Damage Location Identification Research Articles

Research and Evaluation of a New Structural Damage Identification Method Based on a Refined Genetic Algorithm

In order to solve the problem of structural damage location and degree identification, the weighted mean of vectors algorithm (INFO), a high-performance optimization algorithm, was first introduced to identify structural damage. By comparison with the refined genetic algorithm (RGA), the accuracy and advantages of INFO are analyzed and evaluated. An objective function is constructed by combining the dynamic response transfer ratio without modal analysis. The INFO and RGA algorithms are used to optimize the objective function for damage identification. The simulation results verify the effectiveness of the three damage identification methods. The results show that the identification effect of the INFO algorithm can reach nearly 100% without noise influence, and the anti-noise ability is the strongest. Among the three algorithms, the damage identification accuracy of the INFO algorithm is the highest, followed by the RGA algorithm and the GA algorithm.

Computer vision-based substructure isolation method for localized damage identification

In recent years, vision-based structural damage identification techniques have garnered significant attention due to their simplicity and cost-effectiveness. Nevertheless, dynamic response-based vision methods face challenges related to the limitations of the field of view (FOV), i.e., the extent of the FOV is often sacrificed in order to ensure sufficient monitoring accuracy, which reduces the amount of data available for structural health monitoring (SHM). This study presents a solution to this problem by employing the substructure isolation method (SIM), which focuses on local vibrations of the structure and enables local damage identification by isolating the area of interest. Additionally, a method combining kernelized correlation filter (KCF) with sub-pixel template matching is used to extract vibration information recorded by visual sensor. This strategy balances both the efficiency and accuracy of displacement extraction. In numerical simulations, the performance of the SIM based on acceleration response and displacement response is compared, demonstrating the potential compatibility of visual sensors with the SIM. Finally, the effectiveness of the proposed vision-based local damage identification method is validated through vibration testing of a shear frame model.

A vine-copulas based multi-sensor fusion structural damage monitoring method and its application in dam engineering

As a civil engineer that is susceptible to the mechanism of slow and continuous deterioration, the dam must detect damage according to the static data of the arranged sensors, which is an important issue in structural health monitoring. Due to the characteristics of huge solid structures, damage with a slight degree and small range usually does not cause significant changes in the monitoring data of a single sensor. Therefore, it is difficult to effectively detect local damage by traditional monitoring methods that establish operating warning values for a single sensor. Currently, there have been some approaches to fuse sensor information that consider correlations between multiple sensors as well as the overall operational behavior of the dam. However, their objectives are focused on improving the accuracy of response variable prediction models without addressing the local damage identification aspect. In this study, we apply the Vine-Copula model for the first time in a multi-sensor information fusion dam damage detection method based on static monitoring data. In this way, it improves the sensitivity of local damage identification for huge solid structures. A prediction model is fitted with the measuring data of each sensor to obtain the residual series. Then the joint probability distribution of the multidimensional residuals is acquired by constructing the Vine-Copula model. Finally, the joint cumulative distribution function value is used as an alarm limit to determine whether the structure has an abnormal damage condition. The effectiveness and accuracy of the proposed method were demonstrated in both a numerical arch dam simulating local damage and an actual rockfill dam engineering with cracks at the crest.

Identification of the damage location for the structural sealant based on deep learning

Structural sealants are essential to maintain the safety of panel units of hidden frame glass curtain wall. Damage localization of the concealed structural sealant is still difficult because of the unknown baseline model. In this paper, a convolutional neural network-based identification method is proposed to localize the damage of structural sealants without the baseline model. The method develops a novel input of the convolutional neural network (CNN), multi-symmetry-point images (MSPI), which is encoded by the response of four symmetrical points to impulse excitations. The CNN would identify the damage location by discerning differences among four images. Then, a dataset with 28803 samples, which considers the effect of the multiple damages and noise, was used to train the CNN. Three types of transformed images and four CNN models were compared to optimize the input signals and the configuration of the CNN. A series of numerical examples indicated that the Gram angle difference field is the optimal image transformation method, and improved-DenseNet121 is the optimal CNN for damage localization in structural sealants. Then, several laboratory experiments validate the effectiveness of the optimized image input and CNN with an accuracy rate of 92 % in the identification of damage location.

