Identify Damage Location Research Articles

An Efficient Approach for Damage Identification of Beams Using Mid-Span Static Deflection Changes

In structural health monitoring, determining the location and index of damage is a critical task in order to ensure the safe operation of the construction project and to enable the early recovery of losses. This paper presents a novel method for identifying damage location and damage index in simply supported (SS) beams by analyzing deflection changes at the mid-span point. Theoretical equations for mid-span deflection of simply supported beams with local damage are derived based on the principle of Virtual Work. Utilizing mid-span deflection, formulas for deflection change (DC) between two structural states, along with the first and second derivatives of DC at the mid-span point, are developed. The method of determining the location and damage index is then extended from intact beams to cases of beams with multiple damage zones and from damaged beams to beams with new failures. The graphical analysis of these quantities facilitates the determination of the number, location, and index of new damages. Various case studies on simply supported beams, involving one, two, and four damage zones at different positions and with varying damage indexes, are examined. The comparison of the theoretical method with the numerical simulations using Midas FEA NX 2020 (v1.1) software yields consistent results, affirming the accuracy and efficacy of the proposed approach in identifying and determining the damage locations as well as the damage indices.

Identifying damage in long-span self-anchored suspension bridges using characteristic indices

This study presents a methodology for detecting damage in bridge structures using three damage identification indices: the deviation of displacement influence line (DDIL), the deviation curvature of displacement influence line (DCDIL), and the relative rate of change of wavelet packet energy (RES). The proposed approach is based on the principles of displacement influence line and wavelet packet transform. To demonstrate the effectiveness of the three metrics in determining the location of structural damage, a finite element model of the main bridge in Zhengzhou Taohuayu Yellow River Bridge, a three-span twin-tower all-steel girder self-anchored suspension bridge, is evaluated. The study investigates the impact of various factors, such as damage degree, damage location, signal noise, variable speed vehicles, and random traffic flow, on the usefulness of the metrics. The results indicate that all three indices are effective in detecting damage in bridge structures. The DCDIL demonstrates a distinct, abrupt change at the location of damage, while the DDIL is somewhat less effective. Additionally, the RES curve is particularly sensitive to local damage and can identify damage locations far from the bridge span. However, the study notes that the accuracy and reliability of the RES identification indicators decrease non-linearly in the presence of signal noise. The highest values of the RES curve are positively correlated with acceleration and less closely related to uniform acceleration. A vehicle speed of 20 m/s is recommended for identifying the location of bridge structure damage. Finally, the study suggests that random, unevenly spaced traffic enhances vibration and can more precisely localize the damage location and degree of the bridge structure.

Adaptive unscented Kalman filter methods for identifying time‐variant parameters via state covariance re‐updating

AbstractThe conventional parameter identification process generally assumes that parameters remain constant. However, under extreme loading conditions, structures may exhibit nonlinear behavior, and parameters could demonstrate time‐variant characteristics. The unscented Kalman filter (UKF), as an efficient online recursive estimator, is widely used for identifying parameters of nonlinear systems. Nevertheless, it exhibits limitations when attempting to identify time‐variant parameters. To address this issue, this paper proposes a covariance matching technique that produces an array of adaptive UKF algorithms. Firstly, the sensitivity parameter η is defined to identify the instant when the parameter change occurs, and its threshold is calculated based on the sensitivity parameter time history curve. Secondly, an adaptive forgetting factor is introduced to simultaneously update the innovation, cross, and state covariance matrices when the kth‐step sensitive parameter surpasses the threshold. Finally, a secondary correction forgetting factor (SCFF) is employed to further re‐update the state covariance values at the identified damage locations. This creative step enhances the adaptive capability and optimizes the identification accuracy of the proposed algorithms. Both the numerical simulations and shaking table test demonstrate that the proposed adaptive algorithms can efficiently identify the time‐variant stiffness‐type parameters, and accurately capture their time‐variant characteristics.

