention. Earlier studies have yet to integrate several scenarios in an experiment. This research aims to determine damage to a ship's hull that is assumed to have been stranded due to being hit by a rock under the sea.Design/methodology/approachThis scenario was analyzed by conducting a penetration test simulation using the ANSYS LS DYNA. Modeling was carried out with core, speed, indenter shape and indenter angle variations. The test is carried out by moving the indenter against the plate until the plate is damaged.FindingsThe results of this research show that the round indenter gave the best results in testing, while changes in speed below 1.5 m/s did not significantly affect the results. The contact angle of the indenter to the panel is directly proportional to the damage to the panel, and core X has the best results in the test.Originality/valueThis work presented several proposed eccentric sandwich panels to calculate their performance against impact loading. The detailed geometry for future reference is presented, while the indenter-panel interactions are validated based on experimental work.

Burned Olive Trees Identification with a Deep Learning Approach in Unmanned Aerial Vehicle Images

Olive tree orchards are suffering from wildfires in many Mediterranean countries. Following a wildfire event, identifying damaged olive trees is crucial for developing effective management and restoration strategies, while rapid damage assessment can support potential compensation for producers. Moreover, the implementation of real-time health monitoring in olive groves allows producers to carry out targeted interventions, reducing production losses and preserving crop health. This research examines the use of deep learning methodologies in true-color images from Unmanned Aerial Vehicles (UAV) to detect damaged trees, including withering and desiccation of branches and leaf scorching. More specifically, the object detection and image classification computer vision techniques area applied and compared. In the object detection approach, the algorithm aims to localize and identify burned/dry and unburned/healthy olive trees, while in the image classification approach, the classifier categorizes an image showing a tree as burned/dry or unburned/healthy. Training data included true color UAV images of olive trees damaged by fire obtained by multiple cameras and multiple flight heights, resulting in various resolutions. For object detection, the Residual Neural Network was used as a backbone in an object detection approach with a Single-Shot Detector. In the image classification application, two approaches were evaluated. In the first approach, a new shallow network was developed, while in the second approach, transfer learning from pre-trained networks was applied. According to the results, the object detection approach managed to identify healthy trees with an average accuracy of 74%, while for trees with drying, the average accuracy was 69%. However, the optimal network identified olive trees (healthy or unhealthy) that the user did not detect during data collection. In the image classification approach, the application of convolutional neural networks achieved significantly better results with an F1-score above 0.94, either in the new network training approach or by applying transfer learning. In conclusion, the use of computer vision techniques in UAV images identified damaged olive trees, while the image classification approach performed significantly better than object detection.

Application of high adaptive models in building damage assessment: case studies of earthquake in Turkey and hurricane in Louisiana

Application of high adaptive models in building damage assessment: case studies of earthquake in Turkey and hurricane in Louisiana

Life-cycle cost assessment of conventional and self-centering steel frames in seismic zones: An extra focus on environmental impacts

This study develops a life-cycle cost assessment framework that integrates seismic loss and environmental costs (carbon pricing). The analysis covers initial construction, operation and maintenance, seismic loss, end-of-life costs, environmental costs, and recycling benefits, demonstrated through a case study of five 9-story steel frames, including buckling-restrained braced frame (BRBF), conventional concentrically braced frame (CBF), and three self-centering braced frames (SCBFs) employing various self-centering technologies. Combining fragility and damage assessments, the probability of exceedance of seismic loss at a given ground motion intensity measures (IM) can be obtained by Monte Carlo simulations. Then, the total seismic loss of the structure over the lifespan is obtained based on site-specific hazard curves. Results show that seismic losses for the buildings range from $5.75 million to $12.3 million, with embodied carbon (EC) emissions from 1291 to 4167 tons. SCBFs have higher initial costs and emissions than conventional frames but reduce seismic-related emissions by up to 61.6 %. Seismic and environmental costs contribute significantly to the life-cycle cost, and all SCBFs outperform conventional frames in terms of collapse probability, post-earthquake losses, and emissions. Material recycling could reduce emissions by up to 1374.9 tons.