Piezoelectric Impedance-based Structural Damage Identification Empowered with Tunable Circuitry Integration and Multi-objective Optimization based Inverse Analysis

Abstract The piezoelectric impedance-based technique is increasingly recognized for its promise in structural health monitoring and damage identification. Built upon their self-sensing actuation capability, piezoelectric transducers can be integrated to host structures to acquire the system-level impedance information in high frequency range with small wavelength. Furthermore, the frequency-sweeping harmonic excitations in impedance measurements lead to the potential for model-based inverse identification of damage location and severity. A major challenge in damage identification, however, is that the inverse analysis is generally underdetermined, as the measurement information may not be adequate to yield a unique solution. In this research, a new methodology of tunable sensing in conjunction with multi-objective optimization inverse analysis is established. Taking advantage of the two-way electromechanical coupling of piezoelectric transducers, tunable inductance is integrated into the measurement circuit. For the same damage scenario, by tuning the inductance to a series of values, a family of impedance measurements can be acquired. Meanwhile, the inverse analysis is cast into a multi-objective optimization problem, aiming at minimizing the difference between measurement and model prediction and achieving sparsity in damage index vector. A Q-learning based multi-objective particle swarm optimization is synthesized to reach a small yet diverse solution set. We report the circuitry integration details as well as the algorithm enhancement with systematic case investigations. It is validated that the new methodology with enriched measurement can produce smaller solution set encompassing the true damage scenario, thereby providing vital information for diagnoses and prognosis.

Vibration-based methods for local damage identification of breathing cracks in truss-like structures

A vibration-based local damage identification method is proposed for truss-like structures with breathing cracks. Firstly, a bilinear model is established to represent the characteristics of breathing cracks, and the features of such cracks are detected through reciprocity and homogeneity tests on structural input-output signals. Since the crack is influenced by tensional and compressive forces on truss members, only one damage factor is required to quantify the stiffness reduction of each member. Subsequently, a dynamic equilibrium equation concerning the damage factor is constructed to identify the new load term. The integral equation method, which relies on the virtual work principle, is employed to obtain the displacement responses and loads under all tensile conditions. Finally, the mean value of cumulative damage coefficients for each truss member can be derived as their corresponding damage factor. Numerical examples involving a plane truss structure and a truss offshore platform are utilised to illustrate the effectiveness of this damage identification method. The results demonstrate exceptional precision and efficiency in accurately identifying various damages in truss structures under different excitations and signal-to-noise ratios. A actual space truss structure confirm that this damage identification method exhibits high-level precision in identifying single-member damages as well as multi-member damages with breathing cracks locally.

A data fusion-based approach for structural damage detection with distributed long-gauge strain measurements

Distributed long gauge strain sensing technology has solved the problem of difficult identification of local damage in traditional ‘point’ monitoring, and has received extensive attention in the field of structural damage identification. Owing to the inevitable presence of measurement noise and environmental factors in the macro strain response measurement, a single damage index has also underlined some drawbacks generally arising when multiple damages occur, or errors affect the identified dynamic properties of the systems. To address these challenges, this paper proposes a data fusion method based on the Dempster–Shafer evidence theory, relying on distributed strain sensing technology. The identification results of the modal macro strain-based index and quasi-static macro strain energy-based damage index are fused to make a comprehensive decision on structural damage location. Damage identification studies are conducted on different types of structures under impact loads and random wind loads to verify the effectiveness and accuracy of the proposed data fusion method in the case of single and multiple damage conditions. The results show that the proposed data fusion method can accurately identify the damage location and effectively reduce misjudgment on undamaged locations; it has potential application value in practical structural health monitoring.