Damage detection in jacket-type offshore platforms via generalized flexibility matrix and optimal genetic algorithm (GFM-OGA)

Jacket-type offshore platforms are crucial infrastructure assets that can be damaged, disrupting operations and causing significant economic losses. To manage these risks, Structural Health Monitoring (SHM) is crucial in extending the lifespan of these structures. Recent years have seen the development of innovative vibration-based methods for SHM, which are based on the principle that changes in the dynamic model specifications of a structure indicate damage. The aim of this paper is to present an effective process for detecting structural damage through vibrational analysis and determining its location and severity. The proposed method uses vibrational characteristics of the structure, such as mass and stiffness, to deduce changes in its physical properties through monitoring with sensors. The methodology is formulated as an optimization problem where the target function is established based on the modal information of the structure in both intact and damaged states, and optimized subject to specific constraints. The solution to the optimization problem provides information on the location and severity of the damage. The methodology is demonstrated through a case study on a scaled model of a jacket-type offshore platform, showing its effectiveness in accurately identifying damage location and severity at varying levels of damage.

Improved vibration based damage detection in laminated composite plate structures under free and forced modal analysis

Composite materials are widely used in modern structural design and technologies due to their qualified mechanical properties and high stiffness to weight ratio. Fibre breakage (FB) and delamination (Dlm) are the most serious damages in these materials, which led to stiffness reduction and loose of functionality and followed by catastrophic failure of the overall structure. The current study presents an extended evaluation of a recent proposed damage index to confirm its capability to identify and quantify structural damage in much more moderate conditions. It has used an improved technique to analysis the measured mode shape curvature (MSC) of the first three mode shapes under both free and forced vibrations for identifying damage location in laminated carbon fibre-reinforced polymer (CFRP) plate structures. It has confirmed its best finding for optimally diagnosis the damage severity and suppressing the confusion of noise effects in both theoretical and experimental analyses. The results show that the improved damage index can be employed with higher accuracy when compared with previous studies for detecting the exact damage location within the laminated composite structure.

Evaluating pod-based unsupervised damage identification using controlled damage propagation of out-of-service bridges

The effectiveness of Proper Orthogonal Decomposition (POD) for damage identification on out of service, rural bridges is examined using field data. Three similar, out of service, simply supported bridges, consisting of steel beams supporting a concrete deck, were tested prior to demolition and replacement. Strain time histories were recorded using optimal sensor networks with a vehicle of known weight crossed each bridge in different transverse locations and directions, and at varying speeds. The effectiveness with which the developed POD based methodology for damage identification was evaluated by progressively flame cutting select steel beam flanges and webs on each bridge. Snapshot matrices of structural response as measured by the sensors were used to create Proper Orthogonal Modes (POMs), and were obtained for each damage level and test, with the POMs being used to identify if changes in structural response were detected. The accuracy with which the POMs identified damage locations and their sensitivity to vehicle location and direction were evaluated. In addition, a previously developed novelty detection framework proposed by some co-authors was applied to identify changes after damage. It is shown that POD and the developed novelty index can effectively identify certain levels of imposed damage in tested bridges under known loads.

Removing speckle noise from the signals of a laser Doppler vibrometer on moving platforms (LDVom) by ensemble empirical mode decomposition

With increasing requirements for structural stability and durability, effective monitoring strategies for existing and potential damage are necessary. A laser Doppler vibrometer on moving platforms (LDVom) can remotely capture large-scale structural vibrations, but speckle noise, a significant signal issue mainly when one-way continuously scanning from moving platforms, restricts its applications. A novel approach based on ensemble empirical mode decomposition (EEMD) is proposed to eliminate speckle noise. Moving root-mean-square thresholds are used to cut off signal drop-outs. With both numerically simulated and experimentally acquired signals, the proposed EEMD-based approach reveals the true vibrations despite the low initial signal-to-noise ratio. Other methods fail to eliminate the speckle noise. In physical experiments, the despeckled signal energy is concentrated at defect locations in the Hilbert–Huang spectrum. The identified damage locations agree well with the actual damage locations. Therefore, the developed approach demonstrates advantages and robustness of eliminating speckle noise in LDVom signals for damage inspection.

