Damage Assessment Method Research Articles

Determination of Distributional Characteristics and Efficacy of Hand Radiographic Damage in Patients with Rheumatoid Arthritis Using a Novel Damage Assessment Method

Background: The purpose of this study was to identify the distribution characteristics of radiological damage to the hand of rheumatoid arthritis patients and evaluate its effectiveness using a new damage assessment method.Methods: Hand radiographs of a total of 127 rheumatoid arthritis patients were evaluated. We simplified the pre-existing van der Heijde modified total sharp score to assess the extent of bone damage. We selected 36 joints in both hands as regions of interest in our own way, and the erosion and joint space narrowing were scored.Results: When the erosion and joint space narrowing score values of each joint of the hand were added together, the joint space narrowing score was higher than the overall erosion score. We can see that the scaphoid,lunate,radius and ulnar joints have higher erosion scores, and the 4th proximal interphalangeal (PIP), Scaphoid, Lunate, and Radius joints have higher joint space narrowing scores than other joints. These damage assessment results were similar to the results from the previous damage assessment model.Conclusions: The joint space narrowing score is higher than the erosion score, suggesting that joint space narrowing precedes erosion. When checking the hand X-ray of rheumatoid arthritis patients, it is necessary to carefully check the scaphoid,lunate,radius and ulnar and 4th PIP joints, which relatively high radiographic damage scores. Also, the damage assessment model used in this study is relatively simple and does not show difference in analysis results from existing assessment models, so it can be considered for application in follow-up research.

Assessment of Mechanical Damage and Germinability in Flaxseeds Using Hyperspectral Imaging.

The high demand for flax as a nutritious edible oil source combined with increasingly restrictive import regulations for oilseeds mandates the exploration of novel quantity and quality assessment methods. One pervasive issue that compromises the viability of flaxseeds is the mechanical damage to the seeds during harvest and post-harvest handling. Currently, mechanical damage in flax is assessed via visual inspection, a time-consuming, subjective, and insufficiently precise process. This study explores the potential of hyperspectral imaging (HSI) combined with chemometrics as a novel, rapid, and non-destructive method to characterize mechanical damage in flaxseeds and assess how mechanical stresses impact the germination of seeds. Flaxseed samples at three different moisture contents (MCs) (6%, 8%, and 11.5%) were subjected to four levels of mechanical stresses (0 mJ (i.e., control), 2 mJ, 4 mJ, and 6 mJ), followed by germination tests. Herein, we acquired hyperspectral images across visible to near-infrared (Vis-NIR) (450-1100 nm) and short-wave infrared (SWIR) (1000-2500 nm) ranges and used principal component analysis (PCA) for data exploration. Subsequently, mean spectra from the samples were used to develop partial least squares-discriminant analysis (PLS-DA) models utilizing key wavelengths to classify flaxseeds based on the extent of mechanical damage. The models developed using Vis-NIR and SWIR wavelengths demonstrated promising performance, achieving precision and recall rates >85% and overall accuracies of 90.70% and 93.18%, respectively. Partial least squares regression (PLSR) models were developed to predict germinability, resulting in R2-values of 0.78 and 0.82 for Vis-NIR and SWIR ranges, respectively. The study showed that HSI could be a potential alternative to conventional methods for fast, non-destructive, and reliable assessment of mechanical damage in flaxseeds.

Segmented superimposed model of near-bore reservoir pollution skin factor for low porosity and permeability sandstone horizontal gas wells

