Damage Identification Method Research Articles

A Structural Damage Identification Method Based on Arrangement of the Static Force Residual Vector

In order to effectively and conveniently identify the damage location and damage degree of structural members under static response, a structural damage identification method based on the force residual vector is proposed. The force residual vector is defined by using the static displacement data and the stiffness matrix of the finite element model structure. The structural element corresponding to the non-zero element of the permutation force residual vector is intelligently determined as the damage element. The damage degree of the damaged unit is calculated from the equilibrium equation, which is established by the global stiffness matrix with only the damaged unit. For example, the identification analysis of damage units of numerical models is carried out for a simply supported beam as a simple structure and a truss as a complex structure based on the proposed method. In El Centro seismic wave, the dynamic responses of the original model and truss damage model are simulated and compared by using state space theory to verify the necessity of damage identification.

Implementing Fuzzy-Based Artificial Intelligence Approach for Location of Damage in Structures

Modal parameters are functions of the physical characteristics of a structure and they are very sensitive to damage. Therefore, any alterations in the physical features can change the vibration parameters of a structure. Modal data such as natural frequencies and mode shapes are easy to acquire from the measurements of structural behavior. One method of structural damage identification is to apply natural frequency. Natural frequencies represent the global behaviors of a structure and are not too sensitive when detecting the damage in structures and cannot offer spatial information about structural changes, and thus, their application is considered as challenging. On the other hand, a mode shape is a vibrational deformation of a system and it represents the relative displacement of all parts of a structure and can provide spatial information as well as give a significant indication of the damage occurring in a structure. In this present research, an intelligent hybrid approach, namely adaptive neuro-fuzzy inference system (ANFIS), as a fuzzy-based artificial intelligence approach was developed and applied due to its ability to recognize patterns, strong computational features, and capability of locating defects in a scaled girder bridge using direct modal parameters. The experimental analysis and numerical simulations of a steel girder bridge provided mode shape parameter datasets under different positions and sizes of faults in the structure. The results demonstrated the effectiveness of this method and provided acceptable precision even when the input datasets contained errors or were corrupted with a certain level of noise.

Quasi-Static Influence Line Identification and Damage Identification of Equal-Span Bridges Based on Measured Vehicle-Induced Deflection

The bridge influence line (IL) reflects the response of a certain section due to varying load positions. As a result, IL has a wide application prospect in damage identification and condition assessment. Up to date, studies regarding IL have been focused on the structure condition evaluation. A feasible and practical method for damage identification is still not yet available. The present paper proposes a comprehensive damage identification methodology based on IL under a moving vehicle is composed of data pre-processing, IL extraction, and damage detection. Firstly, a thorough review of existing IL identification methods based on signal processing is provided. Then three quasi-static IL identification methods based on measured data are discussed. Consequently, the study proposes a two-stage damage identification approach for simply supported bridges with equal span length. Also, the effectiveness of this approach is verified through field tests on a real girder bridge. At last, conclusions are drawn, and potential issues for the application of the proposed method in practice are discussed.

A statistical index based damage identification method of a bridge using dynamic displacement under moving vehicle

Potential damage identification is essential to avoid the sudden and premature failure of aging bridges, which have attracted ample attention over the decades. This paper proposes an output only data driven technique for damage identification which will not require any physical model of the structure nor any known input excitation. Statistical moments have been considered as the key feature for the proposed method of damage identification which has been derived from the measured vehicle-induced dynamic displacement responses of a pre-stressed concrete I-girder bridge. The displacement responses data of a bridge is influenced by many factors, including Vehicle-Bridge Interaction (VBI), vehicle speed and road roughness. The interaction between vehicles and bridges includes dynamic models for bridge structure subsystem and vehicle subsystem, interaction constraints and road roughness modelling. Firstly, the coupled formulation is derived based on the interaction constraints. Then, Newmark’s β method is utilized for solving the coupled VBI problem to extract the vehicle-induced displacement responses. Later, the statistical moment is calculated from the Power Spectral Density (PSD) of the displacement response retrieved from sensors positioned on the span of the bridge for both damaged and undamaged conditions. A damage index named Curvature of Statistical Moment Difference (CSMD) is calculated to identify the damages. Artificial damage is incorporated through the reduction of structural stiffness of the bridge element. The results of the study demonstrate the method's capability to identify and locate damage at any position of the bridge span and assess the relative severity of the damage. The effect of varying pavement roughness conditions is also considered in this study.