Localization and classification of structural damage using deep learning single-channel signal-based measurement

Diligent damage identification is a core thrust of structural health monitoring (SHM). Vibration-based SHM has recently gained paramount importance. Substantial research devoted to Deep Learning (DL) algorithms yielded superior accuracy. This study proposes a novel DL-based damage detection approach to automatically extract features from raw acceleration sensor data. A new One-Dimensional Convolutional Neural Network named BuildingNet was designed to learn features and identify damage locations in real-time under different damage assessment scenarios. Parametric studies were conducted on different layer numbers, size of training datasets, and noise levels. Systematic studies for optimizing the network architecture and training data were performed. Mid-rise building case studies demonstrated the accuracy and efficiency of the proposed model. Time-domain monitoring data, both from multiple and single-channel measurements, were used for training and testing different architectures of BuildingNet. The proposed model achieved excellent damage localization and classification performance on both noise-free and noisy data sets.

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.

Identifying Damage Location under Statistical Pattern Recognition by New Feature Extraction and Feature Analysis Methods

Identifying Damage Location under Statistical Pattern Recognition by New Feature Extraction and Feature Analysis Methods

Experimental investigation of decomposition of signal energy for damage detection of jacket type offshore platforms

ABSTRACT Steel jacket-type offshore platforms are operating in extreme environmental conditions and are prone to various damages. Thus, using structural health monitoring techniques prevents irreparable damages, long-term out of services, and losses. Several methods, such as physical and data-driven models-based, are presented for damage detection. In this paper, the experimental response of a prototype model with various damage scenarios has been measured. The prototype was excited using an eccentric-mass actuator, and dynamic responses were applied to identify damage locations using the decomposition of the signal energy approach. This approach could overcome the weaknesses of other energy methods. These methods don't demonstrate the location and intensity of damages when a wide range of damages exists in the structure. However, the DSE approach can predict all damages in a prototype with reasonable accuracy. Therefore, the DSE method can be proposed as an acceptable method for structural health monitoring for steel jacket-type offshore platforms.

Damage detection using both energy and displacement damage index on the ASCE benchmark problem

This paper aims to present a novelty damage detection method to identify damage locations by the simultaneous use of both the energy and displacement damage indices. Using this novelty method, the damaged location and even the damaged floor are accurately detected. As a first method, a combination of the instantaneous frequency energy index (EDI) and the structural acceleration responses are used. To evaluate the first method and also present a rapid assessment method, the Displacement Damage Index (DDI), which consists of the error reliability (β) and Normal Probability Density Function (NPDF) indices, are introduced. The innovation of this method is the simultaneous use of displacement-acceleration responses during one process, which is more effective in the rapid evaluation of damage patterns with velocity vectors. In order to evaluate the effectiveness of the proposed method, various damage scenarios of the ASCE benchmark problem, and the effects of measurement noise were studied numerically. Extensive analyses show that the rapid proposed method is capable of accurately detecting the location of sparse damages through the building. Finally, the proposed method was validated by experimental studies of a six‐story steel building structure with single and multiple damage cases.

Theoretical and experimental study on the continuum damage mechanical (CDM) behavior of RTPs under axial tension

An analytical model is proposed to investigate the linear and nonlinear mechanical response of reinforced thermoplastic pipes (RTPs) under axial tension, in which the existing homogenization method, failure criteria and material degradation models are combined to predict the CDM behavior in an iterative and cyclic way. To obtain damage sequences, the homogenization method is modified by a stress correction factor to consider the effect of cross-sectional curvature. Once corrected stresses of homogenous layers satisfy von Mises criterion, Ramberg-Osgood curve is used to update elastic constants of isotropic materials. For composite laminates, a nonlinear stiffness degradation model is adopted to update the stiffness matrix if Hashin-Yeh failure criterion is satisfied. Quasi-static uniaxial tension tests were conducted on two RTP specimens to verify the proposed model. Besides, numerical simulation by calling a VUMAT subroutine were performed to observe the stress field in 3D composites. The proposed model was found to give accurate prediction on stiffness characteristics and stress field, and have functions including identifying damage location, predicting failure mode, and analyzing damage propagation. Furthermore, the effects of winding angles are studied, which showed that dominant failure mode would change from tensile fiber to tensile matrix as winding angles rise.