Drilling and completion processes can often result in reservoir contamination around the wellbore, leading to decreased oil and gas productivity and significant economic losses for the oil field. This issue is particularly complex in sandstone reservoirs with low porosity and permeability horizontal wells, wherein traditional models have limited accuracy and applicability due to numerous unknown parameters. To address this challenge, this study focuses on non-uniform pollution around horizontal wells and proposes a new approach to divide the horizontal well pollution area into N micro-element sections. By establishing a seepage differential equation and employing the similar flow substitution method, we construct models for the pollution skin coefficient of each micro-element section as well as the total pollution skin coefficient. Furthermore, we combine empirical equation models and an oscillation-decreasing function model to develop a pollution radius distribution model that encompasses linear, parabolic, exponential, and logarithmic patterns. Through these advancements, we can realize a comprehensive reservoir damage assessment method. It is verified that the calculation error of this model is very small, and the influence of skin effect and reservoir anisotropy and the radius distribution of various heterogeneous pollution zones are fully considered. These findings indirectly suggest the rationality and practicality of the model presented in this paper. By incorporating actual gas well data into this model, it has been determined through discussion and analysis that the exponential distribution of the pollution radius has the greatest impact on the pollution skin factor along the horizontal well, from the heel to the toe. Increasing the pollution radius and decreasing the pollution permeability both result in an increase in the skin factor of the micro-segment and the total pollution skin factor of the horizontal well. However, compared to the pollution permeability, the radius of the pollution zone has a relatively minor effect on the total pollution skin factor. The proposed technique aims to serve as a valuable tool in optimizing and designing stimulation measures aimed at boosting production and minimizing formation damage. Through evaluation and analysis to reduce risks, protect reservoirs and extend well life, reduce costs, and enhance technical capabilities and economic benefits.

Unsupervised structural damage assessment method based on response correlations

Structural damage assessment for civil engineering structures has become an important research topic and has gained much attention for decades in the field of structural health monitoring (SHM). Current unsupervised structural damage assessment methods can achieve damage detection for practical civil engineering structures. However, these methods cannot fully mine the response correlations among sensors, thus are difficult to localize damage in practical engineering applications. Therefore, this study proposes an unsupervised structural damage assessment method with bidirectional long short-term memory (BiLSTM) networks to establish the response correlations among sensors. Numerical studies of a beam-like bridge model, an experimental study of Yellow Frame, and an in-field study of Yonghe Bridge were conducted to validate the accuracy and reliability of the proposed approach. The results show that unsupervised structural damage identification and localization can be achieved even in the case of sparse sensor deployment due to the fact that the response correlations among sensors can be well extracted. The proposed approach can be applied to damage identification of practical engineering structures under operational and ambient excitations and can automatically provide damage location warnings to ensure structural health and prevent catastrophic events.

The Effect of Water Below the Road Surface on Surface Distress Index (SDI) Values in the Road Condition Survey (RCS) Method

The results of the road damage assessment will reveal the Surface Distress Index (SDI) value according to the Bina Marga method (potholes, cracks and rutting). To determine the effect of subsurface water on road damage, geo-electrical measurements were carried out at road locations where it is estimated that there is subsurface water. At the location of the road segment, the damage value is then compared between the road with normal conditions SDI=aNx±ε and the road with water below the road surface. The results of the SDI value are then compared with the number of tracks of heavy vehicles (N) according to Equivalent Single Axle Load (ESAL) and the area of distribution of water below the surface (Aw). So it is expected to know the correlation model between road damage and the influence of water under the road surface surface SDI’=kAwn. These results will also be used as additional damage assessment items in the new damage assessment method based on the Bina Marga method.

A modal-based method for blast-induced damage assessment of reinforced concrete columns: Numerical and experimental validation

Reinforced concrete (RC) columns play a crucial role in the overall performance of modern RC frame buildings under blast loading. The RC columns under blast loads might experience various irreversible consequences such as concrete cracks, crushing and spalling, changing the status of both modal properties and load-carrying capacity. In this paper, the blast-induced damage assessment of the RC columns involving the measurement of modal parameters was introduced and validated. Firstly, a modal-based method for blast-induced damage assessment of RC columns was proposed, in which the damage is characterized as the reduction of axial bearing capacity and further expressed as the explicit function of the vibration frequencies and the mass-normalized displacement mode shapes. Then, the finite-element analysis program LS-DYNA was employed to establish the numerical model of RC columns under axial compression loads and lateral blast loads. The residual load-bearing capacities and modal properties of the RC columns were illustrated. By using the numerical results collected in the present study, the validity of the proposed damage assessment method was discussed and proved numerically. Further, field explosion tests and experimental modal tests were conducted, and the results showed that the modal-based damage assessment method exhibited remarkable agreement with the blast damage determined by residual bearing capacities. Based on the numerical and experimental results, the proposed modal-based damage assessment method is applicable for the non-destructive evaluation of blast-induced damage of RC columns.

