Vibration-based Damage Detection Research Articles

Locating and Quantifying Damage in Beam-like Structures Using Modal Flexibility-based Deflection Changes

This paper presents an enhanced method to locate and quantify damage in beam-like structures using changes in deflections estimated from modal flexibility (MF) matrices. The method is developed from explicit relationship between a series of MF-based deflection change vectors and the damage characteristics. Based on this, three damage locating criteria are defined and used to detect and locate damage. Once the damage is located, its severity is estimated conveniently from a closed-form function. The capability of the proposed method is examined through numerical and experimental verifications on a steel beam model. The result shows that the method accurately locates and quantifies damage under various scenarios using a few modes of vibration, with satisfactory or even better results compared to those obtained from traditional static deflection-based method. The performance of the proposed method is also compared with three well-known vibration-based damage detection methods using changes in MF and modal strain energy. It is found that the proposed method outperformed the other three methods, especially for multiple damage cases. As beams can represent various structural components, the proposed method provides a promising damage identification tool targeting the application to real-life structures.

Experimental modelling of a top-tensioned riser for vibration-based damage detection

Damage incidents in subsea risers may lead to catastrophic economic, environmental and human safety consequences. Therefore, early detection of damage is of paramount importance. Vibration-based damage detection methods have been extensively studied in the past for damage detection in a vast variety of structural types, however, their application to risers has hardly been explored to date. In the limited existing literature of vibration-based damage detection in risers, the proposed methods have mostly been verified by numerical studies or by experimental models which lacked realistic representations of ambient excitations and similarity of the model to a full-scale structure. This paper presents, for the first time, experimental modelling of a top-tensioned riser at a laboratory scale for vibration-based damage detection studies, in which the similarity of the experimental model to a full-scale structure, the multifaceted effects of damage and the dynamic ambience are explicitly addressed. An experimental riser model is built and subjected to random wave loading in a flume according to the JONSWAP spectrum to simulate the ambient excitations realistically. The experimental model consists of a tensioned rod made of acetal plastic and several small lumped steel masses attached to the rod such that they reduce the natural frequencies of the model to the range of wave frequencies that can be generated in the flume. An analysis is conducted to account for the similarity between the experimental model and a full-scale structure with the priority given to the modal characteristics of the structures. Corrosion is considered as the cause of damage and it is simulated non-destructively on the experimental model by partial removal of the attached lumped mass at the assumed damage location and reduction of applied tension load. The amount of removed mass and reduced tension load are calculated such that the simulated damage on the experimental model is equivalent to an actual cross-sectional area loss due to corrosion damage in the full-scale structure. An application of a vibration-based damage detection method is demonstrated using experimental data obtained for several different damage scenarios. An auto-regressive (AR) model is fitted to the acceleration response of the riser and coefficients of the AR model are used as the damage sensitive features (DSFs). Damage detection is achieved by conducting outlier analysis on the DSFs.

Damage detection in girder bridges using modal curvatures gapped smoothing method and Convolutional Neural Network: Application to Bo Nghi bridge

This paper addresses a damage detection method based on changes in modal curvature combined with Convolutional Neural Network (CNN). The use of modal curvature for damage detection is very well known. Some methods require modal data of healthy structures as a reference, others do not. Gapped Smoothing Method (GSM) is a vibration-based damage detection methodology, which makes use of modal curvature for identifying the location of structural damage and does not require information of the intact structure. The Bo Nghi bridge is used as an illustrative example. This bridge consists of four T-shaped concrete simply supported girders. One single beam with the same length and cross-section of the bridge girder is modeled and used to extract numerical data to train the CNN. A CNN is trained by using images from the damage index of the GSM to classify the damage location in the numerical beam model. Finally, the finite element model of the bridge is built and used to model damage scenarios to test the trained CNN. The results indicate that the combination of GSM and CNN can be used for damage detection and localization.