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.

Operational Damage Identification Scheme Utilizing De-Noised Frequency Response Functions and Artificial Neural Network

A damage identification scheme combining impact-synchronous modal analysis (ISMA) and artificial neural network is developed in this study. The ISMA de-noising method makes it feasible to detect and classify the damage states with high accuracy when the machine is under operation. The feed-forward backprop network was utilized in this study. The input feature vector of the network consisted of the FRF changes in a selected vibrational mode frequency interval at several measurement points. The scheme was tested on a rectangular Perspex plate. It is proved that the trained network can successfully identify damage locations with the testing data collected by ISMA, which allows the damage detection to be carried out without shutting down the tested machine. For the plate structure in this study, an overall accuracy reached 100% when all five measurement points were used. With the input features optimized by mode shape assessment, 100% accuracy was also achieved with only two measurement points.

Steel railway bridge fatigue damage detection using numerical models and machine learning: Mitigating influence of modeling uncertainty

Stringer-to-floor beam connections were reported as one of the most fatigue-prone details in riveted steel railway bridges. To detect stiffness degradation that results from the initiation and growth of fatigue cracks, an automated damage detection framework was proposed by the authors (Eftekhar Azam et al., 2019; Rageh et al., 2018). The proposed method relies on Proper Orthogonal Decomposition (POD) and Artificial Neural Networks (ANNs) to identify damage location and intensity under non-stationary, unknown train loads. Bridge computational models were used to simulate damage scenarios and for training the ANNs. Damage detection method efficiency and accuracy were shown to be significantly influenced by the level of modeling uncertainties (MUs). To investigate the applicability of the proposed framework to in-service bridges, a systematic analysis of the effect of MUs on the proposed POD-ANN framework was necessary. MU influence on the performance of the POD-ANN damage detection method was investigated and a new procedure for generating training data for ANNs was proposed. The procedure was based on synergizing Proper Orthogonal Modes (POMs) extracted from measured structural response and POMs calculated from the numerical model. The current study integrated numerical and field investigations. The main objective of the numerical investigation was to identify a robust damage feature independent of the level and location of assumed MUs. Results showed that Damage Location (DL) and Damage Intensity (DI) were detected with high accuracy for studied uncertainty cases; however, as expected, damage detection accuracy reduced as MU increased. A hybrid experimental-numerical approach was then implemented for the field investigation studies. This approach applied identified damage features from the numerical investigation to measurements from an in-service railway bridge to produce damage scenarios used to train the framework. MATLAB algorithms were developed that preprocessed field data and eliminated POM variations resulted from loading uncertainties. ANNs were trained and tested using the field strain estimated POMs from the hybrid approach and DL and DI results were obtained for the studied railway bridge under non-stationary, unknown train loads. These results show the promise of the POD-ANN method as a robust, real-time fatigue damage identification tool for steel railway bridges.

Damage detection of highway bridges based on long-gauge strain response under stochastic traffic flow

Abstract Highway bridges are essential infrastructure engineering. To monitor their conditions effectively, bridge structural health monitoring (SHM) techniques have been developed to substitute the manual inspection. Among SHM techniques, the damage detection method is the most promising one utilized for identifying damage location and extent. However, most frequency-domain signal based damage detection methods are not sufficiently sensitive to local damage, while other time-domain signal based methods with a sufficient sensitivity have many limitations, especially on working conditions where those methods are only feasible when just a single vehicle passes over the bridge. This restriction severely differs with actual traffic situations. Under these backgrounds, in this study, a damage detection method based on long-gauge fibre Bragg grating (FBG) was proposed, which can achieve the identification of the damage location and extent under stochastic traffic flow. This method’s feasibility was initially verified through a series of indoor bridge model experiments. Then, some numerical case studies were conducted to mimic actual stochastic traffic flow conditions. The results of experiment and numerical simulation demonstrated that this method performs well in detecting the damage location and extent under various designed situations, and it has the potential to be an alternative for the current methods.

