Vibration-based Damage Identification Research Articles

Wireless vibration testing and bridge deck damage identification using underneath maintenance walkway

Bridges will inevitably experience structural damage during long-term operation, which will decrease their structural load bearing capacity and durability. The vibration characteristics of bridge structures can be used as evaluation indicators for accurately diagnosing their deterioration. This study aimed at developing a vibration testing method for identifying bridge deck damage that does not affect traffic and is easy to implement. The vibration tests on the in-service bridge revealed it was mainly subjected to vertical vibrations, with the accelerations in the vertical direction about twice those in the horizontal direction. Based on fast Fourier transform analysis, the dominant vertical vibration frequencies were 2.34 Hz, 4.10 Hz, 10.30 Hz, 14.45 Hz and 18.75 Hz. A series of 0.1–0.3 Hz bandpass filters were used with those frequencies at the centers for modal analysis. The experimental results were then compared to the finite element analysis results. This study proposed a vibration-based damage identification method based on the differences between the bridge deck and both main steel girders. The study confirmed the convenience and effectiveness of using underneath maintenance walkways for wireless vibration testing.

Lorentz attractor excitation-based structural damage identification using state space curvature reconstruction- enhanced transformer

Abstract Vibration-based structural damage identification has been widely investigated. Different from previous studies that analyze vibrational responses in time and frequency domains, a new Lorentz attractor excitation-based damage identification is becoming a novel strategy with the advantage of capturing the structure’s nonlinear dynamic effects. In this study, Lorentz attractor-based chaotic signals were employed as excitation signals for the structural damage identification of a frame structure. Nonlinear responses were recorded and damages of bolt looseness at different locations were considered. The structural damages could be revealed in the state-space plot of the responses. A state space curvature reconstruction method was introduced to enhance the key features of the nonlinear responses. A small-sample damage identification is performed using a deep learning algorithm – a Transformer with an accuracy of 92.38%. The advantages of the proposed method over conventional deep learning algorithms were validated. The proposed method can be applied to health conditions identification of buildings, bridges, and trusses.

Structural damage identification by using physics-guided residual neural networks

In recent years, vibration-based structural damage identification has made significant progress by exploiting data-driven deep learning techniques, which can efficiently extract damage-sensitive features from a large amount of data. However, in some practical engineering applications, large volumes of measurement data are not readily available. This paper proposes a novel physics-guided residual neural network (PhyResNet) framework to improve the robustness and accuracy of structural damage identification under data-scarce conditions. In contrast to the state-of-the-art purely data-driven ResNet, the proposed method embedded available physics knowledge (e.g., governing equations of dynamics) of structures into the feature learning process via a novel physics-based loss function. The input-output relationship of the network is constrained to retain its physical meaning implicitly while the demand for large amounts of labeled training data is reduced. Notably, even with only 5 % of the dataset used for training, PhyResNet achieves a 13.1 % improvement in R-Value. The performance of the proposed approach is evaluated through both numerical and experimental verifications. Results demonstrate that damage localization and quantification are achieved with high accuracies and good robustness.

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.

Frequency response function-based closed-form expression for multi-damage quantification and its application on shear buildings

Vibration-based damage identification techniques have gained popularity immensely in the field of structural health monitoring (SHM). Frequency response function (FRF)-based damage quantification is mainly performed based on iterative model updating techniques. Most of these methods are time-consuming and erroneous in higher modes. Spectral element method (SEM) can overcome some of these limitations and can determine FRF expression directly for a broad class of civil structures. However, a closed-form expression for multiple damage severity is not proposed to date. In this study, an FRF-based formulation for quantifying damage has been proposed with the help of SEM in order to bypass the mode shape extraction for damage identification. To determine the effectiveness of the proposed formulation both numerical and experimental studies have been performed. A numerical simulation has been performed on a 14-storey shear building. Damage has been introduced by reducing the storey stiffness at intermediate and adjacent storeys. It has been perceived that damage severity at any storey can be estimated with only 3 sensors, one at the top storey and two others at the adjacent floors of the desired storey. To verify the effectiveness of the proposed expression, an experimental study has been carried out on a 9-storey miniature model. It has been observed that the first resonating peak amplitudes of the responses recorded simultaneously at desired locations of intact and damaged structures are sufficient to estimate the damage intensity. Thus, the proposed formulation is efficient as an output measurement-based approach. The novelty of this study lies in the proposed closed-form FRF-based multi-damage severity expression involving minimum number of sensors and without any information on the input excitation. The results obtained from both numerical and experimental study showcase the robustness, simplicity and applicability of the proposed formulation.