Piezoelectric impedance-based high-accuracy damage identification using sparsity conscious multi-objective optimization inverse analysis

Two elements are essential in structural health monitoring utilizing dynamic responses: response measurement with high-frequency contents, i.e., small characteristic wavelengths, that can adequately reflect damage features, and effective inverse identification analysis that is however oftentimes under-determined. The advancement of smart structure integration has led to active interrogation through frequency-sweeping piezoelectric impedance measurement at high frequency range. In this research we develop a multi-objective optimization formulation for the identification of damage location and severity utilizing piezoelectric impedance. While one optimization objective is to match the response measurement with finite element model prediction in the damage parametric space, the other is the number of locations of damage, i.e., the sparsity of damage index as the solution vector, since damage usually occurs within a small number of locations. This multi-objective formulation fits well the under-determined nature of damage identification, as it naturally provides multiple solutions as basis for further elucidation. The challenge remaining is how to find a small solution set that can include the actual damage scenario. Here we develop a novel inverse identification framework utilizing the intelligent swarm optimizer which possesses flexibility for enhancement. We first embed a sparsity enforcement process into the population generation of the optimizer, which yields a solution repository intrinsically possessing sparsity. We then apply reinforcement learning so the agents can adaptively opt for local strategies with the aim of enriching the searching patterns to diversify the solutions. Through the incorporation of a Q-table, searching toward more promising directions will be rewarded. Our case analyses employing experimental data indicate that this sparsity-conscious multi-objective particle swarm optimization technique can lead to a small solution set which generally encompasses the true damage scenario. This effectively solves the structural damage identification problem with piezoelectric impedance measurement.

Physics-guided diagnosis framework for bridge health monitoring using raw vehicle accelerations

Damage detection of bridges using vibrations from a passing vehicle has received a lot of interest recently. Though non-modal parameter-based methods (e.g., data-driven approaches) have shown promising results in this context, their advancement towards a comprehensive and rigorous monitoring system is hampered by their overreliance on machine learning techniques. On this background, this paper proposes a novel automatic physics-guided diagnosis framework for bridge health monitoring utilizing only raw vehicle accelerations. First, numerical studies are conducted to investigate the relationship between vehicle time-domain signals and bridge damage, based on which a new damage index is proposed. At the same time, it also explores the identification of damage locations and proposes a location index. Second, a damage diagnosis framework, which consists of a data processing method and a physics-guided model, is designed to overcome deficiencies from a drive-by measurement and to automate the damage detection process. The proposed framework was validated using datasets acquired from laboratory experiments employing a scale vehicle model and a steel beam. The results affirmed the method's efficacy in damage indication, quantification, and localization. Moreover, the superiority of the proposed damage index and the rationale for the proposed physics-guided approach were also demonstrated through comparisons with machine learning-based methods.

Spacecraft Segment Damage Identification Method Based on Fiber Optic Strain Difference Field Reconstruction and Norm Calculation

Real-time online identification of spacecraft segment damage is of great significance for realizing spacecraft structural health monitoring and life prediction. In this paper, a damage response characteristic field inversion algorithm based on the differential reconstruction of strain response is proposed to solve the problem of not being able to recognize the small damages of spacecraft structure directly by the strain response alone. Four crack damage location identification methods based on vector norm computation are proposed, which realize online identification and precise location of structural damage events without external excitation by means of spacecraft structural working loads only. A spacecraft segment structural damage monitoring system based on fiber optic grating sensors was constructed, and the average error of damage localization based on the curvature vector 2 norm calculation was 2.58 mm, and the root-mean-square error was 1.98 mm. The results show that the method has superior engineering applicability for on-orbit service environments.

Experimental study on fatigue damage of continuous steel–concrete composite beam by acoustic emission

To investigate the developing process and mechanism of internal fatigue damage of continuous steel–concrete composite (CSCC) beams, the fatigue test of two-span CSCC beams under cyclic loading was conducted, and the acoustic emission (AE) testing method was used to monitor the fatigue damage accumulation during loading. Internal AE propagation characteristics and residual deflection were compared to analyze the overall damage process as well as the boundary points of each fatigue stage. The fatigue failure mechanism of CSCC beams was deduced from an internal damage point of view. The fuzzy C-means algorithm was used to cluster the AE signals and extract the signals of the concrete structure. According to the energy accumulation-release signal of the fatigue crack tip, a novel method for fatigue damage location identification of the concrete structure was proposed, and the propagation characteristics of the fatigue crack along with the variation law of the AE wave velocity within the concrete slab were further investigated. Control parameters for fatigue damage, similar to the relationship between magnitude and frequency of earthquakes, were calculated, and the warning signs of fatigue failure of CSCC beams were inferred. The AE cumulative energy-based fatigue damage model was proposed to predict the damage status and fatigue life of the composite beam. Results have shown that the AE detection method is capable of continuously and accurately monitoring the fatigue damage within the composite beams in real-time as opposed to inspections that are solely based on appearance. The fatigue damage energy model also confirmed the fact that the fatigue damage growth follows an exponential function. The fatigue damage energy model and the AE method proposed based on AE attributes provided a more feasible and stringent method for fatigue damage prediction and analysis for CSCC beams.

