Quantitative Damage Evaluation Research Articles

A Two-step Inverse Method for Quantitative Damage Evaluation of Plate-like Structures Using Vibration Approach

This study presents a novel two-step approach to assess plate-like structural laminar damages, particularly for delamination damage detection of composite structures. Firstly, a 2-D continuous wavelet transform is employed to identify the damage location and sizes from vibration curvature data. An inverse method is subsequently then used to determine the bending stiffness reduction ratio along a specified direction, enabling the quantification of the delamination severity. The method employed in this study is an extension from the one-dimensional inverse method developed in a previous work of the authors. The applicability of the two-step inverse approach is demonstrated in a simulation analysis and by an experimental study on a cantilever composite plate containing a single delamination. The inverse method is shown to have the capacity to reveal the detailed damage information of delamination within a constrained searching space and can be used to determine the effective flexural stiffness of composite plate structures, even in cases of complex delamination damage. Conflict of Interest Statement The authors declare no conflicts of interest.

Method for measuring laser damage threshold of optical thin film elements based on quantitative damage evaluation

Method for measuring laser damage threshold of optical thin film elements based on quantitative damage evaluation

Quantitative damage evaluation of tunnel subjected to a subsequent strong seismic after a quasi-static reverse faulting

An individual fault dislocation or seismic effect is often considered to investigate the deformation and failure of a fault-crossing tunnel, while emerged in high-intensity seismic zones, it will be likely to subject to a superimposed effect from fault dislocation and strong seismic excitation. In this study, a three-dimension numerical model to simulate fault dislocation and subsequently dynamic response characteristics of fault-crossing tunnel was established, in which fault dislocation and earthquake levels at the site of interest. The nonlinear damage feature of tunnel was considered to furtherly reveal damage states of tunnel under the superimposed effect. And then, a quasi-static large-scale model test was performed to verify the correctness of pre-order fault dislocation calculation results. The results showed that only when the tunnel was subjected to fault dislocation, a prominent relative deformation of tunnel exists in the fault fracture zone and tension and compression zone were noticeably identified at the convex and concave zones respectively. After superimposing the seismic loading, acceleration and equivalence plastic strain amplification of tunnel near the fault fracture zone is great of significance and the damage of the tunnel under the fault dislocation was concentrated at the fault core. More importantly, the fault-crossing tunnel is more of sensitivity to the initial fault dislocation from the obvious rising on the tension damage index, and when PGA is greater than 0.2 g, the tunnel emerged in the host rock mass should also be paid more attentions, not only the tunnel in the potential fault-affected zone.

High-velocity impact experiments and quantitative damage evaluation for finite ultra-high-performance concrete targets

In this work, we aim at improved characterization of target damage occurring as the result of projectile impact against ultra-high-performance concrete (UHPC). For this purpose, we present the results of high-velocity impact experiments with spherical steel projectiles and finite-thickness UHPC targets of approximately 115 MPa compressive cylinder strength in the impact velocity range from approximately 600 m/s to 1500 m/s. The data set obtained from these experiments includes residual projectile velocities as well as qualitative and quantitative information on damage. Quantitative damage information is mainly extracted from digital 3D post mortem targets, which are produced by 3D-scanning. For all damage quantities, a dependence on the impact velocity and the target thickness is discussed and used to provide possible explanations for the origin of the particular type of damage. The large data set presented in this work can constitute the basis for a comprehensive and quantitative verification and validation of analytical, empirical, and numerical models that describe the perforation of UHPC targets in the investigated impact velocity range.

Quantitative damage evaluation of curved plates based on phased array guided wave and deep learning algorithm

Recent advances in phased array guided wave (PAGW) have demonstrated the potential of minor damage detection and localization in widely used curved plates, but quantitative damage evaluation remains difficult since effective features that are sensitive to damage size are hard to extract. In this study, a novel integrated framework, GW-SHMnet, is proposed, which leverages the advantages of the PAGW, finite element (FE) modeling, and deep learning algorithm. Firstly, an FE model is constructed to simulate PAGW propagation in curved plates. Secondly, PAGW experiments are performed on a curved aluminum plate to validate the FE model. Thirdly, an FE simulation database considering different sensor locations, testing frequencies, and damage sizes, is constructed and used as the training and testing data. Finally, deep learning is used to automatically extract features to determine damage size. The effectiveness, accuracy, and robustness of GW-SHMnet enable autonomous quantitative evaluation of minor damage in curved plates.

