Damage Assessment Tool Research Articles

Assessment of whole cartilage surface damage in an osteoarthritis rat model: The Cartilage Roughness Score (CRS) utilizing microcomputed tomography

ObjectiveThis study aims to establish an accurate and robust imaging biomarker for pre-clinical osteoarthritis (OA) research, focusing on early detection of cartilage surface degeneration. MethodUsing 50 male Wistar rats, this study aims to observe Collagenase Induced Osteoarthritis (CIOA) progression through microcomputed x-ray tomography (µCT), histopathological analysis, and gait analysis. A novel parameter, Cartilage Roughness Score (CRS), was developed for assessing cartilage structural damage from µCT data and was compared with histological OARSI Cartilage Degeneration Score (OARSI CDS). Additionally, as CRS maps the full surface, it was used to simulate the level of uncertainty in histological sampling. ResultsCRS and OARSI CDS have a linear relationship. CRS for healthy cartilage is 2.75 (95% CI: 1.14-4.36) and with every 1 unit increase in OARSI, CRS is expected to increase by 0.64 (95% CI: 0.35-0.92). Cartilage degeneration due to CIOA was evident in both histopathological scoring and CRS. However, only CRS was sensitive enough to show consistent damage progression from day 10 to day 60. Furthermore, our simulation for histological sampling suggested that up to 16 coronal slices with 200µm spacing would be needed to accurately represent the full extent of cartilage surface degeneration in a slice wise manner. Gait analysis showed changes solely at eight days post-collagenase injection, normalizing by day 60. ConclusionThe CRS analysis method emerges as a robust tool for cartilage surface damage assessment. This study demonstrates the potential of automatic 3D analysis over the traditional 2D histological approach when evaluating cartilage surface damage. Data statementMicro-computed tomography data is available via ida.fairdata.fi46 (https://doi.org/10.23729/f33d3ba8-01b8-47af-90dd-94b7c7861247)

Low cycle fatigue of doped tetrahedral amorphous carbon coatings by repetitive micro scratch tests

In contrast to the conventional progressive scratch testing where load is increased up to a critical damage level, the repetitive scratch testing is an assessment of coating behavior under repeatedly applied subcritical load. It often represents a better approximation to actual application conditions of well-designed coating systems where overcritical conditions typically do not occur. In this work, the cyclic micro scratch behavior of differently doped amorphous carbon coatings with similar coating thicknesses were studied using repetitive scratch tests at loads that correspond to 40 %, 60 % and 80 % of the critical loads from progressive scratch testing. Depending on the doping element, the structural and mechanical properties changed, in some cases considerably, which also had a clear effect on the test results. The damage progression was examined using optical microscopy, friction coefficient, and residual depth measurements. The progression of failure phenomena was categorized into different stages. Furthermore, the critical number of cycles for coating failure was recorded at the various load levels. For the identification of the critical cycles, the use of the deviation of the friction coefficient from its mean value has proven to be a valuable parameter. The approach of testing at different load levels allows the creation of low cycle fatigue curves to evaluate the coating behavior. It was found that the hardness and residual stresses of the coatings had different effects on the lifetime of the coatings at the various load levels, i.e. higher hardness and residual compressive stresses were only beneficial for moderate stress levels with low plastic substrate deformation. The results demonstrate that the cyclic scratch test is a powerful tool for comparative damage assessment at different load scenarios.

Evaluation of the Correlation Between Electrocardiographic Abnormalities, Cardiac Iron Overload, and Magnetic Resonance T2* Values in Patients with Beta Thalassemia Major

Background: Beta thalassemia major (β-TM) is an inherited blood disorder. Affected patients require frequent blood transfusions, leading to iron deposition and end organ damage, particularly myocardial dysfunction. A 12-lead ECG is a readily available tool that could be used to screen for conduction abnormalities and arrhythmias as a marker of worsening myocardial function. Methods: A total of 108 β-TM patients were evaluated for correlation between abnormal findings on the surface ECG and severity of myocardial iron deposition in magnetic resonance imaging as measured by T2* levels. Results: Patients with T2* below 20 msec had significantly longer PR intervals, P wave durations, and QTc intervals. Patients with T2* below 10 msec had the longest QRS duration and QRS activation times. Atrial fibrillation was more prevalent in patients with lower T2* levels. With a decrease in T2*, the probability of notching of QRS in the limb and precordial leads increased. Conclusion: Abnormal ECG is prevalent in β-TM patients, and the frequency of changes increases with the severity of iron overload. A 12-lead ECG is a valuable and readily available tool for the early assessment of myocardial damage and the implementation of a timely and appropriate management strategy.

