Damage Detection in Structures Using Strain Measurement

Damage detection in structural elements using curvature mode shape technique has become a research focus of increasing interest during the last few years. A noticeable deficiency of curvature mode shape, however, is its susceptibility to measurement noise, easily impairing its advantage of sensitivity to damage. To overcome this drawback, a nonlinear operator called Teager Energy Operator (TEO) is incorporated. The efficacy of TEO is analytically verified through modal curvatures in a steel reinforced concrete cantilever beam with an induced damage (stiffness loss) along its length. The applicability of the proposed curvature mode shape technique is experimentally validated for detecting simulated damage (mass attached) in an aluminum plate from mode shapes acquired by a non-contact Scanning Laser Doppler Vibrometer (SLDV). Normal responses (out of plane flexure response) are measured with SLDV. The excitation is given with the help of a data-physics exciter based on the frequencies obtained from the free vibration tests using a roving hammer attached with a forced transducer. The proposed algorithm is to compute a robust nonlinear operator - TEO using the attained curvature, dynamic rotation and displacement mode shapes. This algorithm has been successfully implemented and tested for detecting damages on beam and plate elements, using finite element simulations as well as laboratory experiments. It is anticipated that the suggested approach facilitates damage localization in a comparatively fast and accurate manner.

The use of modal curvatures for damage localization in beam-type structures

Vibration-based damage identification is a well-known method to support health monitoring of civil engineering structures. Damage in such structures can be identified by measuring changes of the natural frequencies, damping factors or modal displacements of the structure. However, this approach suffers from the low sensitivity of these natural frequencies and modal displacements to certain types of damage. Modal strains and curvatures can be more sensitive to local damage, but direct monitoring of these quantities with sufficient spatial resolution is not possible with current measurement techniques due to the very small strain levels (sub-microstrain) caused by ambient or operational excitation. To deal with this issue, we propose a novel mechanical transducer equipped with an optical fiber Bragg grating (FBG) sensor that enhances the sensitivity to strain with a factor larger than 30. The principle of operation of the transducer exploits a symmetric cantilever structure that enlarges the strain experienced by the FBG sensor compared to the strain applied to the transducer itself. We carried out dynamic and static tests to verify the ability of the strain-amplifying transducers to measure small-amplitude strain levels and to evidence the potential for carrying out FBG based modal strain measurements on concrete civil engineering structures.

Use of modal testing to identify damage on steel members

In this paper, we discuss the dynamics of beams with multiple delaminations. The objective of the study is to use the dynamic response of the delaminated beams in health monitoring of composite structures. As a first step, we present experimental results on the vibrations of a cantilever beam with multiple delaminations. These experiments are conducted by exciting the beam with piezoelectric actuators. The response is detected by using a high-speed video camera to study the existence of delamination opening modes in a beam with multiple delaminations. Then, an analysis of natural frequencies and mode shapes, for a delaminated beam, is presented by using analytical techniques. The measured dynamic responses of the beam with multiple delaminations are also presented. The experimentally detected nonlinear response is discussed. Introduction Delaminations or interlayer cracks and impact damages are typical defects observed in composite structures. The presence of these defects changes the dynamic characteristics of the structure . Some of the specific changes in the observed dynamic characteristics include changes in natural frequencies, changes in the mode shapes and changes in damping ratios. These changes can be used to identify the existence, location and magnitude of a delamination or an impact damage, before they can grow to their critical sizes. Studies, to date, are concerned with the dynamics of beams and plates with a single delamination. In this paper, we specifically discuss the structural dynamics of a beam with multiple delaminations. This information can be used to develop health-monitoring techniques for composite beams with multiple delaminations. In an earlier paper, Luo and Hanagud studied the effects of defects, like saw-cuts, impact damage and delamination, on the structural dynamic characteristics of composite beams. They have shown that perturbations in natural frequencies and modes of a beam, due to defects, can be used for health monitoring of structures. In particular, it has been shown that an integral equation that considers perturbations in curvature modes and natural frequencies can be very effectively used to detect local defects. To distinguish delaminations from other types of monitored (identified) defects Luo and Hanagud have discussed nonlinear effects like superharmonic response in the dynamic response of a harmonically excited delaminated beam. They considered only a single delamination. In this paper we study beams with multiple delaminations. More specifically, we discuss dynamic response and the changes in natural frequencies, mode shapes due to multiple delaminations in a composite beam. One of the earliest models for vibration analysis of composite beams with delaminations was proposed by Ramkumar et al. This model simply used four Timoshenko beams connected at the delamination edges to model a composite beam with one throughwidth delamination. The predicted frequencies, based on this model, were consistently lower than the results of experimental measurements. Wang et al. improved the analytical solution by including coupling between flexural and axial vibrations of the delaminated sublaminates. Using an isotropic beam with splits and the classical beam model, they found that the calculated natural frequencies were closer to experimental results. With similar considerations, Nagesh Gummadi and Hanagud formulated a finite element solution for arbitrary composite beams. In the finite element models, they considered a classical composite beam model as well as the beam model with high order shear deformations. Mujumdar and Suryanarayan have proposed a model which imposed a constraint between the delaminated sublaminates to force them to have the same flexural displacement. This model was unable to * Ph.D. Student, School of Aerospace Engineering † Professor, School of Aerospace Engineering, AIAA Member Copyright © 1999 The American Institute of Aeronautics and Astronautics Inc. All rights reserved