Deep learning-based fatigue damage segmentation of wind turbine blades under complex dynamic thermal backgrounds

Passive thermography is an efficient method to inspect fatigue damage of large-scale structures such as wind turbine blades under cyclic loads. Quantitative damage evaluation often requires the damage region to be segmented from the thermal image, which challenges conventional image process techniques, especially when the structure is moving, and the thermal background is changing. This study proposes a model based on deep learning and thermography to automatically segment complex dynamic background of images taken from a wind turbine blade during cyclic loading and subsequently segment the fatigue blade damages. An automated background segmentation algorithm is developed to isolate the blade from the background using six state-of-the-art deep learning models. The most accurate model is then chosen and improved for the second step of damage segmentation, achieving a level of accuracy comparable to that of human observation, even with fewer images in the training process. The proposed background and damage segmentation methods have recall of 99% and 82%, respectively, indicating that the proposed approach is accurate, efficient, and robust.

Biomechanical modeling of rice seedling stalk based on multi-scale structure and heterogeneous materials

Rice seedling stalks are biologically characterized by multilayer porous structure and multiphase inhomogeneous material properties. This biological properties make it challenging to reveal the biomechanical behavior and damage mechanism of rice seedling stalks under mechanical load. In this research, the mechanical properties of the cell wall in each leaf sheath of rice seedling stalk were measured by nanoindentation, and a three-dimensional (3D) structure model of rice seedling stalk was established using micro-computed tomography (Micro-CT). According to the gray value difference in the CT images, rice seedling stalk was defined as three types of materials: protoplasts, parenchyma cell wall and collenchyma cell wall. A 3D finite element model of rice seedling stalk with heterogeneous material distribution was established based on the mapping relationship of gray value-density-elastic modulus. The accuracy of the model was verified by radial compression and axial tensile tests from three aspects: macroscopic mechanical response, microscopic mechanical behavior, and actual load effect. The results show that the modeling method is fast and reliable, and effectively improves the analytical accuracy of biomechanical properties for living plant materials with multi-layer porous structure characteristics, heterogeneous anisotropic material properties, and high moisture content. It also provides a useful tool for revealing the damage mechanism and quantitative damage evaluation of living plants under mechanical loading. This will be of great help to the optimization of the structure and working parameters of transplanting mechanism.

Quantitative damage evaluation of prestressed concrete containments with steel fiber strengthening under internal pressure

Current investigations cannot quantitatively predict the overall damage performance of prestressed concrete containment vessels (PCCVs) under internal pressure and the effect of steel fiber strengthening on the damage performance improvement has not been investigated. A new parameter definition is proposed to characterize the entire damage performance of conventional and steel fiber-reinforced concrete (SFRC) PCCVs under internal pressure in this study. The prestress loss is precisely calculated considering 40 years of the service period of PCCV. To review the possibility of operating lifetime extension of PCCV, the subsequent 60 years are also considered. Firstly, elaborate three-dimensional numerical models of conventional and SFRC containments were established. Then, three parameters to represent the extent corresponding to concrete, steel liner, and prestressed tendons of containment under damage were proposed. Finally, damage performance of conventional and SFRC containments considering the impact of prestress loss was discussed in depth. The results show that by introducing the SFRC and considering the five-year prestress loss, the pressure capacities for containment concrete, steel liner, and prestressed tendons are increased by 22.16%, 18.99%, and 12.35%, respectively. What's more, the proposed parameters can reflect the changing trend of the damage extent of containments under internal pressure and inclusion of steel fibers can improve damage performance significantly. To examine the realistic damage performance under internal pressure, the five-year prestress loss requires to be specifically considered.