Analytical review of methods and tools for assessing crop damage caused by elephants: implications of new information technologies

The devastation of crops by elephants is increasingly fuelling the anger of farmers in the human-elephant conflict. However, the crop damage attributed to elephants by farmers appears disproportionate. Despite the fact that observed crop damage has an impact on the lives of local populations, the assessment of damage remains a controversial issue. The multitude of evaluation methods used by informants makes it difficult to compare results from different areas and to harmonise and/or transpose management strategies. It is therefore wise to take an interest in evaluation methods, to know their constraints, their advantages, and their connection points. This is the purpose of this study, which offers a bibliographic synthesis of the subject based on documentary research and a synthetic analysis of articles and documents. Two types of damage assessment emerge: quantitative and qualitative. Each type of assessment requires different assessment methods, which can be combined to obtain more accurate results. A qualitative assessment can be carried out through interviews, questionnaires, bibliographic research/review, etc., and a quantitative assessment through a total site visit, a visit to conflict hot and cold spots, or a visit to a random sample of the site. Ratings can be combined because qualitative rating is subjective. Many informants use it because it is easy to carry out and gives an idea of the state of devastation of cultures and sociological constraints. Quantitative assessment requires resources but provides results on the ground. However, combining different types of assessment improves the accuracy, reliability of results, and understanding of the conflict. This bibliographic synthesis also reveals that damage assessment tools are today moving towards new information and communication technologies, because these make the work easier and more practical while improving precision.

A particle finite element method approach to model shear cutting of high-strength steel sheets

AbstractShear cutting introduces residual strains, notches and cracks, which negatively affects edge-formability. This is especially relevant for forming of high-strength sheets, where edge-cracking is a serious industrial problem. Numerical modelling of the shear cutting process can aid the understanding of the sheared edge damage and help preventing edge-cracking. However, modelling of the shear cutting process requires robust and accurate numerical tools that handle plasticity, large deformation and ductile failure. The use of conventional finite element methods (FEM) may give rise to distorted elements or loss of accuracy during re-meshing schemes, while mesh-free methods have tendencies of tensile instability or excessive computational cost. In this article, the authors propose the particle finite element method (PFEM) for modelling the shear cutting process of high-strength steel sheets, acquiring high accuracy results and overcoming the stated challenges associated with FEM. The article describe the implementation of a mixed axisymmetric formulation, with the novelty of adding a ductile damage- and failure model to account for material fracture in the shear-cutting process. The PFEM shear-cutting model was validated against experiments using varying process parameters to ensure the predictive capacity of the model. Likewise, a thorough sensitivity analysis of the numerical implementation was conducted. The results show that the PFEM model is able to predict the process forces and cut edge shapes over a wide range of cutting clearances, while efficiently handling the numerical challenges involved with large material deformation. It is thus concluded that the PFEM implementation is an accurate predictive tool for sheared edge damage assessment.

Damage evaluation of craniofacial localized scleroderma using magnetic resonance imaging.

Localized scleroderma (LoS) is an autoimmune disease in which craniofacial lesions can cause severe facial deformities with brain involvement. Objective evaluation of craniofacial LoS is challenging. Magnetic resonance imaging (MRI) may be used as a damage assessment tool. This study aimed to analyze the tissue involvement of craniofacial LoS based on MRI and evaluate MRI for craniofacial LoS assessment. This cross-sectional study included patients with craniofacial LoS from September 2021 to August 2022 in Peking Union Medical College Hospital. Patients who were clinically assessed in a stable phase were enrolled; patients with previous surgical treatment or contraindications to MRI were excluded. Participants underwent clinical, MRI, and ultrasound assessments. MRI was compared with ultrasound by correlation analysis and Bland-Altman analysis. The involvement of different tissues and different facial subunits was compared. The accumulated soft tissue atrophy index (ASTAI) was compared with clinical scores by correlation analysis. A total of 28 patients were included (13 female; mean age, 18 years). MRI showed a good correlation and agreement with ultrasound (r=0.916, P<0.001). In different facial subunits, a significant negative correlation between the forehead and chin was found (r=-0.593, P=0.001). The ASTAI correlated well with the facial LoS damage index (r=0.580, P=0.001) and the Peking Union Medical College LoS facial aesthetic index (PUMC LoSFAI) (r=0.921, P<0.001). A total of 38.6% of clinical scores were inaccurate based on MRI. Neurological changes were found in one patient. MRI can reliably quantify damage in craniofacial LoS, and may serve as a useful and objective tool for overall craniofacial LoS evaluation.