Vibration-based damage detection for bridges by deep convolutional denoising autoencoder

One of the main challenges for structural damage detection using monitoring data is to acquire features that are sensitive to damages but insensitive to noise (e.g. sensor measurement noise) as well as environmental and operational effects (e.g. temperature effect). Inspired by the capabilities of deep learning methods in representation learning, various deep neural networks have been developed to obtain effective damage features from raw vibration data. However, most of the available deep neural networks are supervised, resulting in practical difficulties owing to the lack of damage labels. This article proposes a damage detection strategy based on an unsupervised deep neural network, referred to as deep convolutional denoising autoencoder, which accepts multi-dimensional cross-correlation functions as input. The strategy aims to extract damage features from field measurements of undamaged structures under the influence of noise and temperature uncertainties. In the proposed strategy, cross-correlation functions of vibration data are first calculated as basic features; then deep convolutional denoising autoencoder is developed to reconstruct cross-correlation functions from their noise-corrupted versions to extract desired features; exponentially weighted moving average control charts are finally established for these features to identify minor structural damages. The strategy is evaluated through a numerical simply supported beam model and an experimental continuous beam model. The mechanism of deep convolutional denoising autoencoder to extract damage features is interpreted by visualizing feature maps of convolutional layers in the encoder. It is found that these layers perform rough estimations of modal properties and preserve the damage information as the general trend of these properties in multiple extra frequency bands. The results show that the proposed strategy is competent for structural damage detection under the exposed environment and worth further exploring its capabilities in applications of real bridges.

On the functional model–based method for vibration-based robust damage detection: versions and experimental assessment

The problem of random vibration–based robust damage detection for structures operating under varying and non-measurable environmental and operating conditions is considered via a novel unsupervised functional model–based method. Two versions of the method are employed based on either the residual variance or uncorrelatedness (whiteness) of a proper functional model that incorporates the varying environmental and operating conditions in a scheduling vector. This article constitutes a proof-of-concept study in which a comprehensive laboratory assessment of the functional model–based method is undertaken using hundreds of experiments with a composite tail structure of an unmanned aerial vehicle and two early-stage damages under a considerable number of different environmental and operating conditions. Comparisons with two alternative state-of-the-art statistical time series type methods, that is, a multiple model–based method and a principal component analysis–based method, are also performed. The results indicate ideal detection performance for the functional model–based and multiple model–based methods, with the true positive rate reaching 100% at 0% false positive rate, but degraded performance for the pricipal component analysis–based method.

VIBRATION-BASED STRUCTURAL DAMAGE DETECTION TECHNIQUES: A REVIEW

Damage in infrastructure can be as a result of its degenerating state under service loads or after exposure to impact loads such as earthquakes. Early damage detection is essential to preventing failure and ensure the integrity and safety of structures. Damages lead to changes in the geometric and material properties like mass, stiffness, and damping, and influences the response behavior of the structure. It has been proven that vibration-based damage detection technique is an efficient means of damage identification and assessing structural integrity. This review article examines conventional vibration-based damage detection techniques. It highlights the importance of early damage detection as a means of ensuring infrastructural safety, reliability and maintenance. Damage detection techniques like the time domain methods, frequency domain and modal domain methods have been developed and constantly evolving to meet the existing challenge of identifying structural damages. The practical application is still minimal, hence more research works are necessary for damage detection in large civil engineering structures.

Damage identification in frame structures using high‐speed digital image correlation and local modal filtration

This paper presents a technique for vibration-based damage detection and localization in engineering structures based on the high-speed three-dimensional Digital Image Correlation (HS 3D-DIC) and local modal filtration. The efficacy of the proposed procedure is demonstrated on a frame structure with different severities of localized damage. The full-field vibration data acquired under the white noise excitation provided the input to the damage identification algorithm. The spatial density of sensors on a test structure is one of the critical factors in the accurate localization of damage. Hence, the use of vision-based vibration measurement methods, such as the Digital Image Correlation (DIC) is very advantageous as it provides full-field quantification of dynamic displacement fields in three dimensions. Both numerical simulations with the use of Finite Element (FE) code and experimental tests with the use of high-speed digital cameras were performed to confirm the validity of the proposed approach. The research confirmed that it is feasible to use the proposed approach to detect and localize damage in a frame structure under random vibration excitation with a localization accuracy of a few percent of the field of view dimensions. On the basis of the obtained results, we believe that there is a great practical potential of this approach when applied to real-life engineering structures.