Support vector regression model for the prediction of buildings’ maximum seismic response based on real monitoring data

Maximum drift ratio (MDR), one of the engineering demand parameters (EDPs), provides fundamental physical value for predicting building damage. Existing machine learning based prediction models mainly rely on numerical simulation data or structural experiments and are not appropriate for prediction of seismic response of real structures. The New Earthquake Data (NDE1.0) is the most comprehensive publicly available dataset of actual structural seismic response observations. Currently the prediction models using NDE1.0 are mainly based on linear or log-linear regression. In this study, based on the NDE1.0 flatfile, we develop a full-feature support vector regression (SVR) based MDR prediction model (SVR-MDR), treating all the available 41 characteristic parameters including structural information as input feature. To improve the model’s efficiency and practical applicability, we also establish a reduced-feature SVR model (RSVR-MDR) by selecting 10 fundamental parameters based on SHapley Additive exPlanations (SHAP) values and the accessibility of features. Our results demonstrate that SVR-MDR model outperform other machine learning models such as kernel ridge regression and decision tree models, and SVR-MDR and RSVR-MDR models outperform conventional loglinear regression and multinomial models, because SVR can map the complex nonlinear function of multiple variables and consider the available information of buildings especially the fundamental frequency. The proposed RSVR-MDR model have promising potential application for post-event seismic damage assessment and post-event emergency response in near real time.

Impact performance and damage assessment of GFRP-RC columns at high temperatures: a numerical insight

Impact performance and damage assessment of GFRP-RC columns at high temperatures: a numerical insight

Quantitative assessment of thalamic damage and serum neurofilament light chain in relapsing-remitting multiple sclerosis

BackgroundThe study assessed group differences in the thalamus microstructural parameters among healthy individuals and relapsing-remitting multiple sclerosis (RRMS) patients and examined the relationship between quantitative MRI (qMRI) parameters and neurological scores, T2 lesion load, and serum neurofilament light chain (sNfL) levels. MethodsA total of 30 patients with RRMS and 26 age- and sex-matched healthy controls were recruited in this study. The qMRI images were obtained from these individuals. T2 lesion load, thalamic volume, and parameters of thalamic subnuclei were estimated. The neurological functions of participants were assessed using a battery of tests. sNfL concentrations were measured using the single molecule array (SIMOA) technique. ResultsT2 relaxometry in the whole thalamus and its subnuclei were increased, and showed an apparent correlation with T2 lesion load and the severity of MS (EDSS, MSSS, 9HPT, T25FW, SDMT). T1 variability was prolonged in most thalamic subnuclei, and it was correlated with the severity of MS (EDSS, 9HPT, SDMT). Thalamic volumetric parameters of MS patients were smaller than those of healthy controls (p < 0.001) and showed an apparent correlation with MS severity. Surprisingly, sNfL levels showed no correlation with T2 relaxometry, T1 variability, or thalamic volumetric parameters. ConclusionQuantitative synthetic MRI, especially, the metric T2 relaxation times provides a surrogate parameter for assessing underlying thalamic and subnuclei damage in the RRMS group. Integrating structural and quantitative MRI allows a better assessment of the neurodegeneration in the normal-appearing thalamus of RRMS.

Assessment of foundation damage during earthquakes using acoustic emission monitoring: An experimental study on the shaking table test

Assessing structural foundation damage following an earthquake is critical for safety evaluation. However, assessing the damage to pile foundations with traditional visual inspections and non-destructive testing methods is challenging. This study evaluated the use of acoustic emission (AE) monitoring for damage detection and location in foundation structures during earthquake simulations by conducting shaking table experiments. Scaled models of foundation structures with and without surrounding soil were used in the experiments, and the performance of the AE monitoring system was evaluated by comparing the AE parameters via visual inspection. The experimental results showed that the AE monitoring system could effectively predict the initiation of cracks in foundation structures that experienced an earthquake. In addition, an appropriate filtering criterion for the shaking table experiments was established based on the AE characteristics of the foundation structures during the earthquake simulation, thereby improving the performance of the AE monitoring system for damage location. Consequently, this study contributed to a better understanding of the applicability of AE monitoring systems to foundation structures during earthquakes.