Performance evaluation of crack identification using density-based topology optimization for experimentally visualized ultrasonic wave propagation

Although techniques to visualize ultrasonic wave propagation help to detect defects, quantitative evaluation using these techniques remains a significant challenge. In this study, a topology-optimization-based damage identification method combined with an ultrasonic wave visualization technique is applied to the experimental results, and its performance is evaluated. This method estimates the target crack as the distribution of ‘damage parameters’ that reproduces the experimentally visualized ultrasonic wave propagation in an inverse analysis model. The objective function of the optimization was the sum of the squared error between the maximum amplitude distribution obtained in the experiment and finite element analysis. Our results suggest that the crack identification results depend on the incidence angle of the ultrasonic wave to the target crack. This is because the low amplitude in the downstream region of the ultrasonic wave to the crack causes low sensitivity of the objective function. Crack identification was performed using the dataset of ultrasonic wave propagation incident from two directions. The crack could be identified with high accuracy provided the sensitivity of the objective function was high in the entire design domain. Based on this mechanism of crack identification, this study proposes two indices to evaluate the validity of crack identification results in actual inspections.

Structure-type classification and flexibility-based detection of earthquake-induced damage in full-scale RC buildings

Detecting early damage in civil structures is highly desirable. In the area of vibration-based damage detection, modal flexibility (MF)-based methods have proven to be promising tools for promptly identifying changes in the global structural behavior. Many of these methods have been developed for specific types of structures, giving rise to different approaches and damage-sensitive features (DSFs). Although structural type classification is an important part of the damage detection process, this part of the process has received little attention in most literature and often relies on the use of a-priori engineering knowledge. Moreover, in general, experimental validations are only performed on small-scale laboratory structures with controlled artificial damage (e.g., imposed stiffness reductions). This paper proposes data-driven criteria for structure-type classification usable in the framework of MF-based damage identification methods to select the most appropriate algorithms and DSFs for detecting and localizing structural anomalies. This paper also tests the applicability of the proposed classification criteria and the damage identification methods on full-scale reinforced concrete (RC) structures that have experienced earthquake-induced damage. The considered structures are a seven-story RC wall building and a five-story RC frame building, which were both tested on the large-scale University of California, San Diego-Network for Earthquake Engineering Simulation (UCSD-NEES) shaking table.

A centrifugal fan blade damage identification method based on the multi-level fusion of vibro-acoustic signals and CNN

A single sensor's blade damage identification is difficult because of the complex noise environment. At the same time, the multi-source signals include complete information of fault characteristics. Aiming to effectively fuse multi-sensor signals and improve identification accuracy, a centrifugal fan blade damage identification method based on the multi-level fusion of vibro-acoustic signals and a one-dimensional convolutional neural network (1D-CNN) is proposed. Firstly, acoustic and vibration signals are fused at the data level respectively by a data-adaptive synchronization weighted fusion algorithm. Secondly, the proposed 1D-CNN network extracts feature from the fused acoustic and vibration signals. Finally, the extracted features are fused by a fully connected layer. The experimental results show that the proposed method achieves 100% recognition accuracy at four speeds. By analyzing different signal to noise ratios (SNR), this method has higher diagnostic accuracy and better robustness compared with single sensor, single-level fusion, and other diagnostic methods.