In this work, we present a two-step procedure for damage identification in beam structures exploiting modal curvature changes. The reconstruction of modal curvatures requires the knowledge of several mode shape components along the analyzed beam. This requirement is practically unachievable when mode shapes are identified via vibration-based monitoring using a limited number of accelerometers. To overcome this limitation, in the first step of the proposed procedure, we perform a mode shape expansion employing a reduced subset of measured modal components. The remaining measured components are used as control parameters to formulate a first hypothesis on damage location and extent. For this purpose, the expansion procedure is performed considering a number of possible damage scenarios, consisting of a location and a severity (loss of stiffness) of the damage. Using the Total modal assurance criterion (TMAC), we select the expanded modes with the highest degree of correspondence with the measured control components. These expanded modes are thus associated with a first guess of the damage location and severity. In the next step, this initial damage identification is verified through the computation of a modal curvature-based damage index. If the curvature-based damage identification confirms the previous identification, the damage location and extent are determined. The procedure can be easily extended to identify multiple simultaneously damaged elements. The approach is numerically validated using a benchmark beam modeled via finite elements, investigating the influence of different parameters such as noise, position of the control components and beam discretization on the identification success rate. Finally, the procedure is tested on two experimental specimens: a steel beam, with three different damage configurations and a concrete beam progressively damaged with multiple damage locations.

This paper presents an experimental study on the detection and localization of damage in a steel plate using vibration-based methods. Dynamic impulse tests were conducted on both undamaged and damaged plate to determine changes in modal parameters, such as eigenfrequencies and mode shapes. Several damage detection methods, including the Modal Assurance Criterion (MAC), Coordinate Modal Assurance Criterion (COMAC), Mode Shape Curvature (MSC), Mode Shape Curvature Square (MSCS), and Damage Index (DI), were applied. The results showed that while MAC and COMAC effectively detected damage, they did not provide information about the location. MSC and MSCS indices demonstrated improved damage localization, with the MSCS being particularly effective. The DI index provided the clearest and most accurate representation of the damage location. These findings highlight the potential of these methods for non-destructive testing and structural health monitoring.

Many composite materials and structures are susceptible to defects, which can significantly reduce the strength of structures and may grow to failure. To avoid the catastrophic failure of structures, development of a reliable method of structural health monitoring is one of the most important keys in maintaining the integrity and safety of structures. Dynamic response-based damage detection offers a simple procedure as an alternative to the conventional nondestructive evaluation techniques. However, this technique depends on the quality of measured data for its identification accuracy. In this article, experimental aspects of dynamic response-based damage detection technique on carbon/ epoxy composites are addressed. Smart piezoelectric materials are used as sensors or actuators to acquire the curvature modes of structures. These materials are surface-bonded to the beams. An impulse hammer is used as an actuating source as well. Four types of damage detection algorithms are evaluated for several possible damage configurations with two different excitation sources. The quality of damage identification with the four different detection algorithms is discussed. These experimental damage identification techniques using curvature modes and piezoelectric materials can be effectively used in damage detection and health monitoring of composite structures.

Damage detection for beam-like structures using the normalized curvature of a uniform load surface

Motivated by a need to reduce energy consumption in wireless sensors for vibration-based structural health monitoring (SHM) associated with data acquisition and transmission, this paper puts forth a novel approach for undertaking operational modal analysis (OMA) and damage localization relying on compressed vibrations measurements sampled at rates well below the Nyquist rate. Specifically, non-uniform deterministic sub-Nyquist multi-coset sampling of response acceleration signals in white noise excited linear structures is considered in conjunction with a power spectrum blind sampling/estimation technique which retrieves/samples the power spectral density matrix from arrays of sensors directly from the sub-Nyquist measurements (i.e., in the compressed domain) without signal reconstruction in the time-domain and without posing any signal sparsity conditions. The frequency domain decomposition algorithm is then applied to the power spectral density matrix to extract natural frequencies and mode shapes as a standard OMA step. Further, the modal strain energy index (MSEI) is considered for damage localization based on the mode shapes extracted directly from the compressed measurements. The effectiveness and accuracy of the proposed approach is numerically assessed by considering simulated vibration data pertaining to a white-noise excited simply supported beam in healthy and in 3 damaged states, contaminated with Gaussian white noise. Good accuracy is achieved in estimating mode shapes (quantified in terms of the modal assurance criterion) and natural frequencies from an array of 15 multi-coset devices sampling at a 70% slower than the Nyquist frequency rate for SNRs as low as 10db. Damage localization of equal level/quality is also achieved by the MSEI applied to mode shapes derived from noisy sub-Nyquist (70% compression) and Nyquist measurements for all damaged states considered. Overall, the furnished numerical results demonstrate that the herein considered sub-Nyquist sampling and multi-sensor power spectral density estimation techniques coupled with standard OMA and damage detection approaches can achieve effective SHM from significantly fewer noisy acceleration measurements.

