Damage Location Identification Research Articles

A Particle Swarm Optimization Algorithm with Sigmoid Increasing Inertia Weight for Structural Damage Identification

In this study, a particle swarm optimization with a sigmoid increasing inertia weight (SIPSO) algorithm is proposed for structural damage identification based on the optimization of structural vibration response constraints. In view of the existing problems for particle swarm optimization algorithms used for structural damage identification, such as low accuracy of damage identification and easy misjudgment of damage location, the sigmoid increasing inertia weight is introduced to improve the global and local search ability of the algorithm. Simulation results show that the parameters of the sigmoid increasing inertia weight have a significant effect on the performance of the SIPSO algorithm for structural damage identification. Compared with similar improved particle swarm optimization algorithms, the SIPSO algorithm has some advantages of fast convergence speed, high identification accuracy, and strong robustness ability in structural damage identification.

Research on Bridge Structural Damage Identification

The traditional identification methods have limited ability to identify damage location of bridge structures. Therefore, a bridge structural damage location identification method based on deep learning is proposed. In addition, the sigmoid function is the activation function, and the cross entropy is the cost function. Meanwhile, take the Gaussian noise as the addition method and take the softmax as the classifier. So the constructed SDAE deep learning model can realize damage location identification of the simply supported the continuous beam bridges. Compared with the traditional identification methods of bridge structures, namely BP network and SVM, the proposed method shows higher identification accuracy and antinoise performance. Here, the average identification accuracy of the method for continuous beam bridge is 99.8%. As can be seen that the proposed method is more suitable for practical bridge structure damage location identification.

Structural Damage Detection through EMI and Wave Propagation Techniques Using Embedded PZT Smart Sensing Units.

Lead Zirconate Titanate (PZT) sensors have become popular in structural health monitoring (SHM) using the electromechanical impedance (EMI) technique for damage identification. The vibrations generated during the casting process in concrete structures substantially impact the conductance signature’s (real part of admittance) magnitude and sensitivity. The concept of smart sensing units (SSU) is presented, composed of a PZT patch, an adhesive layer, and a steel plate. It is embedded in the concrete structure to study the impact of damage since it has high sensitivity to detect any structural changes, resulting in a high electrical conductance signature. The conductance signatures are obtained from the EMI technique at the damage state in the 10–500 kHz high-frequency range. The wave propagation technique proposes implementing the novel embedded SSUs to detect damage in the host structure. The numerical simulation is carried out with COMSOL multiphysics, and the received voltage signal is compared between the damaged and undamaged concrete beam with the applied actuation signal. A five-cycle sine burst modulated by a Hanning window is employed as the transient excitation signal. For numerical investigation, six cases are explored to better understand how the wave travels when a structural discontinuity is accounted for. The changes in the received signal during actuator–receiver mode in the damage state of the host structure are quantified using time of flight (TOF). Furthermore, the numerical studies are carried out by combining the EMI-WP technique, which implies synchronous activation of EMI-based measurements and wave stimulation. The fundamental idea is to implement EMI-WP to improve the effectiveness of SSU patches in detecting both near-field and far-field damage in structures. One SSU is used as an EMI admittance sensor for local damage identification. Meanwhile, the same EMI admittance sensor is used to acquire elastic waves generated by another SSU to monitor damages outside the EMI admittance sensor’s sensing area. Finally, the experimental validation is carried out to verify the proposed methodology. The results show that combining both techniques is an effective SHM method for detecting damage in concrete structures.