A Novel Method for Fatigue Damage Assessment in Bimodal Processes Considering High- and Low-Frequency Reduction Effects

Due to inherent nonlinearities within floating systems and the second-order wave forces affecting them, the dynamic responses of floating systems manifest as bimodal Gaussian processes. Consequently, the classical spectral fatigue assessment method grounded in the Rayleigh distribution cannot be applied. This paper introduces the double frequency coupled (DFC) method as a spectral fatigue assessment approach, providing an accurate estimation of fatigue damage originating from bimodal Gaussian processes. Within the DFC method, the bimodal Gaussian process is partitioned into two components: low-frequency (LF) and high-frequency (HF) processes. A Gaussian distribution is employed to describe the probability distribution function (PDF) of the amplitude reduction induced by the interaction between LF and HF processes. The PDF of small-cycle fatigue can be computed by convoluting the PDF of HF amplitudes and the reduction amplitude between LF and HF. Similarly, the PDF of large-cycle fatigue can be determined through convolution, which involves the PDF of LF amplitudes and small-cycle fatigue. The overall fatigue damage arising from the bimodal Gaussian process is obtained by directly summing the contributions of small-cycle and large-cycle fatigue. Numerical investigations of the DFC method’s effectiveness are presented through a series of parametric studies, demonstrating its robustness, efficiency, and accuracy within engineering expectations. Furthermore, the DFC method is found to be applicable to both single-slope and two-slope S-N curves.

An intelligent crack damage assessment method by integrating information and physics

This paper proposes a novel intelligent crack damage assessment method by integrating information and physics, in which automatic crack detection, real-time crack damage assessment, and external load optimization are integrated into a system. The pixel-level information of cracks is automatically identified and segmented by the mask region convolutional neural network (Mask R-CNN). A crack digital model is reconstructed by fusing perceived information and geometric models. An offline physical model and an online deep learning model are integrated for assessing crack damage status. Experiments show that the method can effectively detect and localize fatigue surface cracks together with a load optimization to ensure safe conditions when visible fatigue cracks appear.

Comparing Flood Damages to an Industrial Region in Turkey Using Several Methods

Floods are one of the disasters that cause the greatest damage to social life, living health, economy and environment. This paper reveals the calculation of the economic damage of the commercial areas located near the creek due to floods in Sakarya, Dinsiz Stream. In this study, first, flood propagation maps were produced according to 50, 100, 200 and 500 years of flood flow periods that may occur in the study area. The HECRAS program was used to produce the flood maps and a 2D model was created from these maps, water level maps were obtained and then depth damage curves were used to figure out the economic losses leading to flood. In addition to the Huizinga, van Eck and Kok, ICBR, Pistrika and Jonkman flood damage assessment methods, which are frequently used in the literature, the HAZUS-MH method was used to calculate the economic losses that may occur in commercial areas. While HAZUS has functions that make separate calculations for each commercial sector, in other methods, calculations are made over the total area without sector discrimination. In addition, HAZUS has more detailed ranges of flooding water depths and therefore the calculation of damage rates corresponding to water depths can be done with higher precision than other methods. According to the data obtained as a result of the study, the losses exceeded million euros in all methods, while the highest values were in the Pistrika and Jonkman methods.

Fine‐scale damage assessment of urban flooding based on the 1D/2D coupled hydrodynamic model

AbstractDue to the high population density and economic property in urban environments, urban flooding can result in substantial economic losses. However, accurate urban flood modeling and assessing the associated damages are challenging due to complex urban layouts and rugged topography. Furthermore, the rapid development of urban underground spaces has introduced additional complexities. To address the demanding need for accurate flood modeling and damage assessment in cities, a fine‐scale flood damage assessment method was proposed in this study, based on the 1D/2D coupled hydrodynamic model, which can not only assess the flood damage on the ground surface but also evaluate the flood loss in underground spaces. Taking the Gangxi drainage area in Wuhan City, China as a case, this study extensively discussed the flood inundation processes on the ground surface as well as in the buildings and underground spaces, under different rainfall scenarios with the return period ranging from 2 to 200 years. Based on the high spatial‐temporal hydrodynamic simulations, the flood damage degrees were evaluated for the buildings and underground spaces with different industry types. The results indicated that the inundation of buildings and underground spaces in densely built cities cannot be neglected in urban flood modeling. Buildings with public service attributes can have a higher flood damage degree, as compared with other types of buildings and underground spaces.