Exploiting evanescent bending waves for the identification of near-boundary cracks in slender beams

Bending evanescent waves are among the promising approaches for the local damage identification of slender beams due to the high sensitivity and spatial concentrating nature. However, such local damage usually appears near the boundary of the beam in practices. It is therefore difficult to distinguish the evanescent wave components resulting from the boundary and the crack, leading to confusion in damage identification. In order to tackle this issue, the generalized reflection coefficient of the evanescent wave with respect to the incident propagating wave (GRC-EP) is exploited in the present work, which allows to quantitatively identify the severity of the near-boundary crack. Taking the semi-infinite Timoshenko beam having a crack damage at the fixed end, the generalized reflections of the evanescent and propagating waves at the crack, with respect to a unit incident wave, are derived analytically, showing that the amplitude of the evanescent wave is negatively related to the damage severity. Based on this, the amplitude ratio between the reflected evanescent wave and the incident propagating wave, i.e., the GRC as mentioned, is proposed to be the damage indicator. Referring to the theoretical GRC values, the damage severity can then be identified from the estimated GRCs that are obtained from the inverse algorithm, as long as the acceleration responses are given. Finally, the feasibility of the proposed indicator is verified by taking a slender beam having a local damage at the fixed end as an example. The results show that the proposed GRC-EP is capable to indicate the severity of the near-boundary damage despite the consideration of noise, confirming its potential value in the health monitoring of slender beam structures.

Mode conversion of Lamb waves in a composite phononic crystal plate: Numerical analysis and experimental validation

The mode manipulation of Lamb waves plays an important role in damage detection and identification of damage types, location, and size. In this paper, we propose a composite phononic crystal (PC) plate with antisymmetric and symmetric PCs for realizing mode conversion from A0 to S0 mode of Lamb waves. The theoretical analysis, numerical simulations, and experimental validation are introduced and the mode conversion mechanism of the composite PC plate is systematically investigated. The effect of geometrical parameters on band structures of antisymmetric and symmetric PCs is first discussed. Then multi-physics field simulation models are developed and in-plane displacement fields are obtained in numerical simulations, which shows that the mode conversion is enhanced when the period number of the antisymmetric PC decreases and that of symmetric the PC increases. The composite PC plate specimens are fabricated with precision wire cutting technology for experimental measurements and the self-designed polyvinylidene fluoride (PVDF) comb transducer is used to stimulate the Lamb waves. The experimental results are consistent with the numerical simulations, which demonstrate that the proposed composite PC plate can achieve the mode conversion from A0 to S0 mode of Lamb waves. Our proposed structures have applicable values for the mode manipulation of Lamb waves in damage detection.

Damage shape recognition algorithm of composite woven fabric plate based on guided waves

Modern lightweight woven fabric composite structures play an important role in industrial field. The occurrence and accumulation of structure damages can threaten the integrity of in-service structures. With the high damage sensitivity, guided wave-based structural health monitoring has been regarded as the effective tool for damage detection. In this study, a damage shape recognition algorithm has been developed to continuously identify the damage extent in composite woven fabric structures, which is necessary for further analysis of remaining life estimation. The proposed algorithm is established relying on the accurate guided wave propagation information and precise damage scattered wave sources identification, and consists of the following stages: guided wave velocity calculation, guided wave signals collection, damage location identification, extraction of time of flight (TOF), damage boundary estimation, noise points filtering and damage shape and size recognition. The semi-analytical finite element method is presented to model guided wave propagation in multi-ply woven fabric panels. The numerical predictions are experimentally validated using the novel omnidirectional wave velocity measurement of guided wave collected by mobile PZT transducer. The precise TOF extraction and noise point elimination method are first proposed for improving effectiveness of damage identification. The approach was validated by several cases of damage at arbitrary positions. The estimated numerical and experimental results show the potential of the proposed algorithm in identifying various damage features in anisotropic composite plates, such as its location, shape and size, and achieve good agreement with the actual damage.