Baseline-free structural damage identification for plate-like structures based on two-dimensional curvature propagating flexural waves

Waveforms of propagating flexural waves can reveal plentiful information about local anomalies caused by damage, and the local anomalies can be used for damage identification. However, the local anomalies can be masked by the interference of measurement noise and global trends of the waveforms, and they may be able to indicate only fractions of the size and extent of the damage. In this paper, an effective noise-robust damage identification method for plate-like structures is proposed based on the fact that damage can introduce imminent local anomalies to two-dimensional curvature propagating flexural waves (2D-CPFW). The 2D-CPFW can be alternated using two-dimensional continuous wavelet transform (2D-CWT) to lower adverse effects of measurement noise and errors. To suppress global trends and intensify the local anomalies in the 2D-CPFW, 2D-CWT with a higher-order Laplacian of Gaussian function and the Teager energy operator are applied. The fundamental mechanism of how 2D-CWT with different orders of Laplacians of Gaussian function can suppress global trends of 2D-CPFW is investigated for the first time. It is found that the rth-order Laplacian of Gaussian function can well suppress the global trend of a 2D-CPFW within a finite interval if the trend can be well approximated by a 2r−1th-order bi-variant polynomial. The modal assurance criterion and a statistical criterion are used to assist the selection of the proper order of Laplacian of Gaussian function. An accumulative damage index is proposed, and the locations and extent of the damage can be identified within neighborhoods with high damage index values. If an intact plate-like structure is geometrically smooth and made of materials that have no mass and stiffness discontinuities, the proposed method does not require any waveforms of the intact structure serving as a baseline, and therefore, the method can be considered baseline-free. Effectiveness and noise-robustness of the proposed method are investigated in two numerical examples. Two experimental investigations were conducted on two aluminum plates under different damage scenarios. Both the numerical and experimental examples verify that the proposed method is effective and noise-robust in identifying the location and extent of the damage.

Structural Damage Identification Based on Variable-Length Elements and an Improved Genetic Algorithm for Railway Bridges

A new damage identification method is proposed to solve the problem of no correspondence between the element division form of the finite element model and the actual damage location. The three basic operators in the traditional genetic algorithm are improved, and the catastrophe and neighborhood search processes are introduced to enhance the local optimization ability of the algorithm. The train–rail–bridge coupling time-varying equation is established. Based on the dynamic response of the bridge under trainload, the damage index is constructed, and the corresponding objective function is given. Through a numerical example, the stability and convergence rate of the algorithm are statistically analyzed. The effects of noise, the number of measuring points, and train speed on the recognition results are discussed. The research results indicate that, even if the damage location is different from the element division form of the finite element model, this method can accurately locate the damage location, but it will affect the quantitative results to a certain extent. In addition, the convergence speed of this method is fast, and the computing efficiency is about 6.7 times that of the conventional one-time recognition method.

Time Domain Damage Identification Approach of the Newly Hollow Floor Structure with TRMD

A tuned rolling mass damper system (TRMD) is newly combined with structural hollow slabs to act as an ensemble passive damping device to mitigate structural responses. The main advantage of this controlled configuration lies in its capacity to maintain architectural integrity. To investigate damage identification of this controlled structure with TRMD, a time domain identification approach with sensitivity-based model updating is studied in this paper. The simplified model and motion equation of a controlled structure with TRMD are derived considering the interaction force between the main structure and the damper. Furthermore, the dynamic response and dynamic response derivatives with respect to elemental variations in the controlled structure are analyzed to identify damage by minimizing the response difference between the intact state and the damaged state in the sensitivity-based updating procedure. A numerical example for a six-story frame structure with a TRMD on the top is employed to verify that not only the structural dynamic responses are controlled effectively by the rolling ball damper subjected to the earthquake excitation, but also damage location and extent can be identified accurately by the proposed method even with measurement noise considered. The shaking table test of a single-story frame equipped with TRMD is additionally carried out to validate the accuracy of the proposed structural damage identification method in the laboratory.