Detection of Concrete Damage by X-ray CT and Related Non-destructive Elastic Wave Monitoring in Core Test

As a detailed inspection of a concrete structure in service, core samples are usually drilled out and then mechanical properties are measured. In this study, damage estimation of freeze-thawed concrete from concrete-core samples is developed, applying X-ray CT with machine learning method. By the authors, the quantitative damage evaluation of concrete has been proposed by applying acoustic emission and damage mechanics in core concrete. In this study, detection of cracking damage distribution in concrete-core by decision tree method. Prior to the machine learning analysis, distribution of micro-cracks in a concrete-core sample is inspected by helical X-ray computer tomography (CT). Two feature values are set as explanatory variables: luminance value and pixel value after DoG (Difference of Gaussian) filter. The evaluation values converged from the sample size of 12,000 or more. This suggests that one cross section can be estimated with about 50% of the X-ray CT image data by using machine learning. The accuracy rate is 0.723 for the threshold processing and 0.793 for the decision tree method, which is higher for the decision tree method. These results suggest that the proposed method is useful for identifying cracks in concrete by X-ray CT image.

Seismic Damage Evaluation of Beam-Column Joints in Monolithic Precast Concrete Frame Structures.

Quantitative calculation and evaluation of seismic damage are very important for structural safety, performance-based structural analysis, and seismic reinforcement. However, the relevant research results for precast concrete structures are extremely limited. In this paper, the seismic damage evaluation of beam-column joints in monolithic precast concrete frames was studied through cyclic loading tests and damage index calculation. The seismic damage process, load-displacement relationship, stiffness degradation, and the influence of axial compression ratio were analyzed, then the damage indexes were calculated and analyzed, and the quantitative evaluation of joint damage was conducted last. The results show that the connection seams can significantly affect the mechanical properties of precast joints, easily causing damage concentration, resulting in a lower bearing capacity and faster stiffness degradation compared with a cast-in-situ joint. A larger axial compression ratio can bring higher bearing capacity for the precast joints, and the peak load can be increased by 42.9% when the axial compression ratio is increased from 0.2 to 0.4. In contrast, the stiffness degradation will be accelerated with the increase in the axial compression ratio. From yield load to peak load, the stiffness of the precast joint with the largest axial compression ratio decreases by 46.0%, while the joint with the smallest axial compression ratio is only 36.4%. The damage index model adopted in this paper can accurately reflect the damage characteristics of the precast joints. The presented damage states based on the damage index calculation can accurately reflect the joint’s damage characteristics according to different stages. The paper realizes the quantitative damage evaluation for this kind of joint and provides a theoretical basis and method for further studies.

Visualization and evaluation of concrete damage in-service headworks by X-ray CT and non-destructive inspection methods

The durability of concrete irrigation infrastructures decreases easily due to environmental effects, such as the freeze-thawed process. The degree of damage in concrete is, in most cases, evaluated by an unconfined compression test or ultrasonic test. The limiting states of structures are the result of a gradual accumulation of microdefects during the in-service period, which leads to the initiation and development of macroscopic cracks, loss of performance, and Failure. Therefore, to maintain such structures, it is necessary to properly determine the degree of damage by non-destructive testing. For effective maintenance and management of in-service structures, it is necessary to evaluate not only such mechanical properties as strength but also the degree of damage. Quantitative damage evaluation of concrete is proposed by applying the X-ray CT method and related non-destructive inspection, which is based on estimating cracking damage effects. In this study, the damage evaluation of concrete-core samples is investigated by X-ray CT parameters and the non-destructive parameters. The samples tested were taken from the damaged concrete headwork in Niigata, Japan, about 50 years after its construction. The geometric properties of the concrete matrix were calculated by the analysis of X-ray CT images. After X-ray CT measurement, an ultrasonic test and a resonant frequency test were performed. The dynamic modulus of elasticity was calculated by using ultrasonic pulse velocity and resonant frequency. As a result, based on the relationship between the geometric properties of the concrete matrix and the non-destructive parameters, the accumulation of damage in the samples tested was correlated with the ratio of the total area of cracks to the observation area. In damaged conditions, the high value of its ratio is demonstrated under low ultrasonic pulse velocity. These results suggest that the most sensitive components in a concrete body to the degree of damage are cracks in mortar and the ratio of the total area of cracks to the observation area. These values are affected by the internal actual cracks. Thus, the damage of concrete could be quantitatively evaluated by the geometric properties of the concrete matrix and its physical properties.