Application of the Subspace-Based Methods in Health Monitoring of Civil Structures: A Systematic Review and Meta-Analysis

A large number of research studies in structural health monitoring (SHM) have presented, extended, and used subspace system identification. However, there is a lack of research on systematic literature reviews and surveys of studies in this field. Therefore, the current study is undertaken to systematically review the literature published on the development and application of subspace system identification methods. In this regard, major databases in SHM, including Scopus, Google Scholar, and Web of Science, have been selected and preferred reporting items for systematic reviews and meta-analyses (PRISMA) has been applied to ensure complete and transparent reporting of systematic reviews. Along this line, the presented review addresses the available studies that employed subspace-based techniques in the vibration-based damage detection (VDD) of civil structures. The selected papers in this review were categorized into authors, publication year, name of journal, applied techniques, research objectives, research gap, proposed solutions and models, and findings. This study can assist practitioners and academicians for better condition assessment of structures and to gain insight into the literature.

Sparse Bayesian learning for structural damage detection under varying temperature conditions

Structural damage detection inevitably entails uncertainties, such as measurement noise and modelling errors. The existence of uncertainties may cause incorrect damage detection results. In addition, varying environmental conditions, especially temperature, may have a more significant effect on structural responses than structural damage does. Neglecting the temperature effects may make reliable damage detection difficult. In this study, a new vibration based damage detection technique that simultaneously considers the uncertainties and varying temperature conditions is developed in the sparse Bayesian learning framework. The structural vibration properties are treated as the function of both the damage parameter and varying temperature. The temperature effects on the vibration properties are incorporated into the Bayesian model updating on the basis of the quantitative relation between temperature and natural frequencies. The structural damage parameter and associated hyper-parameters are then solved through the iterative expectation–maximization technique. An experimental frame is utilized to demonstrate the effectiveness of the proposed damage detection method. The sparse damage is located and quantified correctly by considering the varying temperature conditions.

Methods to enhance the automation of operational modal analysis

The vibration-based damage detection and the monitoring of modal data are currently based on different Operational Modal Analysis (OMA) approaches. For the continuous monitoring of modal quantities, different techniques for automated feature extraction are known. Especially in recent years several research groups and companies have been working on the automatic interpretation of stability plots. Nevertheless, many questions regarding data pre-processing for OMA in time or frequency domain are still unanswered. The present paper deals with issues regarding effective pre-processing methods for OMA based on Covariance-Stochastic Subspace Identification. In this context, the orthogonality of matrices after model order reduction, etc. are referred. This includes, for example, a comparison between the classical calculation of the reduced-order matrices and a procedure that preserves the orthogonality of these matrices. A method known from the signal denoising and image processing is also successful used to extract and select the modes. The mode extraction method is validated with an innovative three-dimensional stability plot. This paper does not claim to solve all tasks of an automated OMA, but it contributes the calculation of clean, easy to interpret, stability plots, which should facilitate the automatic evaluation in the future. The effectiveness of the algorithms is demonstrated by means of simulated (3DOF-StateSpace) and measured data of a laboratory structure described in [1]. Afterwards the results and the future works on the topic are discussed.