Non-destructive AC impedance spectroscopy (ACIS) analysis to characterize the evolution of impact damage in UHPC

Ultra-high performance concrete (UHPC) widely serves as protection material owing to superior impact resistance. Rapid and effective assessment of impact damage level is crucial for ensuring structural safety. The non-destructive AC impedance responds sensitively to impact-induced microstructural changes, providing important information for understanding the damage development mechanism. Therefore, this paper intends to investigate the feasibility of AC impedance spectroscopy (ACIS) for UHPC impact damage detection. UHPC specimens with different types and volume fractions of steel fibers were prepared, subjected to multiple repetitive low-velocity hammer impacts to induce cumulative damage, and the impedance responses were simultaneously tested in the frequency range of 10 Hz-5 MHz. Additionally, the effects of steel fiber distribution characteristics and pull-out mechanism on the conductive path and impact resistance were explained through microstructural analysis and 3D reconstructed model. The results show that ACIS exhibits a strong correlation with impact damage, where the increment of the imaginary part can be used as an effective and sensitive indicator to achieve whole-process damage assessment. Compared to straight steel fibers, hooked-end and corrugated fibers greatly enhance the impact resistance of UHPC but cause a nonlinear response in impedance due to extracted deformations and orifice complexities. These findings demonstrate the potential application of ACIS for detecting impact damage in UHPC and warrant further investigation.

Effect of infill walls on the seismic performance of a severely damaged substandard RC building during the February 6, 2023, Kahramanmaras earthquake sequence

On February 6, 2023, two catastrophic earthquakes with moment magnitudes of Mw 7.7 and Mw 7.6 struck southern cities of Türkiye, occurring only nine hours apart. Spectral accelerations obtained from some of the recorded ground motions exceeded the design spectrum specified in the Turkish Building Earthquake Code (TBEC 2018) for the maximum credible earthquake level. This has prompted a review of seismic design practices within the framework of TBEC 2018. To address this concern, this study evaluates the performance of a reinforced concrete (RC) building with a ribbed slab that sustained heavy damage, according to official damage assessments, after the February 6th earthquake sequence. Numerical models were developed both with and without infill walls to assess their influence on the building’s response. Time history analysis and static pushover methods were used for its seismic performance assessment. Time history analyses were conducted under ground motions recorded at three different stations in close proximity to the investigated building. The inclusion of infill walls in the numerical model significantly influences the seismic performance analysis results. Peak floor accelerations of the building are determined to be higher at infilled model compared to the bare frame. The distribution of damaged columns in the actual building aligns well with the analysis results of the numerical model that includes infill walls. Notably, the numerical analysis using the ground motion record of the closest station to the building most accurately represents the actual damage distribution observed after the earthquakes. Finally, while the inclusion of infill walls in the model increases the lateral stiffness of the structure, it was also observed that displacement-dependent results obtained in the nonlinear time history analysis, such as relative story drifts, could increase depending on the selected ground motion record. This highlights the complex interaction between the infill walls, modal properties of the building, and the dynamic characteristics of the earthquake record.

Bayesian modeling of incompatible spatial data: A case study involving Post-Adrian storm forest damage assessment

Modeling incompatible spatial data, i.e., data with different spatial resolutions, is a pervasive challenge in remote sensing data analysis. Typical approaches to addressing this challenge aggregate information to a common coarse resolution, i.e., compatible resolutions, prior to modeling. Such pre-processing aggregation simplifies analysis, but potentially causes information loss and hence compromised inference and predictive performance. To avoid losing potential information provided by finer spatial resolution data and improve predictive performance, we propose a new Bayesian method that constructs a latent spatial process model at the finest spatial resolution. This model is tailored to settings where the outcome variable is measured on a coarser spatial resolution than predictor variables—a configuration seen increasingly when high spatial resolution remotely sensed predictors are used in analysis. A key contribution of this work is an efficient algorithm that enables full Bayesian inference using finer resolution data while optimizing computational and storage costs. The proposed method is applied to a forest damage assessment for the 2018 Adrian storm in Carinthia, Austria, that uses high-resolution laser imaging detection and ranging (LiDAR) measurements and relatively coarse resolution forest inventory measurements. Extensive simulation studies demonstrate the proposed approach substantially improves inference for small prediction units.