Quantitative assessment of damage evolution of carbon fiber‐reinforced polymer laminates embedded with different nanoparticles using acoustic emission

AbstractNanoparticles can be used to optimize the interface structure and improve the mechanical properties of fiber‐reinforced composites. Herein, the damage evolution of carbon fiber‐reinforced composites embedded with different nanoparticles has been investigated using acoustic emission (AE) technology. Principal component analysis is used to reduce the dimension of AE parameters. Combining fuzzy C‐means (FCM) clustering with hierarchical clustering can improve the clustering quality, which is helpful to apply FCM results to the quantitative evaluation of damage. For multiwalled carbon nanotubes, graphene, and cellulose nanofibres, the cumulative AE energy of matrix cracking increases by 39.29%, 51.12%, and 53.69%, respectively, that of fiber/matrix debonding increases by 13.5%, 118.5% and 56.58%, and that of fiber breakage decreases by 97.9%, 94.32%, and 87.6%, respectively. The addition of nanoparticles helps to improve the stiffness and toughness of epoxy resin, and restricts crack propagation, thus enhancing the interfacial properties. AE technology can successfully help quantitatively evaluate the damage behavior and the mechanism of different multiscale reinforced composites. AE characteristic responses within the composites are consistent with scanning electron microscopy results.

Quantitative evaluation of damage to retinal capillaries caused by half-dose and half-time photodynamic therapy with optical coherent tomographic angiography

Purpose: To determine and compare retinal capillaries damage in patients with chronic central serous chorioretinopathy (CSC) after half-dose and half-time photodynamic therapy (PDT) based on optical coherence tomographic angiography (OCTA). Methods: Thirty-three eyes of 33 patients and 32 eyes of 32 patients with active CSC were treated with half-dose PDT and half-time PDT respectively and followed up for 3 months. Fundus fluorescein angiography (FFA) and indocyanine green angiography (ICGA) were performed at baseline; best corrected visual acuity (BCVA) and OCTA were performed at baseline, 3-day, 7-day, 14-day, 1-month, and 3-month follow-up visits. Results: Twenty-eight patients in half-dose group and 27 patients in the half-time group finished the 3-month follow-up visits. BCVA was significantly improved at post-PDT 1 month and 3 months (both P < 0.05) and central macula thickness (CMT) was significantly decreased at all post-PDT time points (all P < 0.05) in both groups. 22 patients (78.57%) in the half-dose group and 21 patients (77.78%) in the half-time group had complete absorption of subretinal fluid (SRF) at post-PDT 3 months. There was no significant difference in the above-mentioned prognostic measurements between two groups. The mean vessel densities of superficial retina (VDSR) and inner retina (VDIR) layers were decreased at all post-PDT time points in both groups. However, the decrease of VDIR and VDSR at post-PDT 3 days and 3 months in the half-dose group was much severe than those in the half-time group (P < 0.05). Conclusions: Both half-dose and half-time modifications of PDT were similarly effective in improving the BCVA and decreasing the SRF for chronic CSC. However, the retinal capillaries damage in the half-time group was less severe than that in the half-dose group, so the half-time modification may be a more appropriate parameter for patients with chronic CSC.

Quantitative damage evaluation of LY225 steel under monotonic tensile loading based on acoustic emission entropy

The purpose of this paper is to establish a quantitative relationship between acoustic emission (AE) signal acquisition data and the void growth model (VGM) of low yield point steel LY225 under monotonic tensile loading, to realize the real-time evaluation of plastic damage and the prediction of ductile fracture of materials. A series of material tests, finite element analysis and scanning electron microscope (SEM) tests were carried out to calibrate the VGM fracture parameters of LY225 steel, including the characteristic length l∗ and the critical value of VGI. It is found that the AE signals under monotonic tensile loading have a prominent two-stage characteristic and the cumulative AE entropy of AE signals in LY225 steel is proportional to the volume of material involved with plastic deformation. Then a bilinear function model between cumulative AE entropy and void growth index VGI is proposed and calibrated with monotonic tensile test results from a set of standard rectangular section specimens, and the effectiveness of the proposed model in more complex stress and strain state is verified by tests of a set of variable cross-section flat specimens.