Structural assessment of bridges through ambient noise deconvolution interferometry: application to the lateral dynamic behaviour of a RC multi-span viaduct

Operational Modal Analysis (OMA) is becoming a mature and widespread technique for Structural Health Monitoring (SHM) of engineering structures. Nonetheless, while proved effective for global damage assessment, OMA-based techniques can hardly detect local damage with little effect upon the modal signatures of the system. In this context, recent research studies advocate for the use of wave propagation methods as complementary to OMA to achieve local damage identification capabilities. Specifically, promising results have been reported when applied to building-like structures, although the application of Seismic Interferometry to other structural typologies remains unexplored. In this light, this work proposes for the first time in the literature the use of ambient noise deconvolution interferometry (ANDI) to the structural assessment of long bridge structures. The proposed approach is exemplified with an application case study of a multi-span reinforced-concrete (RC) viaduct: the Chiaravalle viaduct in Marche Region, Italy. To this aim, ambient vibration tests were performed on February 4^{text {th}} and 7^{text {th}} 2020 to evaluate the lateral and longitudinal dynamic behaviour of the viaduct. The recorded ambient accelerations are exploited to identify the modal features and wave propagation properties of the viaduct by OMA and ANDI, respectively. Additionally, a numerical model of the bridge is constructed to interpret the experimentally identified waveforms, and used to illustrate the potentials of ANDI for the identification of local damage in the piers of the bridge. The presented results evidence that ANDI may offer features that are quite sensitive to damage in the bridge substructure, which are often hardly identifiable by OMA.

Damage Identification Based on the Loaded Frequency Changes and Deep Learning for Bridge

Frequency is widely used in damage identification as the dynamic characteristic parameter with the simplest measurement method and the highest measurement accuracy, but the frequency changes are nonunique parameters of damage location for the symmetrical structure. To improve the method of damage location identification based on frequency changes for bridge, the bridge damage identification network based on loaded frequency changes and deep learning theory is proposed by introducing the moving vehicle detection method. A continuous beam example is used for testing, and the test results show that the identification network based on the bridge load frequency changes and the Stacked Denoising Auto-Encoder(SDAE) network can effectively locate the damage of bridge, which enables the frequency changes data to overcome the nonunique limitation of the symmetrical structure, enhances the expression ability of the parameters and improves the effectiveness and the anti-noise property of bridge damage location identification.

Two-Stage Damage Detection of Beam Structure Based on Improved PSO Algorithm

In order to solve the problem of damage location and degree identification of beam structure under multiple damage and small damage condition, a two-stage recognition method based on the improved particle swarm optimization (PSO) algorithm was proposed. Firstly, the first stage damage localization is carried out. The D-S evidence theory fusion algorithm is used to fuse element damage variable and element stiffness reduction coefficient. Then, the improved PSO algorithm is used to update the location results and accurately quantify the damage. The results of simulation and experiment show that the two-stage recognition method can identify the damage locations and detect the damage extent precisely for multi-damage and small damage, and has the advantage of good anti-noise performance.

Local damage identification of high-strength circular concrete-filled steel tubes under low cycle fatigue

This paper investigates the low cycle fatigue (LCF) induced damages of high-strength circular concrete-filled steel tubular (CFT) beam-columns subjected to nonstationary loading history. The test specimens are fabricated by ultra-thin walled steel tubes with 2 mm in thickness to clarify the vulnerability concerning fatigue failure, and to evaluate the fatigue life of slender and over-design circular CFT beam-column members. Experimental results indicated that the specimens with a large D/t ratio together with thin-walled steel tubes tend to trigger highly-concentrated local buckling at column bottom where sequentially experienced premature fractures due to low cycle fatigue and the fragmentation of concrete infill. This complex LCF-induced failure mode can be identified by developing a new damage detection technique using polymer optical fiber sensors with distributed placement. It is found that the sequence of variable amplitudes during LCF history plays significant influences on the accumulation of plastic deformation which is not necessarily correlated to the sequence of nonstationary loading history induced by earthquake ground motions. Moreover, the epistemic uncertainty of variable amplitude of displacement demonstrates considerable influences on fatigue life with variable displacement amplitudes.