Hierarchical neural network for damage detection using modal parameters

This study develops a damage detection method based on neural networks. The performance of the method is numerically and experimentally verified using a three-story shear building model. The framework is mainly composed of two hierarchical stages to identify damage location and extent using artificial neural network (ANN). The normalized damage signature index, that is a normalized ratio of the changes in the natural frequency and mode shape caused by the damage, is used to identify the damage location. The modal parameters extracted from the numerically developed structure for multiple damage scenarios are used to train the ANN. The positive alarm from the first stage of damage detection activates the second stage of ANN to assess the damage extent. The difference in mode shape vectors between the intact and damaged structures is used to determine the extent of the related damage. The entire procedure is verified using laboratory experiments. The damage is artificially modeled by replacing the column element with a narrow section, and a stochastic subspace identification method is used to identify the modal parameters. The results verify that the proposed method can accurately detect the damage location and extent.

Genetic algorithm based optimal sensor placement forL1-regularized damage detection

Sparse recovery theory has been applied to damage detection by utilizing the sparsity feature of structural damage. The theory requires that the columns of the sensing matrix suffice certain independence criteria. In l1-regularized damage detection, the sensitivity matrix serves as the sensing matrix and is directly related to sensor locations. An optimal sensor placement technique is proposed such that the resulting sensitivity matrix is of the maximum independence in the columns or is of the least mutual coherence. Given a total number of sensors, the selection of sensor locations is a combinatorial problem. A genetic algorithm is thus used to solve this optimization problem, in which the mutual coherence of the sensitivity matrix is minimized. The obtained optimal sensor locations and associated sensitivity matrix are used in l1-regularized damage detection. An experimental cantilever beam and a three-storey frame are utilized to verify the effectiveness and reliability of the proposed sensor placement technique. Results show that using the modal data based on the optimal sensor placement can identify damage location and severity more accurately than using the ones based on uniformly selected sensor locations.

Damage localization under ambient excitations and non-stationary vibration signals by a new hybrid algorithm for feature extraction and multivariate distance correlation methods

Ambient excitations applied to structures may lead to non-stationary vibration responses. In such circumstances, it may be difficult or improper to extract meaningful and significant damage features through methods that mainly rely on the stationarity of data. This article proposes a new hybrid algorithm for feature extraction as a combination of a new adaptive signal decomposition method called improved complete ensemble empirical mode decomposition with adaptive noise and autoregressive moving average model. The major contribution of this algorithm is to address the important issue of feature extraction under ambient vibration and non-stationary signals. The improved complete ensemble empirical mode decomposition with adaptive noise method is an improvement on the well-known ensemble empirical mode decomposition technique by removing redundant intrinsic mode functions. In addition, a novel automatic approach is presented to select the most relevant intrinsic mode functions to damage based on the intrinsic mode function energy level. Fitting an autoregressive moving average model to each selected intrinsic mode function, the model residuals are extracted as the damage-sensitive features. The main limitation is that such features are high-dimensional multivariate time series data, which may make a difficult and time-consuming decision-making process for damage localization. Multivariate distance correlation methods are introduced to cope with this drawback and locate structural damage using the multivariate residual sets of the normal and damaged conditions. The accuracy and robustness of the proposed methods are validated by a numerical shear-building model and an experimental benchmark structure. The effects of sampling frequency and time duration are evaluated as well. Results demonstrate the effectiveness and capability of the proposed methods to extract sufficient and reliable features, identify damage location, and quantify damage severity under ambient excitations and non-stationary signals.