Temperature Effects on Vibration-Based Damage Detection of a Reinforced Concrete Slab

To study the variations in modal properties of a reinforced concrete (RC) slab (such as natural frequencies, mode shapes and damping ratios) under the influence of ambient temperature, a laboratory RC slab is monitored for over a year, the simple linear regression (LR) and autoregressive with exogenous input (ARX) models between temperature and frequencies are established and validated, and a damage identification based on particle swarm optimization (PSO) is utilized to detect the assumed damage considering temperature effects. Firstly, the vibration testing is performed for one year and the variations of natural frequencies, mode shapes and damping ratios under different ambient temperatures are analyzed. The obtained results show that the change of ambient temperature causes a major change of natural frequencies, which, on the contrary, has little effect on damping ratios and modal shapes. Secondly, based on a theoretical derivation analysis of natural frequency, the models are determined from experimental data on the healthy structure, and the functional relationship between temperature and elastic modulus is obtained. Based on the monitoring data, the LR model and ARX model between structural elastic modulus and ambient temperature are acquired, which can be used as the baseline of future damage identification. Finally, the established ARX model is validated based on a PSO algorithm and new data from the assumed 5% uniform damage and 10% uniform damage are compared with the models. If the eigenfrequency exceeds the certain confidence interval of the ARX model, there is probably another cause that drives the eigenfrequency variations, such as structural damage. Based on the constructed ARX model, the assumed damage is identified accurately.

Application of wavelet transform in structural health monitoring

Structural health monitoring (SHM) is a process of implementing a damage detection strategy in existing structures to evaluate their condition to ensure safety. The changes in the material, geometric and/or structural properties affect structural responses, which can be captured and analyzed for condition assessment. Various vibration-based damage detection algorithms have been developed in the past few decades. Among them, wavelet transform (WT) gained popularity as an efficient method of signal processing to build a framework to identify modal properties and detect damage in structures. This article presents the state-of-the-art implementation of various WT tools in SHM with a focus on civil structures. The unique features and limitations of WT, and a comparison of WT and other signal processing methods, are further discussed. The comprehensive literature review in this study will help interested researchers to investigate the use of WT in SHM to meet their specific needs.

Vibration‐Based Damage Detection of a Steel‐Concrete Composite Slab Using Non‐Model‐Based and Model‐Based Methods

This paper presents vibration‐based damage detection (VBDD) for testing a steel‐concrete composite bridge deck in a laboratory using both model‐based and non‐model‐based methods. Damage that appears on a composite bridge deck may occur either in the service condition or in the loading condition. To verify the efficiency of the dynamic test methods for assessing different damage scenarios, two defect cases were designed in the service condition by removing the connection bolts along half of a steel girder and replacing the boundary conditions, while three damage cases were introduced in the loading condition by increasing the applied load. A static test and a multiple reference impact test (MRIT) were conducted in each case to obtain the corresponding deflection and modal data. For the non‐model‐based method, modal flexibility and modal flexibility displacement (MFD) were used to detect the location and extent of the damage. The test results showed that the appearance and location of the damage in defect cases and loading conditions can be successfully identified by the MFD values. A finite element (FE) model was rationally selected to represent the dynamic characteristics of the physical model, while four highly sensitive physical parameters were rationally selected using sensitivity analysis. The model updating technique was used to assess the condition of the whole deck in the service condition, including the boundary conditions, connectors, and slab. Using damage functions, Strand7 software was used to conduct FE analysis coupled with the MATLAB application programming interface to update multiple physical parameters. Of the three different FE models used to simulate the behavior of the composite slab, the calculated MFD of the shell‐solid FE model was almost identical to the test results, indicating that the performance of the tested composite structure could be accurately predicted by this type of FE model.