Low-velocity impact resistance of thin-ply in comparison with conventional aluminium-carbon laminates

This study present investigation on the low-velocity impact resistance of fibre metal laminates consisting of aluminium layers and a thin-ply carbon fibre reinforced polymer. A thin-ply effect is compared with fibre metal laminates consisting of conventional thickness plies. Low-velocity impact tests are conducted with impact energies ranging from 2.5 J to 30 J. The low-velocity impact behaviour of conventional and thin-ply laminates is estimated and compared by analysis of key characteristics describing impact during the impactor-laminate contact. In addition, impact damage of the laminates is analysed, focusing on the identification of dominant damage modes and locations. Results demonstrate that the use of a thin-ply carbon fibre reinforced polymer neither increases the low-velocity impact resistance of the laminates nor significantly changes their low-impact impact response as compared to the laminates with conventional ply thickness. Results demonstrate that the damage mechanism of the composite thin plies does not significantly differ from that of the conventional carbon plies. Results also confirm that the use of thin-ply fibre metal laminates is an interesting and promising materials solution.

Internal Damage Identification of Sandwich Panels With Truss Core Through Dynamic Properties and Deep Learning

For sandwich panels with truss core, the weakest part is the low density truss core, some effective damage identification methods have been proposed for sandwich panels. However, those works mainly focused on damage location identification and few works discussed the damage extent detection. In the work, a damage identification method integrating deep learning technique with dynamic properties is proposed to identify both location and extent of internal damages in sandwich panels with truss core. An analytical model verified by experiments based on laser vibrometer is used to get raw data, which can generate various damages inside the two face sheets. Instead of using captured surface photos or raw data as the DL training dataset, the dataset is constructed using damage indexes instead of raw data. By combing with the analytical model, dataset of specimens with various defeats was collected and used as the input of the neural networks. Abilities of identifying damage location and damage extent were used to evaluate the effectiveness of the proposed technique. According to the results, it shows that the proposed method could identify the location and extent of internal damages accurately.

Multifunctional flexible sensor array-based damage monitoring for switch rail using passive and active sensing

A passive monitoring technique for switch rails will be very attractive because it does not require extra excitations and can realize large area monitoring. However, complex wheel-rail interactions and sparse sensors will affect the accuracy of local damage identification. In contrast, an active monitoring technique has high local damage quantification ability but has low efficiency for large area monitoring. This study firstly presents a combination of piezoceramic material Lead zirconate titanate (PZT) wafers with accelerometers to configure a multifunctional sensor array that brings together active and passive monitoring mechanisms. The multifunctional sensor arrays are produced in a flexible form, which are adapted to complex geometry and are easy to install. In complementary passive monitoring, each sub area’s health state between 2 accelerometers is represented by reconstructing transfer functions, which are calculated from rail vibrations. The reconstructed transfer function in conjunction with principal component analysis (PCA)-based feature extraction is utilized for estimating the damaged sub area. Then accurate local damage identification can be realized through the active guided waves technique and correlation-based diagnostic imaging in conjunction with singular value decomposition (SVD). Simulation and experimental studies show the proposed mechanisms can identify the damaged sub area and basically extract damage locations with different lengths correctly with the sparse sensor array. Finally, based on a controlled experiment, the causes of the errors are evaluated and analyzed. The proposed mechanism can simplify data analysis, save monitoring time, and decrease the final cost.

Unsupervised Damage Identification Scheme Using PCA-Reduced Frequency Response Function and Waveform Chain Code Analysis

Mechanical machines face structural damage problems during their service life. Structural damage can severely affect safety and functionality of the structure and lead to economic loss. In this work, a damage identification scheme is developed by combining waveform chain code (WCC) analysis and hierarchical cluster analysis based on complex network theory. Waveform chain code analysis was carried out using the principal component analysis reduced frequency response function (PCA-reduced FRF), and the areas under the slope differential value curves were calculated as damage-sensitive WCC features. Unsupervised machine learning using hierarchical cluster analysis was then conducted on these damage-sensitive features. A rectangular Perspex plate was studied using the newly developed damage identification scheme as an example. Experimental results showed that the proposed scheme can successfully separate all the damage conditions from the undamaged state with 100% accuracy. In terms of damage severity and location identification, the proposed scheme is sensitive to detect damage severity with damage index as low as 0.17. In addition, combination of PCA-reduced FRF and mode shapes showed positive correlation between the magnitude of the resonant peak and the displacement of the impact point in identifying different damage locations of the plate.