A New Approach to Structural Damage Identification Based on Power Spectral Density and Convolutional Neural Network

In the field of structural health monitoring, vibration-based damage identification remains a formidable challenge. Key to this challenge is the establishment of a reliable association between observed vibration characteristics and the actual state of structural damage (e.g. stiffness reduction). This association not only accurately indicates the presence of damage, but also the location and severity of the damage. To solve this complex pattern identification problem, a large number of approaches, including deep learning, have emerged in recent years. In this paper, we propose a new structural damage identification method that utilizes the vibration information of the structure and a convolutional neural network based on Alex NET improvement. The method consists of calculating the acceleration response power spectral density of damaged and undamaged structures under impact loading separately, and then making a difference between the two power spectral data, and subsequently introducing these power spectral difference data into the convolutional neural network for training. The use of power spectral density analysis as a preprocessing step converts the time-domain signals into frequency-domain signals, and this conversion allows the convolutional neural network to capture and learn from the specific frequency characteristics of the data, thus facilitating the learning process of the neural network model. In this paper, the effectiveness of the method is critically evaluated through numerical simulation and experimental validation, and 3% and 5% noise are added to the numerical study to test the robustness of the method. During the convolution neural network training process, the optimal training mean squared error (MSE) is [Formula: see text] in the case of no noise addition; the optimal training MSE is [Formula: see text] in the case of noise addition. Both the results of simulations and experiments confirm the high accuracy and good robustness of the method in localizing structural damage.

A comparative analysis of damage identification methods based on multi-channel data fusion and convolutional neural network for frame structures

Vibration-based damage identification methods are crucial for structural health monitoring. In recent years, deep learning methods, such as convolutional neural networks (CNNs), have been widely used for damage identification due to their superior performance. To enhance the accuracy of damage identification for complex frame structures and effectively utilize data measured from multiple sensors, this study proposes and compares four different damage identification methods based on CNN and four different kinds of multi-channel data fusion approaches, i.e., data splicing, matrix reconstruction, data dimension elevation, and sub-model integration. Firstly, the original one-dimensional vibration signals of the frame structures are measured. Then, multi-channel data fusion is performed on the signals to construct datasets used as input to train the CNN model. Finally, the sensitive damage features are automatically extracted using the trained CNN models after parameter tuning, and the structural damage patterns are identified. The effectiveness of these damage identification methods is evaluated using the numerical model of IASC-ASCE Benchmark structure, the Qatar University Grandstand Simulator test, and a three-layer stainless steel frame structure test. The efficiency of damage identification methods using multi-channel data is compared to those using only single-channel data. Additionally, the impact of noise on damage identification accuracy is also analyzed. The results demonstrate that the proposed four different damage identification methods based on CNN and multi-channel data fusion, outperform the methods that solely rely on single-channel data. Furthermore, these methods significantly enhance the accuracy of damage identification.

Reference-Free Vibration-Based Damage Identification Techniques for Bridge Structural Health Monitoring—A Critical Review and Perspective

Reference-Free Vibration-Based Damage Identification Techniques for Bridge Structural Health Monitoring—A Critical Review and Perspective

A novel surrogate-based crack identification method for cantilever beam based on the change of natural frequencies

A novel crack identification method is presented in this paper for cantilever beam-type structures within the scope of vibration-based damage identification. Natural frequencies are more easily to be measured yet are weakly sensitive to damage in comparison to other dynamic parameters such as mode shape and damping ratio. Therefore, this paper develops a surrogate-based crack identification method in order to overcome the low sensitivity of frequency to damage. The surrogate model was constructed using radial point interpolation method (RPIM) and subsequently trained with the frequency shift database established from parametric finite element analysis (FEA) modeling of cracked/ non-cracked cantilever beams. On this basis, the crack identification problem had been converted to a standard optimization model with crack length and location as design variables. Subsequently, genetic algorithm (GA) was employed for solving the optimization model to achieve crack parameters. In the end, numerical examples and experimental test have been conducted to validate the developed method, revealing maximum relative errors within 0.5% and 2.5%, respectively.