Damage Detection Method of Mining Conveyor Belt Based on Deep Learning

The belt conveyor is one of the key equipment in coal mining. Timely identification of belt damage is necessary to ensure coal mine safety production and reduce economic losses. In order to meet the needs of practical application, a new method of belt damage identification is proposed by fusing the temporal and spatial features of the image and directly using the on-site monitoring video image. Based on the deep learning network structure, an end-to-end belt damage detection model is designed. An improved attention mechanism is designed and introduced into the image spatial feature extraction model to solve the problem of complex background in belt surveillance video. In order to reduce the influence of reflection and shadow on recognition accuracy, a method of extracting temporal features of continuous multi frame video images using Temporal Convolutional Networks (TCN) is designed. The experimental results show that the accuracy of the method for belt damage recognition by fusing the temporal and spatial features of the image is more than 20% higher than that of the method for extracting the spatial features of the image alone, which shows that our proposed method is suitable for belt damage detection directly using the on-site belt monitoring video and is practical.

Vibration-based damage detection in anisotropic laminated composite beams by a shear-deformable finite element and harmony search optimization

A numerical procedure to detect and quantify damages in laminated composite beams with arbitrary lay-up using limited vibration data and harmony search optimization is presented. Since any damage in a structure changes its vibration characteristics, they can be considered as the diagnostic parameters with the help of an optimization technique. For the present study, frequencies and mode shapes are used as the damage diagnostic parameters, and the harmony search algorithm is used as the optimization tool. For finite element analyses, a five-node, thirteen-degree-of-freedom shear-deformable beam element is employed. This element can take into account the elastic couplings among extension, bending, and torsion arising due to material anisotropy existing in generally laminated composites as well as the Poisson’s effect. The damage to the beam is introduced by a stiffness loss coefficient at the elemental level while mass is assumed to be unchanged. Two different objective functions to be minimized are considered for damage detection. The efficacy of the proposed method with and without the presence of noise is demonstrated by two numerically simulated composite beams including single and multiple damages. Although its accuracy somewhat decreases in case of multiple minor damages, the proposed method is successful to detect moderate and severe single and multiple damages in practice. To overcome this weakness, (i) the algorithm can be hybridized with other metaheuristics, (ii) objective function can be enhanced by combining it with other vibration characteristics such as curvature, flexibility, and so on, or (iii) two- or multi-stage damage identification methods can be considered.

Two-stage damage identification method based on fractal theory and whale optimization algorithm

Structural damage identification based on time domain method of vibration response has been widely developed in the recent decades, however, it still confronts some difficulties, such as measurement noise and model error. This paper proposes a novel two-stage damage identification method based on fractal dimension and whale optimization algorithm (WOA). In this study, based on vibration data, the difference in curvature of fractal dimension (DCFD) is used as the damage index to identify the location of suspicious damage elements in the first stage. A new objective function is proposed based on the curvature of fractal dimension (CFD) of acceleration signal, and the WOA is used to estimate the severity of the suspicious damaged element in the second stage. Firstly, the validity of the proposed method is verified by a numerical simply supported beam, and the results exhibit good damage identification ability. Then different noise levels (5% ~ 20%) are introduced into the dynamic responses to verify its robustness, the result shows that the method is of good anti-noise ability in the first stage. Although the second stage is slightly sensitive to noise, it can still effectively identify the severity of damage. Secondly, the vibration testing of a steel I-beam is designed to verify the rationality of the method in the application of actual structure. Finally, based on the simulated vibration test data of the I-40 Bridge, the applicability of the method to complex civil structure is verified, which shows that the method still has good ability to identify the location and severity of damage in complex structure and is of great significance in practical application.

A method for quantitative damage identification in a high-piled wharf based on modal strain energy residual variability

Damage may occur in the pile foundations of high-pile wharfs due to the complex and diverse working conditions. To identify the damage in pile foundations in early stage is very important for the operation and maintenance on wharfs. Research on pile damage identification based on modal strain energy is one of the hot research topics. This study presents the concept of the modal strain energy residual variability using the characteristics of sensor placement and pile mode shapes of high-pile wharfs, which provide a simple, accurate and practical method to identify the damage in pile foundations of high-pile wharfs in early stage. The qualitative and quantitative relationships between the modal strain energy residual variability and element damage extent are deduced. This concept not only enhances the sensitivity of the modal strain energy to damage but also realizes the decoupling of damage element identification, which is easy to calculate in practical engineering. A damage identification method is constructed base on the concept, and the accuracy of the method is verified by a finite element numerical simulation and model test. A sensor placement method for engineering detection is studied by comparing the damage identification effect in different sampling intervals. The results show that this method can accurately locate a damage area and accurately identify the damage extent in numerical simulation and model test, which provides a new method for the practice of pile foundation damage identification of high-pile wharfs.