Energy Flow Considerations for Structural Response under Blast

Load-impulse (P-I) diagrams are commonly used for structural design and damage assessment. Recently, an energy-based alternative to P-I diagrams, known as Energy vs. Energy Rate (E-R) diagrams, was proposed. E-R diagrams are another tool for structural design and damage assessment from an energy flow perspective. This paper describes a study to assess and compare these two analytical approaches, and to provide examples for illustrating the findings.

Machine Learning-Based Seismic Damage Assessment of Residential Buildings Considering Multiple Earthquake and Structure Uncertainties

Wood-frame structures are used in almost 90% of residential buildings in the United States. It is thus imperative to rapidly and accurately assess the damage of wood-frame structures in the wake of an earthquake event. This study aims to develop a machine-learning-based seismic classifier for a portfolio of 6,113 wood-frame structures near the New Madrid Seismic Zone (NMSZ) in which synthesized ground motions are adopted to characterize potential earthquakes. This seismic classifier, based on a multilayer perceptron (MLP), is compared with existing fragility curves developed for the same wood-frame buildings near the NMSZ. This comparative study indicates that the MLP seismic classifier and fragility curves perform equally well when predicting minor damage. However, the MLP classifier is more accurate than the fragility curves in prediction of moderate and severe damage. Compared with the existing fragility curves with earthquake intensity measures as inputs, machine-learning-based seismic classifiers can incorporate multiple parameters of earthquakes and structures as input features, thus providing a promising tool for accurate seismic damage assessment in a portfolio scale. Once trained, the MLP classifier can predict damage classes of the 6,113 structures within 0.07 s on a general-purpose computer.

Exploring MapSwipe as a Crowdsourcing Tool for (Rapid) Damage Assessment: The Case of the 2021 Haiti Earthquake

Abstract. Fast and reliable geographic information is vital in disaster management. In the late 2000s, crowdsourcing emerged as a powerful method to provide this information. Base mapping through crowdsourcing is already well-established in relief workflows. However, crowdsourced post-disaster damage assessment is researched but not yet institutionalized. Based on MapSwipe, an established mobile application for crowdsourced base mapping, a damage assessment approach was developed and tested for a case study after the 2021 Haiti earthquake. First, MapSwipe’s damage mapping results are assessed for quality by using a reference dataset in regard to different aggregation methods. Then, the MapSwipe data was compared to an already established rapid damage assessment method by the Copernicus Emergency Management Service (CEMS). Crowdsourced building damage mapping achieved a maximum F1-score of 0.63 in comparison to the reference data set. MapSwipe and CEMS data showed only slight agreement with Cohen’s Kappa values reaching a maximum of 0.16. The results highlight the potential of crowdsourcing damage assessment as well as the importance for a scientific evaluation of the quality of CEMS data. Next steps for further integrating the presented workflow into MapSwipe are discussed.

Fibroblast Growth Factor 23: Potential Marker of Invisible Heart Damage in Diabetic Population.