Improving damage detection by combining multiple classifiers in different feature spaces

Vibration-based damage identification methods have been explored in several engineering fields. However, the effective damage-sensitivity of vibration-based features and the balance between false alarms and readiness of the anomaly detection are still open issues. Here, a cost-effective damage detection methodology based on a Deterministically Generated Negative Selection Algorithm is implemented and tested over a wide dataset generated through the numerical simulation of a three-storey concrete building in different damage scenarios. The approach allows training the classifiers only based on undamaged samples and ensures a prompt response to damage outbreak by fusing the classification conducted in parallel on different feature spaces, exploiting their distinct sensitivity to damage. A strategy to improve the reliability of the alarm is implemented by introducing counters and thresholds on consecutive classification outputs. A satisfactory trade-off between false and true alarms is achieved by comparing their probabilities, obtained through Markov chains, for different values of such thresholds.

A Neural Network Approach to Estimate the Frequency of a Cantilever Beam with Random Multiple Damages.

Natural frequency is an important parameter in the structural health monitoring (SHM) system. Any changes in this parameter indicate structural alteration due to damage. This study provides a neural network (NN) solution as an alternative to the finite element (FE) method to measure the natural frequencies of a cantilever beam with random multiple damage. It is based on a statistical dataset of a free vibration test obtained from the APDL (Ansys parametric design language) simulation using a MATLAB (matrix laboratory) script. The script can generate an unlimited number of possible damage combinations for any given parameters with the help of the Monte Carlo (MC) technique. MC helps to generate a random number of damages in random locations at each simulation. Damage conditions are controlled by three parameters including damage severity and damage size (in terms of the mean and standard deviation of damage). Moreover, the method proposes a curve-fitting equation to validate the predicted natural frequency for the first three modes obtained from the neural network model. Both methods are in good agreement with each other, having minimal errors in the range of 0.2-3% for each mode. The frequency result shows that the beam frequency is 8.6486 Hz if the area reduction is 10%, whereas it comes down to 7.2338 Hz if there is a 30% area reduction. A two-level factorial test shows that damage severity is the most impactful factor compared to the damage sizes on the frequency shift event. This indicates that damage alters the composition of the beam and has an impact on its frequency change with the assumed damage parameters. Therefore, the proposed NN model can estimate the frequency shift for various damage scenarios. It can be utilized in the vibration-based damage identification process to predict the frequency changes of the damaged beam without any computational burden.

Baseline-free structural damage detection using PCA- Hilbert transform with limited sensors

Conventional vibration-based damage identification methods need a large number of sensors on the bridge to identify the structural modal information and rely on the response data of bridges under a non-damage state for comparison, which restricts their implementations in the health monitoring of bridge structures in service. This paper proposes a baseline-free structural damage detection method based on limited sensor information. The proposed method uses the Hilbert transform combined with principal component analysis (PCA) to locate the damage. Firstly, PCA is performed on the displacement response data matrix of the bridge under a moving load to obtain the principal component matrix. According to the physical interpretation of PCA of the responses of a beam-like bridge under a moving load, each column of the principal component matrix, namely the principal component, composes of a mode shape and the dynamic information at this modal frequency of the bridge. The dynamic information of the modal frequency is indirectly obtained by filtering out the mode shape by low pass filter. Then the Hilbert transform is used to process the dynamic component information to receive the instantaneous frequency which is regarded as the damage index. When the structure is damaged, the first instantaneous frequency at the damaged location changes abruptly. To demonstrate the effectiveness and performance of the proposed method, numerical simulations and experimental validations on beam bridge models are conducted. The results show that the method is free from the requirement of the information of bridges under the undamaged state, and only limited sensors are needed to locate the damage.

Railway track support condition assessment — Methodology validation using numerical simulations

A novel methodology to evaluate railway track support conditions is currently being developed. This methodology is based on modal analysis of the multi-element system composed by the railway infrastructure and an instrumented railway vehicle. It belongs to the group of vibration-based structural damage identification methods and is focused on observing the characteristic frequencies of this multi-element system, which can be related with changes in the physical properties of the railway infrastructure. As opposed to other vehicle-based track monitoring methods, the proposed methodology should enable condition assessment of the railway infrastructure subgrade, an element that is often neglected during railway monitoring operations. Since this methodology is vehicle-based, it can be used to assess the entire extension of a railway infrastructure to gather data regarding the support conditions of the track. It can also assess the evolution of track support conditions over time by comparing different rides over the same railway stretch. An important aspect of this methodology that still lacks validation is the topic of characterizing track support conditions of a railway infrastructure through its natural frequencies. The suitability of the theoretical model behind this methodology is another aspect that needs to be assessed. Numerical simulations tests using the multibody simulation software Simpack® are currently being performed to address both topics. This paper describes some of the simulations performed in this context, including a description of the numerical models used. The obtained results support the selected theoretical model and the overall validity of the proposed methodology.