Although much effort has been made to explore the characteristics of composite lattice truss core sandwich structures, little work has been undertaken into non-destructive evaluation (NDE) and structure health monitoring (SHM). This paper presents a baseline-free NDE method to determine the debonding of truss bar in composite lattice truss core sandwich structures. The method is proposed based on the uniform load surface (ULS) curvature and developed according to the combination of gapped smoothing method (GSM) and Teager energy operator (TEO), which is denoted as GSM-TEO method. The effects of truss bar on the vibration and wave motion characteristics of composite lattice truss core sandwich structures are firstly investigated, and then, experiment and numerical simulation are conducted to assess the performance of the proposed method. Results verify the method is reliable and applicable for damage localization in composite lattice truss core sandwich structures. doi: 10.12783/SHM2015/74

The advancement of machine learning algorithms has opened a wide scope for vibration-based SHM (Structural Health Monitoring). Vibration-based SHM is based on the fact that damage will alter the dynamic properties viz., structural response, frequencies, mode shapes, etc of the structure. The responses measured using sensors, which are high dimensional in nature, can be intelligently analyzed using machine learning techniques for damage assessment. Neural networks employing multilayer architectures are expressive models capable of capturing complex relationships between input-output pairs but do not account for uncertainty in network outputs. A BNN (Bayesian Neural Network) refers to extending standard networks with posterior inference. It is a neural network with a prior distribution on its weights. Deep learning architectures like CNN (Convolutional neural network) and LSTM(Long Short Term Memory) are good candidates for representation learning from high dimensional data. The advantage of using CNN over multi-layer neural networks is that they are good feature extractors as well as classifiers, which eliminates the need for generating hand-engineered features. LSTM networks are mainly used for sequence modeling. This paper presents both a Bayesian multi-layer perceptron and deep learning-based approach for damage detection and location identification in beam-like structures. Raw frequency response data simulated using finite element analysis is fed as the input of the network. As part of this, frequency response was generated for a series of simulations in the cantilever beam involving different damage scenarios. This case study shows the effectiveness of the above approaches to predict bending rigidity with an acceptable error rate.

This research proposed and verified an innovative method to identify and locate structural damage using only the response of operational vibration, that is the displacement acquired by a non-contact optical method. The most efficient and economical way to detect damage within the structure is to monitor its structural health while in operation. However, the uncertainties and the randomness of ambient vibrations due to the operation and environments cause a challenge in conducting the operational analysis. Current technology limits the ability to collect data on the properties of the structure without the interruption of operation. Frequencies and mode shapes have been widely used in structural damage detection, but they are not sensitive enough and cannot provide sufficient information for identifying damage locations and their quantification. Therefore, the goal of this research is to design and verify a method to detect the damage, as well as its location and severity, of structures in operation without any physical contacts for data acquisition (i.e., non-contact based structural health monitoring (SHM)). Three algorithms are integrated into this SHM process. The first algorithm is the determination of structural characteristics (frequencies and mode shapes) of a vibrating structure from output-only data. Stochastic Subspace Identification (SSI) method is applied to measured displacements over time to extract the structural characteristics. The second algorithm is to estimate the scaling factor. The mode shapes obtained from the output-only model analysis are unscaled due to the absence of the information of input excitation forces. Mass Change Modal Scale (McMS) algorithm is used to estimate the modal scaling factors and determine the scaled mode shapes. The third algorithm is to estimate the structural system matrices (i.e., mass and stiffness matrices) and assess the damages. A Finite Element Model Updating (FEMU) is applied and the system matrices are updated from frequencies and scaled mode shapes. The damage within the structure can then be detected by analysing changes in mass and stiffness matrices. All three phases are verified by numerical simulation and laboratory experiments with deflections acquired by non-contact optical methods through video system. At last, to achieve the non-contact based SHM, a modal scaling method based on temperature change is proposed and verified by numerical simulation. Experimental program reveals that the proposed algorithm using McMS method is applicable to detect damage locations and their mass losses. With proposed non-contacted based SHM, the limitations of contact based sensor can be addressed, and the structural damage

Fundamental mode shape and its derivatives in structural damage localization

Support vector machine (SVM) is a machine learning algorithm based on statistical learning theory, and it has recently been established as a powerful tool for classification and regression problems. This paper presents a novel SVM-based approach for damage location identification in beam-like structures. The curvature mode shapes are used as inputs of the SVM. The proposed approach involves two steps. The first step uses support vector classification to obtain the structural damage probability distribution. The second step uses support vector regression to identify the precise damage location after reconstructing the training set. Furthermore, a series of simulations in the cantilever beams involving different damage scenarios (at different location and different extent), have been conducted to verify this method. In order to check the robustness of the input used in the analysis and to simulate the experimental uncertainties, artificial random noise has been generated numerically and added to noise-free data during the training of the SVM. The results show that this approach is a promising method for damage diagnosis