Fatigue damage assessment methodology for a deepwater subsea wellhead based on monitoring data

ABSTRACT Deepwater subsea wellhead system is subjected to the various cyclic loads transmitted from the drilling riser due to ocean waves, currents and platform motion during drilling operation; therefore, the fatigue damage is accumulated. A fatigue damage assessment methodology and its detailed analysis procedure for subsea wellheads utilising transfer function theory based on monitoring acceleration data at the drilling riser are developed. Studies on an actual monitoring case for a subsea wellhead under drilling operation with 336 h of monitoring events are conducted and a verification method by comparison of the measured fatigue results and predicted fatigue results based on wave and current data are introduced. The results for wave and vortex-induced vibration (VIV) fatigue life based on measured acceleration data are calculated, and the fatigue bias with predicted results are summarised. The developed fatigue damage assessment method and monitoring case studies can be valuable references for deepwater drilling engineering.

Quantifying acute kidney injury in an Ischaemia-Reperfusion Injury mouse model using deep-learning-based semantic segmentation in histology.

This study focuses on ischaemia-reperfusion injury (IRI) in kidneys, a cause of acute kidney injury (AKI) and end-stage kidney disease (ESKD). Traditional kidney damage assessment methods are semi-quantitative and subjective. This study aims to use a convolutional neural network (CNN) to segment murine kidney structures after IRI, quantify damage via CNN-generated pathological measurements, and compare this to conventional scoring. The CNN was able to accurately segment the different pathological classes, such as Intratubular casts and Tubular necrosis, with an F1 score of over 0.75. Some classes, such as Glomeruli and Proximal tubules, had even higher statistical values with F1 scores over 0.90. The scoring generated based on the segmentation approach statistically correlated with the semiquantitative assessment (Spearman's rank correlation coefficient=0.94). The heatmap approach localised the intratubular necrosis mainly in the outer stripe of the outer medulla, while the tubular casts were also present in more superficial or deeper portions of the cortex and medullary areas. This study presents a CNN model capable of segmenting multiple classes of interest, including acute IRI-specific pathological changes, in a whole mouse kidney section and can provide insights into the distribution of pathological classes within the whole mouse kidney section.

Damage Assessment of Segmental Concrete Beams with Different Types of Joints and Prestressed External Tendons

Precast segmental beams with prestressed external tendons have become increasingly common in bridge constructions owing to the merits of high-quality control and fast onsite construction with minimum environmental impact. Due to the nonlinear characteristics of the dynamic behavior of these segmental beams associated to the joint opening, the traditional condition monitoring and damage assessment methods based on vibration parameters either in time or frequency domain using the linear theory are unsuitable. To overcome this challenge, this paper proposes a damage assessment method through analyzing nonlinear structural dynamic responses for monitoring the conditions of segmental beams with different joint types and prestressed external tendons. Two damage indices are proposed to identify the occurrence of damage and to qualitatively indicate the damage severity. In the proposed approach, the measured dynamic responses of the segmental concrete beams under hammer impact are first adaptively decomposed into a finite number of mono-components by using variational mode decomposition (VMD) technique. Then, the instantaneous frequencies of the decomposed mono-components from the dynamic responses are obtained by conducting the Hilbert transform. Two damage indicators are defined for the damage assessment and for the identification of the damage location, respectively. Three four-span segmental beams with different types of joints (i.e. dry joints or epoxy joints) and prestressing tendon types (i.e. steel and carbon fiber reinforced polymer tendons) were constructed and tested under five loading levels with different damage severities. The feasibility and accuracy of the proposed approach are verified by comparing the identification results with the secant and initial stiffness extracted from the load–deflection curves at different loading levels. The effectiveness of these two damage indicators is also validated with the structural conditions observed in the tests. The results show that the proposed approach can successfully identify damage in segmental concrete beams.