Damage identification in beams using burst video-records during free-vibration

In Structural Health Monitoring, computer vision has emerged as a promising approach for dynamic measurement; especially for its full-field characteristic. However, real-world implementations are still sparse due to several reasons. One of them is that vibration-based damage identification methods usually rely on Output-Only Modal-Analysis which, to be accurate, requires very long records with stochastic excitation or relatively long free-vibration records. The first option hinders video taking, storage and processing; specially with high-speed cameras. The second is statistically difficult under common operating conditions. To overcome such difficulties, this paper presents a methodology for damage identification in beams; which is vibration-based and Output-Only, but does not rely on Modal (or other Fourier-based) Analysis. Therefore, it can be fed with very short free-vibration displacement time histories; e.g., extracted from burst video records. The key idea is using the recently developed Synthesis of the Healthy-Structure Model Response, but interpreting the accelerations in free-vibration as an inertial pseudo-loading. The likely problem of high levels of noise is addressed by separating damage detection and localisation (using a statistical approach) from damage quantification (which is physics based). In an experimental example, the methodology showed outstanding detection and localisation performance with low quantification error.

The Current Development of Structural Health Monitoring for Bridges: A Review

The health monitoring system of a bridge is an important guarantee for the safe operation of the bridge and has always been a research hotspot in the field of civil engineering. This paper reviews the latest progressions in bridge health monitoring over the past five years. This paper is organized according to the various links of the bridge health monitoring system. Firstly, the literature on monitoring technology is divided into two categories, sensor technology and computer vision technology, for review. Secondly, based on the obtained monitoring data, the data processing methods including preprocessing, noise reduction, and reconstruction are summarized. Then, the technical literature on abnormal data early warning systems is summarized. The recent advances in vibration-based and non-destructive testing-based damage identification methods are reviewed in the next section. Finally, the advantages and disadvantages of the existing research and the future research directions are summarized. This review aims to provide a clear framework and some reliable methods for future research.

Damage identification in steel frames using dual-criteria vibration-based damage detection method and artificial neural network

Vibration-based damage identification methods have proven to be effective in identifying damage in various structures by employing modal parameters. This paper presents and develops a two-stage multi-criteria damage detection method based on modified damage indices (DIs), specifically modal flexibility and modal strain energy-based damage indices, and artificial neural network (ANN) to locate and quantify damage in steel frames. Only information from the first three bending modes was used to calculate the DIs. The location of damages is detected using DIs in the first stage, and two separate artificial neural networks are trained based on the DIs as input parameters in the second stage. The feasibility and performance of the proposed method were assessed by applying different single and multiple damage scenarios to a 3D finite element model of a simply supported steel beam and a steel frame of an industrial shed. The results demonstrate the efficiency and competency of the proposed non-destructive method for damage detection in such steel frames.

Vibration-Based Structural Damage Identification Using P-CNN and Time-Frequency Hybrid Index under the Conditions of Uncertainties and Incomplete Measurements

The accuracy of vibration-based structural damage identification is affected by uncertainties in vibration measurements and finite element modeling. Moreover, a sufficient number of sensor measurements are not practical in large-scale structures. In this regard, the frequency domain [frequencies and mode shapes (FMS)] and time domain [acceleration cross-correlation function (ACCF)] indexes are put into a parallel convolutional neural network (P-CNN, a new convolutional neural network architecture with dual-channel) to locate and quantify structural damage. First, this approach is verified by a numerical model of a simply-supported beam, and the performance is evaluated by comparing it with methods using FMS and ACCF indexes input to the conventional 2D-CNN, respectively. The comparative results demonstrate that the proposed method can identify the damage with the lowest error, and the errors are less than 5%. In addition, three sparse measurement conditions are adopted to investigate the effect of the number of measuring points on damage identification. It is found that under the influence of uncertainties, even if only four sensors are used, this approach can identify damage accurately. Second, an engineering example of a continuous rigid frame bridge is adopted to validate the feasibility of the proposed method. The results show that when the number of sensors accounts for 65% and 47% of test sensors, respectively, it performs well in locating and quantifying structural damage. Therefore, this method can not only reduce the number of sensors and eliminate uncertainties effects, but also improve the performance of the 2D-CNN through information fusion and complementarity, which is also beneficial to minimize the cost of sensor layout in actual structures.