Uncertainty‐assisted deep vision structural health monitoring

AbstractComputer vision leveraging deep learning has achieved significant success in the last decade. Despite the promising performance of the existing deep vision inspection models, the extent of models’ reliability remains unknown. Structural health monitoring (SHM) is a crucial task for the safety and sustainability of structures, and thus, prediction mistakes can have fatal outcomes. In this paper, we use Bayesian inference for deep vision SHM models where uncertainty can be quantified using the Monte Carlo dropout sampling. Three independent case studies for cracks, local damage identification, and bridge component detection are investigated using Bayesian inference. Aside from better prediction results, the two uncertainty metrics, variations in softmax probability and entropy, are shown to have good correlations with misclassifications. However, modifying the decision or triggering human intervention can be challenging based on raw uncertainty outputs. Therefore, the concept of surrogate models is proposed to develop the models for uncertainty‐assisted segmentation and prediction quality tagging. The former refines the segmentation mask and the latter is used to trigger human interventions. The proposed framework can be applied to future deep vision SHM frameworks to incorporate model uncertainty in the inspection processes.

Damage identification based on convolutional neural network and recurrence graph for beam bridge

Traditional statistical pattern identification methods, such as artificial neural network and support vector machine, have limited ability to identify minor damage of bridges. Deep learning can mine the inherent law and representation level of sample data. As a typical algorithm of deep learning, convolutional neural network is a feedforward neural network with deep structure and convolution calculation, and its ability of image identification is very outstanding. The recurrence graph of structural response can reveal the internal structure, similarity, and damage information. The original structure response signal involves the coupling vibration of vehicle and bridge is filtered and reconstructed by wavelet packet, and then the recurrence graph of different damage cases is obtained, which is used as the input image of convolutional neural network as a new type of damage feature; thus, a damage identification method based on convolutional neural network and recurrence graph is established. The results of numerical simulation and model experiment show that the recurrence graph contains more damage information; compared with the traditional statistical pattern identification methods, convolutional neural network can achieve more accurate feature extraction and identification through intelligent learning layer by layer, so as to realize more accurate identification of damage location and damage degree.

A two-step method for beam bridge damage identification based on strain response reconstruction and statistical theory

In this work, a two-step damage identification method has been proposed for simply supported beam bridge local damage identification based on strain response reconstruction and statistical theory under moving loads. Damage could be identified without the response of healthy structure. Strain reconstruction equation was derived based on state space model. Strain responses were divided into two groups and corresponding reconstructed strain response of each group was obtained. Strain standard deviation (SSD) index was proposed based on the theory of static. The developed two-step method could be summarized as the following: first, SSD curves at damage state were employed to locate damage elements. Then, least square-based model updating method was applied to evaluate damage severity in the identified damage elements. Numerical evaluations on simply supported beams showed that the proposed method effectively identified damage elements and revealed damage severity even at noise intensities of up to 15%. In addition, it was found that SSD index was more robust to noise than strain response. Decreasing measurement noise and increasing the sampling frequency decreased the relative error of reconstructed SSD index. Finally, experimental investigation on a simple supported beam bridge model under moving vehicle condition showed that the obtained results were in accordance with pre-set damage scenarios, which proved the effectiveness of the proposed method.

Structural damage detection using principal component analysis of frequency response function data

In this paper, a new sensitivity-based model updating method is presented based on the changes of principal components (PCs) of frequency response function (FRF). Structural damage estimation, identification of damage location and severity, is conducted by an innovative sensitivity relation. The sensitivity relation is derived by incorporating PC analysis (PCA) data obtained from the incomplete measured structural responses in a mathematical formulation and is then solved by the least square method. In order to demonstrate the performance of the proposed method, it is applied to a truss and a frame model. The results prove the ability of the method as a robust damage detection algorithm in the presence of measurement and mass modeling errors. The comparative studies prove that the results obtained by the proposed sensitivity relation are more accurate than the results based on using FRF data directly.