A Rapid Post-earthquake Structural Damage Identification Method Based on Mode Shape Ratio Using Pattern Matching

This paper proposes a rapid identification method for post-earthquake structural damage based on mode shape ratio using pattern matching. In the first stage, the characteristic vector of mode shape ratio is calculated by modal analysis from the modal information of various damage scenarios to establish a pattern database. A small number of dynamic signals are collected for extracting the modal information to obtain the characteristic vector which is treated as the test pattern. Then the similarity between the test pattern and the pattern in the database is calculated using the Euclidean distance (ED). The damage location and severity of the measured structure are treated the same as the pattern corresponding to the minimum value of ED so that the damage is identified. Numerical simulations verify that the proposed method can well identify the damage. In addition, the matching results based on different sensor combinations demonstrate that the method can quickly identify the damage even using a limited number of sensors. The proposed method can provide support for the government to make a rapid post-earthquake emergency decision

Damage identification using wave damage interaction coefficients predicted by deep neural networks

The ever-increasing demand for efficiency and cost improvements in lightweight structures with guaranteed safety and reliability is leading to the application of a damage-tolerant design philosophy. Here, accurate knowledge of structural health is critical to avoid catastrophic failures. This knowledge can be obtained by using advanced structural health monitoring (SHM) systems. For thin-walled lightweight structures, methods utilizing guided waves generated by piezoelectric transducers are well suited. The interaction between the guided waves and potential damages can be described by so-called wave damage interaction coefficients (WDICs). These WDICs are unique for each damage and depend solely on its characteristics for a given structure. Therefore, the comparison of known WDICs with estimated ones allows drawing conclusions about the current structural state. In this paper, a novel damage identification method for plate-like structures based on a database of such WDICs is presented. Selected damages are simulated numerically with finite elements to generate WDIC patterns. However, these simulations are computationally highly demanding, thus only a very limited number of damage scenarios can be simulated. This study proposes an innovative technique to substantially enhance the resulting WDIC database by using deep neural networks (DNNs). These DNNs enable smart interpolations and allow not only predicting WDICs for previously unseen damages at low computational costs but also the discovery of knowledge about the complex relationship between damage features and WDIC patterns. A comparison to other machine learning algorithms clearly shows the superior performance of the utilized DNNs for interpolating complex WDIC patterns. The proposed damage identification method is verified using advanced time-domain simulations of a large aluminum plate. A statistical analysis of correct identification rates in a common three-sensor setting is employed for assessing the general performance. It is demonstrated that carefully identified DNNs enable to accurately replicate and interpolate complex WDIC patterns. Furthermore, it is shown that these predicted WDICs allow identifying damage characteristics with high confidence.

Research on Damage Localization of Steel Truss–Concrete Composite Beam Based on Digital Orthoimage

Most structural health monitoring is carried out for a limited number of key measurement points of a bridge, and incomplete measurement data lead to incomplete mechanical equation inversion results, which is a key problem faced in bridge damage identification. The ability of digital images to holographically describe structural morphology can effectively alleviate the problem of damage identification due to incomplete test data. Based on digital image processing technology, a matrix similarity damage identification method based on a structural digital orthoimage was proposed. Firstly, a steel truss–concrete composite beam specimen with a complex support bar system was designed and fabricated in the laboratory, and the digital orthoimage of the test beam was obtained by the perspective transformation of the original image of the test beam. The body contour of the structure was extracted from the digital orthoimage of the test beam, and wavelet threshold denoising was performed on the lower edge profile to obtain the deflection curves of the structure under different working conditions. The verification results show that the maximum error of the deflection curve is 3.42%, which proves that the digital orthoimage can accurately and completely reflect the deformation of the structure. Finally, based on the digital orthophoto of the test beam, a matrix similarity test before and after the damage was carried out, and the results show that the singularities of the similarity distribution are consistent with the location of the damage; furthermore, the accurate positioning of the damage in different working conditions is achieved.