Guided wave-hidden Markov model for on-line crack evaluation of a full-scale aircraft

Structural Health Monitoring (SHM) has been embraced as an effective method in the aerospace industry. However, one of the main problems hindering SHM for practical usages is the reliable evaluation of damages under in-service conditions that introduce various uncertainties and difficulties for effectively interpreting SHM signals. The Hidden Markov Model (HMM)-based method has been proved to be potential for improving the reliability of damage evaluation under in-service conditions since it can explicitly model the uncertainties during damage state transition and SHM process. Nonetheless, traditional HMM-based damage evaluation methods need sufficient prior data of several discrete damage levels for training. These prior data come from historical experiments or simulations, which may not be sufficient for a new target structure. In this paper, a Guided Wave (GW)-health HMM damage evaluation method with an on-line calibration strategy is proposed to realize quantitative evaluation of damage propagation under time-varying conditions. At the off-line stage, a health HMM is constructed with the prior GW-SHM data collected under the structural healthy state. The likelihood probability of the on-line monitored GW-SHM feature sequence belonging to the prior health HMM is calculated for detecting the damage initiation. A new health-HMM is trained with the health data from the current target structure once the damage is found, replacing the prior health HMM. On this basis, a calibration strategy is conducted to calibrate the likelihood probability to the damage size for quantitative damage evaluation under time-varying conditions in real-time. Finally, the proposed method is validated on the fatigue test of a full-scale aircraft under an actual flight spectrum. The fatigue crack is reliably evaluated, and the maximum error of the evaluated crack length is 0.5 mm.

Collapse analysis and damage evaluation of typical simply supported double-pier RC bridge under truck collision

Vehicular collision with bridge pier can cause significant structural damage, and even partial or full collapse of the bridge, while the existing work mainly focuses on the vehicular-impact resistance of bridge pier. This study aims to study the collapse process and damage levels of the bridge collided by medium and heavy trucks. Firstly, the refined finite element (FE) model of the typical simply supported two-span double-pier RC bridge is established via the commercial software LS-DYNA, and the adopted material constitutive models and numerical algorithms are validated by comparing with the prototype truck-pier collision test and full-scale drop-weight test on RC beam. Then, the validated FE model is employed to numerically reproduce the actual heavy truck collision accident involving the collapse of bridge, and the dynamic behaviors and collapse process of the collided bridge are analyzed. It shows that, for the present bridge type: (i) truck-bridge collision is not a single impact but a successive impact with bumper, engine and cargo, in which the shear failure on the reinforced concrete bridge pier (RCBP) induced by the engine impact is the dominant factor resulting in the bridge collapse; (ii) during the collision process, the impacted RCBP firstly suffers shear failure at the bottom, and followed by the inclination of the bent cap and superstructure, and then the neighboring RCBP sustains flexible-shear failure at the top, which eventually results in the instability of substructure and collapse of superstructure. Furthermore, a total of 35 truck-bridge collision cases are numerically simulated and assessed, and a new quantitative damage evaluation approach, including the modified damage index, damage level and evaluation criterion, is proposed for the bridge under vehicular collision. In addition, the relationship between the damage level of the collided bridge and superstructure inclination is clarified, which is beneficial for bridge engineers to feasibly assess the vehicular-impact resistance of bridge.

Deep Neural Networks for Quantitative Damage Evaluation of Building Losses Using Aerial Oblique Images: Case Study on the Great Wall (China)