Damage assessments of composite under the environment with strong noise based on synchrosqueezing wavelet transform and stack autoencoder algorithm

This study proposes a technique for damage location and quantitative identification for composites under strong noise background on the basis of synchro squeezing wavelet transform and stack autoencoder algorithm. Firstly, the simulation of the mechanism of Lamb wave and damage of different degrees were conduct in this stage. Secondly, the simulation of the actual damage was carried out in the selecting composites with strain field of the structure changed by different masses (within the range of 3 grades) and mass blocks at different locations (64 regions). Moreover, the white noise (3 dB) was introduced to the signal detected by the sensor in order to simulate the Lamb Wave signals collected from the sensors under the background of strong noise. Actually, the training sample is composed of 26,880 (3 × 64 × 140) signals. In addition, the synchronized wavelet transform was proposed to denoise the signal through employing strong noise. After that, Fourier Transform was applied to extract damaged frequencies. Additionally, the input of the stack autoencoder was the frequencies, and that output was the result of corresponding damage, and in the meanwhile the damage identification model was established as well. Finally, the test data which had been processed was then input into the damage identification model for further testing and analysis. It was finally concluded that 191(total 192) samples were correctly identified, and the correct recognition rate arrived as high as 99.48%. It can identify different degrees of damage in different locations. It was demonstrated by the results that the method is highly accurate, with extensive application potential in instantaneous locating and quantitative recogniting damages in the composite plate under the condition of strong noise exists.

Method of damage location determination based on a neural network using a single fiber Bragg grating sensor.

In this paper, a structural damage identification algorithm based on a single fiber Bragg grating (FBG) sensor isproposed. The signal detected by the FBG can be analyzed by the wavelet packet decomposition and back propagation neural network to obtain the damage location information. A high-speed FBG demodulation system based on a tunable Fabry-Perot filter and unbalanced Mach-Zehnder (M-Z) interferometer is designed to respond to a signal with a frequency range from 0 to 4kHz, which will increase the sensing accuracy. This algorithm is verified by the aluminum plate model, which can simulate the generation of damage in reality. The experimental results show that a single FBG sensor is enough to realize accurate damage location identification according to this algorithm, and identification accuracy can reach 90.0%.

Physical‐based parametrization and local damage identification for frame‐type structures using response sensitivity approach in time domain

Physical‐based parametrization and local damage identification for frame‐type structures using response sensitivity approach in time domain

Vibrations of a Box-Sectional Cantilever Timoshenko Beam with Multiple Cracks

This paper considers a Timoshenko cantilever beam with box cross-section including multiple cracks. Under six damage scenarios, the problem is solved analytically by the transfer matrix method, and numerically by the finite element method. Results are validated by the experimental measurements with the aid of ambient vibration tests, that use Enhanced Frequency Domain Decomposition and Stochastic Subspace Identification methods. Measured and calculated natural frequencies and mode shapes for undamaged and damaged beams are compared with each other. Modal assurance criterion and coordinated modal assurance criterion values are obtained from two set of measurements to establish the correlation between the measured and calculated mode shapes for the damage location identification.

Application of Bayesian statistical method in sensitivity-based seismic damage identification of structures: Numerical and experimental validation

Most of the developed sensitivity-based damage detection methods are based on the application of external excitations which could be prohibitive due to infeasible excitation of all structural degrees of freedom. In this regard, identification of damage properties using seismic structural response would be advantageous. In this research, sensitivity-based finite element model updating method is proposed to identify structural damage by earthquake response in the frequency domain and the transfer function of the structure due to ground excitation. The obtained sensitivity equation is solved by linear least square method through defining constraints on the design variables. Since the attainable measured data are restricted by limits on the instrumentations and preciseness of the measurements and due to the fact that only a few of the lower modes of a structure can generally be determined with confidence, a Bayesian statistical method is utilized to enhance the reliability of the predicted damage properties. The proposed technique is applied to a numerical frame model and an experimental six-story steel structure with various scenarios of story stiffness reduction. The results are indicative of the capability of the proposed method for identification of damage location and severity.