Numerical verification of accelerometer-based assessment of hollow-type pretensioned concrete girder

In this paper, a vibration-based damage detection method was employed to assess a hollow-type prestressed concrete (BS12) girder in two different states using its dynamic characteristics. Once, the girder was assessed in as-built condition presenting undamaged sate. Next, the evaluation was carried out after the performance of four-point bending test that caused various cracks in the tension part of the girder. Despite that, change in physical parameter was minor due to high strength materials used in the girder and the cracks appeared were in negligible range. Yet, the method was able to demonstrate a detectable change in modal parameter (notably natural frequency) of the girder which is the function of physical properties. The aim of the study was to validate the preciseness of accelerometer-based method. To this purpose, the test was conducted three times for the initial intact and its following damaged states to have an accurate recorded response of the specimen. The measured data were compared with that acquired numerically using Finite-Element Method (FEM) Midas civil software. This paper concluded that even a minor damage can cause detectable shifts in modal parameters of the structure by presenting the well-matched results of both experimental and numerical methods of pre-damage and post-damage states of the girder.

Application of vibration-based damage detection algorithms to experimental data from multi-storey steel structures

Recently, there has been remarkable progress in the development of data acquisition systems and statistical damage detection algorithms in the field of vibration-based structural health monitoring (SHM). However, many of them have not been validated by experimental data. This paper presents and analyzes a shake table experiment of two three-story steel structures. The structural damage was introduced in a systematic and controlled way and the structures were tested with different levels of earthquakes. Three types of analyses were conducted. First, a wireless sensor system designed for SHM, SnowFort, was demonstrated to have the same accuracy as the conventional wired sensors. Second, the rotation algorithm is applied to estimate residual displacements in the structures and achieves excellent agreements with the displacement sensor measurements. At last, a continuous wavelet transform-based damage detector is applied to the experimental data. The results highlight that the wavelet model parameters are sensitive to the damage state.

Application of vibration-based damage detection algorithms to experimental data from multi-storey steel structures

Recently, there has been remarkable progress in the development of data acquisition systems and statistical damage detection algorithms in the field of vibration-based structural health monitoring (SHM). However, many of them have not been validated by experimental data. This paper presents and analyzes a shake table experiment of two three-story steel structures. The structural damage was introduced in a systematic and controlled way and the structures were tested with different levels of earthquakes. Three types of analyses were conducted. First, a wireless sensor system designed for SHM, SnowFort, was demonstrated to have the same accuracy as the conventional wired sensors. Second, the rotation algorithm is applied to estimate residual displacements in the structures and achieves excellent agreements with the displacement sensor measurements. At last, a continuous wavelet transform-based damage detector is applied to the experimental data. The results highlight that the wavelet model parameters are sensitive to the damage state.

Data‐driven models for temperature distribution effects on natural frequencies and thermal prestress modeling

In vibration-based damage detection, it is fundamentally important to distinguish between changes in the dynamics of a structure and those related to environmental effects. Modal properties of structures are widely used as damage-sensitive features and indicate changes in physical properties due to structural damage or degradation. However, civil structures such as bridges are influenced by various operational and environmental conditions (such as traffic, wind, humidity, and, most importantly, temperature), and these variables also alter the modal properties of structures. Changes in modal properties caused by environmental effects can mask the effect of structural damage, resulting in unreliable structural damage assessments. It is thus fundamentally important to understand the relationship between environmental effects and modal properties. In this paper, a full-scale finite element (FE) model is used in a simulation-based study to investigate the effects of temperature distribution on natural frequencies. Two approaches for modeling the temperature effects on natural frequencies (thermal prestress imposed at an element level and temperature-dependent Young's modulus) are implemented in the FE model, and results are compared. Then, temperature profiles of the model are hypothetically simulated. These simulation results are used to develop a linear model and the three most popular machine learning models to reproduce the effects of temperature distribution on natural frequencies. Finally, a comprehensive comparison is made between the performances of these data-driven models.