Automated damage evaluation is of great importance in the maintenance and preservation of heritage structures. Damage investigation of large cultural buildings is time-consuming and labor-intensive, meaning that many buildings are not repaired in a timely manner. Additionally, some buildings in harsh environments are impossible to reach, increasing the difficulty of damage investigation. Oblique images facilitate damage detection in large buildings, yet quantitative damage information, such as area or volume, is difficult to generate. In this paper, we propose a method for quantitative damage evaluation of large heritage buildings in wild areas with repetitive structures based on drone images. Unlike existing methods that focus on building surfaces, we study the damage of building components and extract hidden linear symmetry information, which is useful for localizing missing parts in architectural restoration. First, we reconstruct a 3D mesh model based on the photogrammetric method using high-resolution oblique images captured by drone. Second, we extract 3D objects by applying advanced deep learning methods to the images and projecting the 2D object segmentation results to 3D mesh models. For accurate 2D object extraction, we propose an edge-enhanced method to improve the segmentation accuracy of object edges. 3D object fragments from multiple views are integrated to build complete individual objects according to the geometric features. Third, the damage condition of objects is estimated in 3D space by calculating the volume reduction. To obtain the damage condition of an entire building, we define the damage degree in three levels: no or slight damage, moderate damage and severe damage, and then collect statistics on the number of damaged objects at each level. Finally, through an analysis of the building structure, we extract the linear symmetry surface from the remaining damaged objects and use the symmetry surface to localize the positions of missing objects. This procedure was tested and validated in a case study (the Jiankou Great Wall in China). The experimental results show that in terms of segmentation accuracy, our method obtains results of 93.23% mAP and 84.21% mIoU on oblique images and 72.45% mIoU on the 3D mesh model. Moreover, the proposed method shows effectiveness in performing damage assessment of objects and missing part localization.

Assessing condition of concrete affected by internal swelling reactions (ISR) through the Damage Rating Index (DRI)

The Damage Rating Index (DRI) has been increasingly used in North America since it answers interesting questions on the cause and extent of damage in concrete. Currently, the DRI is mostly used to appraise alkali-silica reaction (ASR) affected concrete and there is very few research on the evaluation of other distress mechanisms through this method. This work presents the use of the DRI to assess condition of concrete affected by internal swelling reaction (ISR) mechanisms (i.e. ASR, delayed ettringite formation and freeze-thaw cycles), with the aim of verifying the suitability of the method to become a comprehensive damage evaluation protocol. Results show that the DRI number, represented by plots or bar charts, enables a quantitative damage evaluation of concrete regardless of the distress mechanism type and degree. Moreover, the implementation of an extended DRI version makes the method more suitable to describe progress of different ISR deterioration processes in concrete.

Damage estimation of concrete canal due to freeze and thawed effects by acoustic emission and X-ray CT methods

As a detailed inspection of a concrete structure in service, core samples are usually drilled out and then mechanical properties are measured. In this study, damage estimation of freeze-thawed concrete from concrete-core samples is developed, applying acoustic emission (AE) method. By the authors, the quantitative damage evaluation of concrete has been proposed in RILEM TC −212ACD, by applying AE and damage mechanics in the compression test. In this study, characteristics evaluation of cracking damage effects in concrete mechanical properties by detected AE energy in core test. Prior to the compression test, distribution of micro-cracks in a concrete-core sample is inspected by helical X-ray computer tomography (CT). Then, freeze-thawed damaged samples are tested by the compression test with AE monitoring. Concrete-core samples were taken out of a channel structures in Hokkaido, Japan which is extremely developed cracking damage. Thus, it is demonstrated that the concentration of material damage could be evaluated by comparing geometrical characteristics of cracks with the “energy rate” of AE generation, which is analyzed by AE parameter analysis. A relation between AE energy rate, physical properties and geometrical properties of void is correlated, and thus the damage of concrete is qualitatively estimated by AE parameters.

A spherical smart aggregate sensor based electro-mechanical impedance method for quantitative damage evaluation of concrete

In this study, the concrete damage induced by compression is evaluated quantitatively using spherical smart aggregate sensor based on electro-mechanical impedance method. The sensitivity of the spherical smart aggregate sensor embedded in concrete cubes is investigated by comparing the electrical signals recorded during the compressive process with those of the smart aggregate sensor embedded in concrete cubes. Furthermore, the finite element model of concrete cube with an embedded spherical smart aggregate sensor is developed to simulate the concrete compressive tests. The concrete damaged plasticity constitutive model is utilized to simulate the concrete damage process. The numerical model is verified with the experimentally measured compressive test results. Finally, the damage volume ratio is presented to quantify the damage level of concrete based on the numerical model. The relationship between the root mean square deviation index of the conductance signatures obtained from experiments and the damage volume ratio computed by numerical simulation is established to quantify the concrete damage level. The results show that the spherical smart aggregate sensor is more sensitive than the smart aggregate sensor in monitoring the three-dimensional concrete structures. The proposed empirical fitting curve can effectively evaluate the concrete damage level quantitatively.