Two-dimensional speckle-tracking echocardiography (2DSTE) detects myocardial dysfunction despite a preserved left ventricular ejection fraction. Fibroblast growth factor 23 (FGF23) has become a promising biomarker of cardiovascular risk. This study aimed to determine whether FGF23 may be used as a marker of myocardial damage among patients with diabetes mellitus type 2 (T2DM) and no previous history of myocardial infarction. The study enrolled 71 patients with a median age of 70 years. Laboratory data were analyzed retrospectively. Serum FGF23 levels were determined using a sandwich enzyme-linked immunosorbent assay. All patients underwent conventional echocardiography and 2DSTE. Baseline characteristics indicated that the median time elapsed since diagnosis with T2DM was 19 years. All subjects were divided into two groups according to left ventricular diastolic function. Individuals with confirmed left ventricular diastolic dysfunction had significantly lower levels of estimated glomerular filtration rate and higher values of hemoglobin A1c. Global circumferential strain (GCS) was reduced in the majority of patients. Only an epicardial GCS correlated significantly with the FGF23 concentration in all patients. The study indicates that a cardiac strain is a reliable tool for a subtle myocardial damage assessment. It is possible that FGF23 may become an early diagnostic marker of myocardial damage in patients with T2DM.

A Rapid Seismic Damage Assessment (RASDA) Tool for RC Buildings Based on an Artificial Intelligence Algorithm

In the current manuscript, a novel software application for rapid damage assessment of RC buildings subjected to earthquake excitation is presented based on artificial neural networks. The software integrates the use of a novel deep learning methodology for rapid damage assessment into modern software development platforms, while the developed graphical user interface promotes the ease of use even from non-experts. The aim is to foster actions both in the pre- and post-earthquake phase. The structure of the source code permits the usage of the application either autonomously as a software tool for rapid visual inspections of buildings prior to or after a strong seismic event or as a component of building information modelling systems in the framework of digitizing building data and properties. The methodology implemented for the estimation of the RC buildings’ damage states is based on the theory and algorithms of pattern recognition problems. The effectiveness of the developed software is successfully tested using an extended, numerically generated database of RC buildings subjected to recorded seismic events.

Damage assessment using the Lamb wave factorization method

The Lamb wave technique has been widely used for damage detection in plate-like structures. However, for two-dimensional (2D) defects like corrosions or one-dimensional (1D) defects like cracks, conventional Lamb wave-based methodology usually selects the detection algorithm in terms of the damage type, which is restricted if the defect remains unknown. To address the issues, this paper proposes a damage assessment approach based on the Lamb wave factorization method, which is a general method that can adapt to the detection of both 2D and 1D defects. In the process, both the forward-scattered wave across the inspection area and the wave back-scattered from the defect are used for detection, so that the enclosed rich information can be fully exploited. To measure the wave field of defect, a transducer array is firstly set around the inspection area, whose transducers would take turns to excite the Lamb wave while others remain listeners. On the basis, the scattered waves of all directions are then extracted and transformed from the time domain to frequency domain. Under a certain frequency, the inspection area is next visualized by sampling the area and solving the inverse scattering problem using the spectral data at each sampling point. By fusing the results of different frequency together, the final image can be eventually obtained. The method is then numerically investigated and also validated through experiments. The results demonstrate that it can give out the outline description of 2D defect and the size evaluation of 1D defect accurately, thus being an effective tool for damage assessment.

Fragility functions for fiber-reinforced polymers strengthened reinforced concrete beam-column joints

Post-earthquake field inspections have outlined the poor seismic performance of non-conforming reinforced concrete (RC) beam-column joints (BCJs) of existing RC buildings. Experimental and analytical studies have demonstrated that the application of externally bonded fiber-reinforced polymers (FRPs) is an efficient and cost-effective strengthening technique to improve the seismic performance of deficient BCJs. However, seismic losses for FRP-strengthened BCJs at different damage states (DSs) are currently not quantified. A potential seismic damage assessment tool is critical to obtain reliable estimations of the effects of FRP strengthening on reducing the expected damage and losses in existing buildings. Therefore, experimental-based fragility functions for FRP-strengthened corner and exterior BCJs at different limit states are proposed in this study. Experimental tests on 70 corner and 28 exterior FRP-strengthened BCJs are first collected. Then, DSs with increasing severity (i.e., light, moderate, and heavy) are defined according to widely recognized studies. These DSs are quantified on the basis of inter-story drift ratios (IDRs). After retrofitting, IDR-based fragility functions are generated for strengthened BCJs classified in terms of failure mode and achieved ductility level. The proposed functions are also compared with fragility functions available for non-conforming and conforming joints obtained from the literature. Finally, an application of the proposed fragility functions within a performance-based earthquake engineering (PBEE) framework for quantifying expected losses (ELs) and the benefits of FRP strengthening of BCJs is showed.