A Graphical Ontology-Based Method for Rapid Damage Assessment of Stone Cultural Heritage Structures After an Earthquake: A Case Study of Mor Petrus and Mor Paulus Church, Adıyaman/Türkiye

To prevent further damage to historical structures and elements after an earthquake, it is important to implement temporary interventions that aim to stabilize and support the affected area. Within this scope, it is crucial to promptly identify the areas in historical buildings that require urgent intervention. The aim of this study is to design a standardized assessment method that provides an objective evaluation for post-earthquake damage detection in stone cultural heritage structures, independent of subjective assessments, and can be easily and quickly implemented in the field. For the proposed method, the heavily damaged Mor Petrus and Mor Pavlus Church in Adıyaman, which was affected by the 2023 earthquakes centered in Kahramanmaraş, is used as a case analysis. The results of the study demonstrate that the presented method is beneficial for detecting damages in masonry church structures after an earthquake. The graphical ontology-based method followed in the study allows for the identification of damages at the level of structural elements and focuses on areas with urgent damage.

Property Damage Assessment Methods and Models due to Armed Aggression

Abstract The paper deals with the conceptualization of the gross development value to assess direct damages and the restoration needs of lost, destroyed and damaged real estate as a result of armed aggression. A critical review of the existing practice of assessing property damage has been carried out. The measurement units of direct damages and the restoration needs, the evidence base used, the valuation methods for the determination of property damage are analyzed. The methodological potential of compounded cash flow models and the criteria for assessing their reliability is substantiated. A system of valuation models for calculating direct damages and restoration needs is proposed, depending on the category of real estate and market conditions at the valuation date. These valuation models are relatively simple to implement and understandable to the intended users of property damage valuation reports.

Systematic assessment method for post-earthquake damage of regional buildings using adaptive-network-based fuzzy inference system

Rapid damage assessment of regional buildings is critical in post-earthquake rescue and victim resettlement. In this study, a novel rapid damage assessment method for post-earthquake buildings based on multi-types of damage features was proposed. First, the roof-related damage features are extracted from UAV tilt photography with the method of multiscale segmentation and object-oriented classification. Then, the normalized digital surface model was introduced to determine the height-related damage features. Meanwhile, a lightweight CNN model was employed to realize the preliminary evaluation of building facade conditions. Finally, all obtained damage features were taken as inputs to an adaptive-network-based fuzzy inference system for damage assessment. The proposed method was characterized by the capability to comprehensively cover the damage features of regional buildings involved in the evaluation, and thus be able to obtain more accurate judgments than current methods based on a single feature. At the same time, the effectiveness and robustness of the proposed method were evaluated and demonstrated by the field test on the world's largest earthquake site in Beichuan, China, and the prediction of damage level on the testing set achieved an accuracy of 87.1%.

A novel double-hybrid learning method for modal frequency-based damage assessment of bridge structures under different environmental variation patterns

Monitoring of modal frequencies under an unsupervised learning framework is a practical strategy for damage assessment of civil structures, especially bridges. However, the key challenge is related to high sensitivity of modal frequencies to environmental and/or operational changes that may lead to economic and safety losses. The other challenge pertains to different environmental and/or operational variation patterns in modal frequencies due to differences in structural types, materials, and applications, measurement periods in terms of short and/or long monitoring programs, geographical locations of structures, weather conditions, and influences of single or multiple environmental and/or operational factors, which may cause barriers to employing state-of-the-art unsupervised learning approaches. To cope with these issues, this paper proposes a novel double-hybrid learning technique in an unsupervised manner. It contains two stages of data partitioning and anomaly detection, both of which comprise two hybrid algorithms. For the first stage, an improved hybrid clustering method based on a coupling of shared nearest neighbor searching and density peaks clustering is proposed to prepare local information for anomaly detection with the focus on mitigating environmental and/or operational effects. For the second stage, this paper proposes an innovative non-parametric hybrid anomaly detector based on local outlier factor. In both stages, the number of nearest neighbors is the key hyperparameter that is automatically determined by leveraging a self-adaptive neighbor searching algorithm. Modal frequencies of two full-scale bridges are utilized to validate the proposed technique with several comparisons. Results indicate that this technique is able to successfully eliminate different environmental and/or operational variations and correctly detect damage.

