Vibration-based Damage Identification Research Articles

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.

Investigation into vibration-based structural damage identification and amplitude-dependent damping ratio of reinforced concrete bridge deck slab under different loading states

Investigation into vibration-based structural damage identification and amplitude-dependent damping ratio of reinforced concrete bridge deck slab under different loading states

Temperature Effect on Vibration Properties and Vibration-Based Damage Identification of Bridge Structures: A Literature Review

In civil engineering structures, modal changes produced by environmental conditions, especially temperature, can be equivalent to or greater than the ones produced by damage. Therefore, it is necessary to distinguish the variations in structural properties caused by environmental changes from those caused by structural damages. In this paper, we present a review of the technical literature concerning variations in the vibration properties of civil structures under varying temperature conditions and damage identification methods for bridge structures. First, the literature on the effect of temperature on vibration properties is roughly divided into experimental and theoretical studies. According to the classification of theoretical research methods, the progress in research on the probability analysis method, the artificial intelligence method, and the optimization algorithm method in this field is reviewed. Based on the different methods of experimental research employed in this field, the experimental research is reviewed according to qualitative and quantitative analyses. Then, damage identification methods for bridge structures are reviewed, considering data-based and model-based methods. Finally, different research methods are summarized.

Deep-Learning-Based Drive-by Damage Detection System for Railway Bridges

With the ever-increasing number of well-aged bridges carrying traffic loads beyond their intended design capacity, there is an urgency to find reliable and efficient means of monitoring structural safety and integrity. Among different attempts, vibration-based indirect damage identification systems have shown great promise in providing real-time information on the state of bridge damage. The fundamental principle in an indirect vibration-based damage identification system is to extract bridge damage signatures from on-board measurements, which also embody vibration signatures from the vehicle and road/rail profile and can be contaminated due to varying environmental and operational conditions. This study presents a numerical feasibility study of a novel data-driven damage detection system using train-borne signals while passing over a bridge with the speed of traffic. For this purpose, a deep Convolutional Neural Network is optimised, trained and tested to detect damage using a simulated acceleration response on a nominal RC4 power car passing over a 15 m simply supported reinforced concrete railway bridge. A 2D train–track interaction model is used to simulate train-borne acceleration signals. Bayesian Optimisation is used to optimise the architecture of the deep learning algorithm. The damage detection algorithm was tested on 18 damage scenarios (different severity levels and locations) and has shown great accuracy in detecting damage under varying speeds, rail irregularities and noise, hence provides promise in transforming the future of railway bridge damage identification systems.

Vibration-based damage identification in composite plates using 3D-DIC and wavelet analysis

High-speed three-dimensional digital image correlation (3D-DIC) techniques can acquire full-field vibration responses under a single excitation, which is not restricted by the number of degrees of freedom (DOFs) in measurement compared with accelerometers and laser Doppler vibrometers (LDVs). Therefore, 3D-DIC exhibits compelling capacity in dynamic testing, especially for nondestructive damage detection. Based on experimental modal analysis (EMA), this study presents a damage detection method without using a baseline model, which combines the advantages of 3D-DIC with two-dimensional continuous wavelet transform (2D-CWT) for damage detection and recognition. The carbon fiber reinforced plastic (CFRP) composite plates with prefabricated artificial damages, including spatial notches and local crack, were investigated. The mode shapes of damaged CFRP composite plates were obtained by singular value decomposition (SVD) from the matrix of frequency response functions (FRFs). Then the mode shapes were analyzed by using wavelet transform, and the damage index was derived from the wavelet coefficients. For comparison, two other damage indices, namely mode shape curvature and polynomial fitting difference, were also derived. It is found that 3D-DIC could provide different measurement DOFs in line with the damage form for post-processing; and the Mexican-hat wavelet analysis can accurately detect the location and size of damage by suppressing the effects of measurement noise.