Machine Learning-based Seismic Fragility Curves for RC Bridge Piers

A significant part of ongoing studies in the field of earthquake engineering is directed toward the seismic risk assessment of buildings and infrastructures at a territorial scale. This task is usually accomplished by grouping the structures into homogenous classes in terms of typology, for which seismic fragility curves are then obtained for different limit states via numerical simulations or from the statistical analysis of observational data when available. Particularly, the development of typological fragility curves for bridges under earthquake is useful for assessing the reliability and resilience of transportation networks in seismic areas and can be also effective decision-making support. Within this framework, the proposed study establishes a machine learning-based paradigm for the closed-form prediction of the main statistical parameters required to obtain relevant seismic fragility curves for reinforced concrete bridge piers. Initially, a huge training dataset has been obtained by Monte Carlo simulations and displacement-based bridge pier assessments by assuming data representative of the Italian highway transportation network. Next, symbolic nonlinear regression formulae for estimating the main statistical parameters of seismic fragility curves have been generated. With the aid of those formulae, the effort of calculating the seismic fragility curves is greatly reduced since the corresponding main statistical parameters can be directly calculated from a set of commonly available attributes. Therefore, the proposed study provides a helpful tool for the rapid preliminary assessment of damage and risk level of existing highway transportation networks exposed to seismic hazards.

Simplified Damage Assessment Tool for Rails and Crossings Based on Standard Wear and RCF Models

A numerical tool is proposed to simultaneously assess various damage mechanisms that are driven by contact loading. The tool transfers loads to the contact-patch level using three contact parameters: the maximum contact pressure (pmax), the creepage (c) and the contact length (2a). The local wear and RCF predictions are implemented based on existing models from the literature. The load input can originate from numerical vehicle–track simulations or manual input of the user. The assessment tool is applied for a finite element analysis of a fixed manganese crossing nose to prove its validity. The algorithm is implemented via an automated Python code, which, on the one hand enables damage prediction for track components based on standard damage models. On the other hand, knowledge of novel local contact damage models can be transferred to the scale of track components.

An innovative structural health assessment tool for existing precast concrete buildings using deep learning methods and thermal infrared satellite imagery

Currently, there is a limited number of tools that can be used to assess progressive damage of buildings in large-scale study areas. The effectiveness of such tools is also constrained by a lack of sufficient and reliable data from the buildings and the area itself. This research article presents an innovative framework for damage detection and classification of precast concrete (PC) buildings based on satellite infrared (IR) imagery. The framework uses heat leakage changes over time to assess the progressive damage of buildings. Multispectral satellite images are used for a spatial scanning and large-scale assessment of a study area. A deep learning object detection algorithm coupled with two pixel intensities classification approaches are utilized in the framework. The proposed framework is demonstrated on two case study areas (parts of Karaganda and Almaty cities) in Kazakhstan using a set of multitemporal satellite images. Overall, the proposed framework, in combination with a YOLOv3 algorithm, successfully detects 85% of the PC buildings in the study areas. The use of a peak heat leakage classification approach (in comparison to mean heat leakage classification) over the 4 years showed a good agreement with the proposed framework. On-site visual inspections confirmed that PC buildings that were classified as having “High damage probability” have indeed evident signs of deterioration, as well as a more heat leakage than the rest of the buildings in the study areas. Whilst the framework has some limitations such as its applicability to extreme continental climate and its low sensitivity to detect minor damage, the proposed innovative framework showed very promising results at detecting progressive damage in PC buildings. This article contributes towards developing more efficient long-term damage assessment tools for existing buildings in large urban areas.

A Novel Power Distribution Network Assessment Approach Using Drones Considering Wireless Charging

Power networks are an important infrastructure that requires close monitoring and recurring assessment during and following a catastrophic event. The drone’s unique aerial and unmanned nature has made it an efficient and powerful tool for damage assessment of the power networks. In this article, we propose a drone-routing framework to enable the systematic and automatic assessment of power networks considering wireless charging of the drone during the scanning. This article incorporates the resilience-oriented line priority index to periodically prioritize the power lines based on their specifications and conditions, and by doing so, this improves the efficiency of the assessment. A multiobjective mixed-integer linear programming model is developed to dynamically determine the drone’s optimal routing and speed in order to assure that the drone gathers sufficient data while completing the assessment mission in the shortest period of time. Since the proposed optimization algorithm needs to be solved multiple times for different sections of the power network, we propose a set of solution algorithms to reduce the computational burden and allow the proposed algorithm to reach the optimal solution quickly. Simulation results on a power network consisting of 77 nodes and 73 lines illustrate the effectiveness of the proposed approach in performing network-wide dynamic assessment using a drone.