Response spectrum method for fatigue damage assessment of aero-hydraulic pipeline systems

This paper proposes a response spectrum method to assess the fatigue damage of aero-hydraulic pipeline systems subjected to random airframe motion. A real-valued analytical formula for calculating the random stress response of a clamp-pipeline model transporting fluid is derived using the complex modal superposition method and random vibration theory. Based on our previous work on the response spectrum method for fatigue damage assessment, the authors establish a set of complex modal combination formulas for the expected damage rate, introducing new velocity-velocity and displacement-velocity combination coefficients. These formulas characterise the expected damage rate as a linear combination of the damage responses that are not reliant on the spatial distribution of the pipeline model. The fatigue damage assessment of different pipeline models is computationally efficient as only the natural frequencies and the mode shapes need to be calculated. The correctness of the complex modal combination equation is verified by the rain-flow counting method, and the effect of the fluid velocity on the expected damage rate is discussed.

A Target Damage Effectiveness Assessment Mathematical Calculation Method with Uncertain Information Based on an Adaptive Fuzzy Neural Network

Aiming to address the issue of assessing the effectiveness of target damage in air defense interception under projectile and target intersection, this paper proposes a novel target damage effectiveness assessment mathematical calculation method using an adaptive fuzzy neural network. The design and calculation methods introduce the target cabin’s damage weight factor, the dispersion error and coverage density and fire density of warhead fragment, and the ratio of the number of warhead fragments to the cross-sectional area of the target as the primary damage factors, and we establish a new model for calculating the target damage efficiency using Takagi-Sugeno adaptive fuzzy neural network with the multiple damage factors. In the designed model, we take the characteristic parameters of warhead fragments formed by the projectile explosion as the sample data and learn the mapping relationship between damage factors and damage effect expressed by the system of fuzzy rule. Combined with the vulnerability weight and damage factors of the target, the fuzzy rule system is applied to predict and calculate the damage probability and damage effectiveness. In addition, we use shot-line technology to set up the intersection criterion between warhead fragments and target, analyze the coordinate relationship of the projectile explosion relative to the target based on the ground coordinate system, and derive the distribution density function of warhead fragment and research a calculation model for target damage probability with multiple vulnerable cabins. Finally, we use the established model to train, test, and calculate the data from the actual projectile and target intersection damage test and present the comparison results to other damage calculation methods. By calculation and comparative analysis, the results show the smaller the distance between projectile and target, as well as the smaller the average intersection angle, and the larger the weight coefficient of the warhead fragment covering the target cabin, the total target damage probability increases obviously. In the two experimental calculations, the target cabin with the largest weight coefficient was considered, and compared with the existing literature, the damage probability of the proposed calculation method was increased by 9.13% and 10.93%, respectively, and it is clear that the proposed target damage assessment method can effectively reflect the real target damage effectiveness in the state of projectile and target intersection.

Hypervelocity Impact Complex Defect Detection Method Based on Multiobjective-Guided Filter Infrared Thermal Image Fusion

The hypervelocity impact of space debris can cause complex types of defects for spacecraft, posing a significant challenge for spacecraft damage assessment. To meet the need for detecting complex defects, a novel spacecraft damage assessment method based on multi-objective guided-filter infrared thermal image fusion is proposed. The fusion needs of multiple types of defects are jointly optimised by considering them in the image fusion process. Various feature damage fusion needs are considered and optimised simultaneously in the image fusion process. Multiple cost functions for different categories of damage not only consider the edge preservation requirements of large defects but also retain more subtle damage details while suppressing noise. The experiment demonstrates that the propose method, which takes into account multiple damage fusion objectives, exhibits improved performance compared to the conventional single-target fusion methods.