Non-probabilistic method to consider uncertainties in frequency response function for vibration-based damage detection using Artificial Neural Network

Artificial neural network (ANN) has become a popular computational approach in the field of vibration-based damage detection, based on its ability to relate the nonlinear relationship between structural vibration characteristics and damage information. In the meantime, frequency response function (FRF) estimation has been proven effective as a dynamic parameter for damage detection due to its prevention of information leakage. In this regard, FRF is chosen as the input variable for ANN to detect structural damage in this study. However, the main concern in damage detection using FRF with ANN is the size of FRF data. A full-size FRF data will result in a wide composition range of the ANN input layer, thus affecting the iteration divergence in the network training process and resulting in the computational inefficiency. In most applications, principal component analysis (PCA) has been used to reduce the size of the FRF data before being fed to an ANN model. However, as the structures become more complex, the FRF data size also increases. The large size of FRF data may result in the PCA not being effective in selecting important information from the actual FRF data, leading to false damage detection. Moreover, the existence of uncertainties from modelling error and measurement error may also amplify the error in damage detection. Hence, this study proposes a combination of a non-probabilistic method with PCA to consider the problem of the existing uncertainties and the inefficiency of using FRF data in ANN-based damage detection. In this study, ANN is used to relate the FRF data to a damage feature. The input data for the network are the compressed real FRFs and the outputs are the elemental stiffness parameter (ESP). The compressed FRF data obtained from PCA provide a new damage index (DI) that is used as the input layer of the ANN. Based on the interval analysis method, the uncertainties in the new DI are considered to bind together to obtain the interval bound (lower and upper bounds) of the DI changes. The possibility of damage existence (PoDE) is designed to ascertain the relationship between the input and output parameters in the form of undamaged and damaged conditions. The verification conducted on a numerical model and a laboratory tested steel truss bridge model demonstrated that the proposed method is efficient in dealing with uncertainties using FRF for damage detection.

Vibration Based Damage Detection in Plate-Like Structure Using Square of Mode Shape Curvature

In this current work, a new methodology based on the square of mode shape curvature (SMSC) is presented that relates the mode shapes and its curvature changes before and after the damage for localization and sizing of the surface crack in plate-type structures. The significance of this method is it has the capability to portray accurate shape and exact location of the surface crack in a plate-like structure which are related to low and high elastic modes on dense and coarse measurement grids. The efficiency of the proposed SMSC is examined using experimental and numerical data acquired from modal analysis on the aluminum plate containing single and multi-surface cracks with a fixed-free condition using non-contact measurement a scanning laser vibrometer and on simple finite element plate model. As evidence of experimental and numerical study results, highly accurate crack characterization has been attained through the proposed method. In implementing this method, only a few modes of the structure are required. Further, the impact of the mode order on the effectiveness of crack detection, boundary distortion treatment, and grid density analysis was also performed by the proposed method.

Damage detection in a suspension bridge using modal flexibility method

Advances in structural engineering and material technology have resulted in increasing use of suspension bridges in infrastructure systems to satisfy the modern transportation requirements. However, during the long life of a suspension bridge, its main cables and hangers could suffer severe corrosion and fatigue leading to failure of at least a part of the bridge. There is a need for a simple and reliable procedure to detect and locate such damage at its onset so that appropriate retrofitting can be carried out to prevent the failure of these important structural elements. Current Structural Health Monitoring (SHM) systems are integrated with a variety of damage detection methods, which are global and local in nature. Limitations in local methods necessitate the non-destructive and global techniques for damage diagnosis leading to continuous development in vibration based damage detection (VBDD) methods. In this context, this paper develops and applies mode shape component specific damage indices namely; vertical damage index (DIV) and lateral damage index (DIL) based on the Modal Flexibility (MF) concept to detect and locate damage in the main cables and hangers of a suspension bridge. The application of these damage indices is illustrated under single, multiple and complex damage scenarios. Effects of noise in the modal data and higher order vibration modes on the damage detection capability are also captured. Results confirm the applicability of the proposed vertical damage index DIV to accurately detect damage in real suspension bridges using only the first few modes. Bridge engineers can use the proposed technique to monitor the health of suspension bridges and prevent their unexpected failure.