Damage identification of CFRP laminate based on finite element analysis and FBG sensor network

Carbon fiber reinforced polymer (CFRP) is broadly used in the defense and aerospace industries due to its superior performance, and the real-time monitoring of its structural damage can enhance the reliability and safety of utilization. This paper is aimed at collecting the surface strain of CFRP laminates in real-time using FBG sensor network and constructing a structural damage identification model based on strain characteristics to identify the location of hole damage and the length and direction of crack damage. To investigate the impact of damage on the surface strain of CFRP laminates, this research conducted an extensive finite element analysis of laminates without damage and with various degrees of damage, combined with the genetic algorithm and linear regression to identify and optimize the parameters in the damage identification model. Then two CFRP laminates with comparable properties were fabricated, and the FBG sensor network was arranged on the surface to verify the efficiency of the damage identification model through experiments. Results of experiments show that the damage identification model proposed in this paper can accomplish accurate identification of hole damage location and crack damage length and direction within the error range, also proving the feasibility and effectiveness of combining finite element analysis, FBG sensor network, and intelligent algorithm to establish CFRP structural damage identification system proposed in this paper.

Lamb wave-based damage assessment for CFRP composite structures using a CHMM-based damage localization algorithm and a damage quantitative expression

Accurately and quantitatively monitoring the damages in composite structures is very challenging, but important for assessing their integrity. In this paper, an innovative damage location identification and size quantification assessment method based on Lamb wave is proposed for composite laminates. A continuous hidden Markov model (CHMM)-based damage scaling factor together with a weighted average imaging algorithm is adopted in damage location identification. To quantify the degree of structural deviation from the healthy state, the inconspicuous change of guided wave (GW) signal is converted to a CHMM-based damage scaling factor with significant change. Based on the damage scaling factors of all GW sensing paths, a weighted average imaging algorithm is performed to identify the most probable damage location. In addition, a damage quantitative mathematical expression is proposed to approximately estimate the size of circular-shaped damage founded on several different damage-related characterizations. The implementation of the proposed quantitative methodology is comparatively simple and straightforward for damage detection. Experiments on three composite panels with multiple damage cases are conducted to substantiate the proposed method. The sensor layers with embedded piezoelectric transducer (PZT) sensor networks surf-mounted on the structures are employed to excite and receive Lamb wave signals since their superiority of flexibility, electrical stability and apt for complex structures. The results demonstrate that the proposed algorithm is capable of pinpointing both the location and size of the circular-hole damage in composite structures.

Damage Identification of Semi-Rigid Joints in Frame Structures Based on Additional Virtual Mass Method.

In civil engineering, the joints of structures are complex, and their damage is generally hard to be detected. Due to the insensitivity of structural modal information to local joint damage, this paper presents a method based on additional virtual mass for damage identification of a semi-rigid joint in a frame structure. Firstly, the modeling of a semi-rigid is described. Secondly, the frequency response of the virtual structure is constructed, and the natural frequency of the constructed virtual structure is extracted by the ERA method. By adding multiple values of virtual masses at different positions, the natural frequency information sensitive to joint damage for damage identification is effectively increased. Based on the above theory, qualitative identification of joint damage is proposed to detect the potential damage, and identification of both damage location and its extent is presented, using natural frequency. Improved Orthogonal Matching Pursuit (IOMP) algorithm is employed to improve the accuracy of the natural frequency-based method for damage identification. At last, numerical simulation of a three-story frame is performed to discuss and to verify the effectiveness of the proposed method.