Adaptive guided wave-based damage identification under unknown load conditions

Damage identification methods based on guided waves (GWs) have been widely researched in the field of aircraft structural health monitoring. Notably, the existing research has not extensively considered the realization of accurate damage localization in an unknown load environment, although this aspect is of significance to the real-time safety assessment of aircraft structures. To address this issue, we propose an adaptive damage imaging method based on GW signals for typical aircraft structures subjected to unknown workloads. First, through the GW signals and load data sampled in the working load range, a mathematical model between the characteristics of the GW signal and load is established. Second, the active GW signal is adaptively compensated by the real-time identified load through the passive strain signal during the service process of the structure. Finally, the alternating time-reversal phase synthesis (ATRPS) method is used to accurately locate the damage in the monitoring area. The feasibility, applicability, and accuracy of the proposed methodology are validated by three levels of experimental cases. All results consistently indicate that reliable damage localization can be achieved under an unknown load environment.

Nonlinear damage identification method of transmission tower structure based on general expression for linear and nonlinear autoregressive model and Itakura distance

Fatigue cracks and bolt looseness are two kinds of common nonlinear damage in a transmission tower structure. However, due to the complexity of the transmission tower structure, it is difficult to identify the nonlinear damage accurately by using traditional damage identification methods. To solve this problem effectively, a time domain damage identification method based on general expression for linear and nonlinear autoregressive model (GNAR model) and Itakura distance is proposed. To describe the stochastic characteristics of time series more concisely and accurately, the optimized structure of GNAR model was selected by the stochastic pruning algorithm based on greedy strategy. And Itakura distance was used as a damage indicator for nonlinear damage identification. The nonlinear damage experiment of three-story frame model in Los Alamos laboratory was used to verify the effectiveness of the proposed method, and this method was applied to the nonlinear damage identification experiment of a transmission tower steel frame model. In the transmission tower model experiment, two kinds of nonlinear damage types are considered: component breathing cracks and joint bolt loosening. The results show that the proposed nonlinear damage identification method can easily identify the nonlinear damage of the frame model and the transmission tower model effectively. The change of floor mass barely has effects on the damage identification results. The damage probability of the damaged stories calculated by the proposed method is significantly higher than that of the undamaged stories, so that it is helpful to find the location of the nonlinear damage source efficiently. And the proposed method is a damage identification method based on sub-structure story, which can identify the transmission tower model with two nonlinear damage sources at the same time.

Structural Damage Identification Method Based on Parametric Boundary Condition Optimization

According to the heterogeneous background interference, the subjective consciousness, inaccurate quantification, and high cost in practical engineering, a method of applying the Bouc–Wen hysteresis nonlinear model to hysteresis characteristics of rubber isolation bearings is proposed, and this method analyzes the sensitivity of nonlinear parameters of the Bouc–Wen hysteresis model and obtains the order of nonlinear parameters of hysteresis model under different sizes of excitation. The results show that the prediction error of fatigue life is 16% less under various stress ranges, a rectangular identification frame with a certain probability guarantee rate is used to realize multitype seismic damage classification and location, the average accuracy of the identification of concrete cracking and spalling, steel bar exposure, and buckling is more than 80%, and the average coverage rate is more than 88%. During the use of civil engineering structures, the working environment is constantly changing; combined with the current damage identification and detection methods, the structure health monitoring system is established, and it is of great significance to promote the practical development of structural damage identification methods to accurately monitor the structure state in real time and to make a rapid and accurate diagnosis when the structure damage occurs.