A review on recent developments in vibration-based damage identification methods for laminated composite structures: 2010–2022

Laminated composites received great attention in the structural applications due to innumerable advantages over conventional materials. These structures are subjected to environmental and mechanical loading conditions which leads to structural damage. The damage identification at early stage is crucial to overcome catastrophic failures, economic loss, human loss, and its disastrous consequences. Therefore, various structural damage identification techniques have been developed to identify the structural damages stages. These techniques provide the damage information about the systems, including structural health, serviceability, integrity, and safety. Over the past few decades, several review articles have been published on damage detection methods employing vibration characteristics for conventional and composite structures together. There has not been a study where complete concentration is focused on the laminated composite structures alone. This article concentrates on damage identification of laminated composite structures using vibration-based methods integrated with finite element methods, optimization techniques, signal processing methods, and machine learning algorithms over the past thirteen years. In addition, the limitations and applications of various methods have been discussed. This review article will provide the researchers and engineers an insight into the damage detection methods for laminated composite structures.

Damage identification for beam-like structures based on proper orthogonal modes of guided wavefields

Damage in structures can cause local anomalies in a guided wavefield due to reflection of guided waves in neighborhoods of damage. These local anomalies can be used for baseline-free damage identification if the structures are geometrically smooth and made of materials that have no stiffness and mass discontinuities. Recently, guided wavefield-based methods have been studied for damage identification by extracting and localizing local anomalies in guided wavefields in the time- and frequency–wavenumber domains. Meanwhile, proper orthogonal modes (POMs) obtained by the proper orthogonal decomposition have been studied for vibration-based damage identification. In this paper, the effectiveness of POMs of guided wavefields for damage identification in beam-like structures is studied. Since local anomalies in the POMs can be covered by global trends of the POMs, the continuous wavelet transform is used to suppress the global trends and intensify the local anomalies. The fundamental mechanism of how the continuous wavelet transform with Gaussian wavelet functions of a proper order can suppress the global trends of POMs and intensify local anomalies of POMs is explained. Significant POMs used for damage identification are determined by an adaptive truncation technique. The proper orders of the Gaussian wavelet functions, i.e., their number of vanishing moments, are determined based on the modal assurance criterion and a statistical criterion. The continuous wavelet transform of the significant POMs with Gaussian wavelet functions of the proper orders is used to yield an accumulative damage index. Numerical and experimental investigations of the proposed method are conducted on damaged beam-like structures. Their results verified that the proposed method is accurate and noise-robust for identifying the location and extent of damage in beam-like structures.

Meta-Model Assisted Continuous Vibration-Based Damage Identification of a Historical Rammed Earth Tower in the Alhambra Complex

ABSTRACT This work presents the development of a model-based online damage identification system for a 13th century rammed earth (RE) tower in the Alhambra, the Muhammad Tower. The system is fed with continuous data from an ambient vibration-based monitoring system and a meteorological station. Ambient vibrations are continuously processed through Operational Modal Analysis (OMA), and environmental effects are minimised via statistical pattern recognition. The normalized modal signatures are used to update the stiffness properties of certain parts of the tower through inverse model calibration. To do so, a high-fidelity three-dimensional finite element model (FEM) of the tower is developed. Since its computational burden precludes conducting online calibration, the FEM is bypassed by a light Kriging surrogate model (SM). In this light, the developed SM-assisted system identification constitutes a long-term Structural Health Monitoring (SHM) system outputting quasi-real-time series of modal properties and local stiffness parameters, so providing full damage assessment (detection, localization and quantification). The presented results refer to a time period of three months since January until March 2022. Numerical results and discussion are reported concerning the characterization and removal of environmental effects, and synthetic damage scenarios through non-linear simulations are used to validate the developed damage identification system.