TECHNIQUE TO DETERMINE DEPTH OF DEFECTS IN CONCRETE USING INFRARED THERMOGRAPHY – A SIMULATION STUDY

The cracks and damages in concrete can occur during the construction, placement, curing or any time during the service life of the structure. Cracks and damages can indicate concrete deterioration. Structural engineers are interested in the identification of damage location and crack depth measurement. The crack and damage depth measurement can help contractor in evaluating the repair costs. There are different methods to measure the crack depth like Impact-Echo Method, Ultrasonic Pulse Velocity (UPV) which can be employed where location of the cracks and damages are accessible. However, sometimes location of crack and damage in the concrete is inaccessible and this requires non contact measurement methods for identifying damages. Infrared thermography can be effectively used in identifying subsurface cracks and damages in such cases. The research work reported in this study is to identify depth of sub-surface defects located in concrete structures. The depth of damage is identified by taking infrared images of concrete surface. A pseudo experiment of thermal imaging was conducted on concrete surface using FEM simulations. A concrete slab model having inbuilt defects of known thicknesses and at known depths from the surface is used. The surface of slab is subjected to thermal excitation of varying amplitude and time period. The temperature contours on vertical sections passing through the defects are used to accurately determine the depth of damage. Using this technique depth of defect could be determined with a least amount of error up to 2% of actual depth. The results of simulation study are reported.

TECHNIQUE TO DETERMINE DEPTH OF DEFECTS IN CONCRETE USING INFRARED THERMOGRAPHY – A SIMULATION STUDY

The cracks and damages in concrete can occur during the construction, placement, curing or any time during the service life of the structure. Cracks and damages can indicate concrete deterioration. Structural engineers are interested in the identification of damage location and crack depth measurement. The crack and damage depth measurement can help contractor in evaluating the repair costs. There are different methods to measure the crack depth like Impact-Echo Method, Ultrasonic Pulse Velocity (UPV) which can be employed where location of the cracks and damages are accessible. However, sometimes location of crack and damage in the concrete is inaccessible and this requires non contact measurement methods for identifying damages. Infrared thermography can be effectively used in identifying subsurface cracks and damages in such cases. The research work reported in this study is to identify depth of sub-surface defects located in concrete structures. The depth of damage is identified by taking infrared images of concrete surface. A pseudo experiment of thermal imaging was conducted on concrete surface using FEM simulations. A concrete slab model having inbuilt defects of known thicknesses and at known depths from the surface is used. The surface of slab is subjected to thermal excitation of varying amplitude and time period. The temperature contours on vertical sections passing through the defects are used to accurately determine the depth of damage. Using this technique depth of defect could be determined with a least amount of error up to 2% of actual depth. The results of simulation study are reported.

Effect of Embedded Thin-Plies on the Charpy Impact Properties of CFRP Composites.

In this study, different configurations of epoxy composite laminates that contained thin plies were prepared and characterised for sudden impact load bearing applications. The primary aim of this investigation was to develop a hybrid epoxy-based thin ply composite for aerospace and automotive applications that would be tolerant of high impacts. The impact properties of the selected configurations were investigated both experimentally and numerically under low-velocity Charpy impact loading conditions. Furthermore, any damage to the laminates was evaluated with an emphasis on the identification of dominant damage mechanisms and locations. This included a comparison between the laminates that were made from traditional plies and the thin ply laminates in terms of their absorbed energy and failure modes. The results revealed that the integration of thin plies into normal ply had a major effect on the amount of absorbed energy under flatwise conditions: up to 8.7 J at a cut-off angle of 90°. However, edgewise conditions produced a maximum observed energy of 10.0 J for the thin plies that were surrounded by normal plies (Plate 3). The damage assessments showed the increased damage resistance of the hybrid thin ply composites due to their uniform stress distribution. The traditional ply composites incurred large deformations from the impact loads. Moreover, it was noted that delamination formed in the middle regions of the traditional plies. The FEM model analysis revealed that it was capable of accurately predicting the absorbed energy for different configurations of composites, which were prepared and analysed experimentally. Both the experimental and numerical values were very similar to each other. These impact damage assessments improved the thin ply composites so that they could be used as working materials for applications that are prone to high loads, such as the aerospace, defence, automotive and structural industries.