Exploiting HBIM for Historical Mud Architecture: The Huaca Arco Iris in Chan Chan (Peru)

The construction technique of raw earth, which has always been in use in most of the world, has left large monuments or architectural complexes to cultural heritage that need special attention due to the notable vulnerability of the material. A convenient way to deal this threat, besides physical intervention, is by using an information system, such as HBIM (Heritage Building Information Modeling), as a tool for damage assessment and conservation planning. This paper reports on its application in an archaeological setting, in particular, on the Huaca Arco Iris, a religious building of the old city of Chan Chan (Peru), the largest monumental complex in mud on the American continent. The study is part of the bilateral international project between the Consiglio Nazionale delle Ricerche (CNR) and the Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica (CONCYTEC) in the use of HBIM for the prediction of possible natural or anthropogenic damages to buildings in raw mud. Exploiting the data coming from the direct and indirect analyses, a dedicated ontology is built to guide the management of these data within the information system. The creation of an HBIM system for the archaeological domain, based on the trinomial data–information–knowledge, is presented and validated. Following this approach, a customizable HBIM has been created with the 3D model of the spatial entities of the Huaca. As a result, the semantic relationship of an external wall, taken as the benchmark test of our experiment, with the contained bas-relief and the conservation cover is tested.

T-REX: Numerical tool for tungsten damage assessment for DEMO

One of the R&D focus in the European fusion energy program is to establish a physical and technological basis for reliable power exhaust during entire operational situations of a DEMOstrational power plant. Following the design adopted for ITER, the baseline Plasma Facing Component for DEMO divertor is made of tungsten as armor material. The tungsten divertor target are the most thermally loaded in-vessel components in fusion reactor. The PFCs lifetime is affected by material degradations under the different loadings including High Heat Flux (HHF) and neutron irradiation. It has been reported a loss of mechanical properties due to softening (restoration/recrystallization). Latest finite element modeling developments allow to estimate component lifetime under HHF loading taking into account the progressive mechanical properties change due to softening. We propose here to take into account the influence of thermal and neutron loadings on the tungsten mechanical properties change for PFC lifetime assessment. For this, up to date mechanical properties of tungsten, softened tungsten, and neutron-irradiated tungsten are used as input data in the tool named T-REX (for Tungsten-Response based on EXperimental data). This tool is implemented as a user subroutine in a finite elements code. The influence of the neutron irradiation is studied as function of the dpa dose, the incident heat flux intensity and the current defined divertor component geometries (ITER and DEMO). With the study performed, it is shown that the geometry may be optimized to reduce the damage induced by plasticity. The numerical results also highlights that the plastic strain increment obtained after each thermal cycle reduces with the dpa dose.

Structural Damage Assessment Using Multiple-Stage Dynamic Flexibility Analysis

Vibration-based damage assessment technology is a hot topic in aerospace engineering, civil engineering, and mechanical engineering. In this paper, a damage assessment approach using multiple-stage dynamic flexibility analysis is proposed for structural safety monitoring. The proposed method consists of three stages. The content of Stage I is to determine the number of damaged elements in the structure by the rank of dynamic flexibility change. The content of Stage II is to determine damage locations by the minimum rank of flexibility correlation matrices. Finally, the damage extents of those damaged elements are calculated in Stage III. The proposed approach fully uses the filtering ability of matrix rank analysis for data noise. A 27-bar truss structure and a steel frame structure are used as the numerical and experimental examples to demonstrate the proposed method, respectively. From the numerical and experimental results, it is found that structure damages can be successfully identified through the multiple-stage dynamic flexibility analysis. By comparative study, the proposed method has more powerful antinoise ability and higher calculation accuracy than the generalized flexibility method. The proposed method may be a promising tool for structural damage assessment.