Hybrid approach for two dimensional damage localization using piezoelectric smart aggregates

Knowledge of the damage location in composite structures is a necessary output for both Non-Destructive Evaluation (NDE) and Structural Health Monitoring (SHM). Although several damage localization approaches using a triangulation method and Time-of-Flight (ToF) of guided waves have been reported in literature, the damage localization technique is still not mature for composite structures with complex material properties, varying thickness and complex geometries. This paper investigates the development of a new approach for SHM and damage localization using a guided wave based active sensing system. In contrast to the traditional ellipse method, the proposed method does not require the information of structural thickness, ToF, or the estimation of group velocities of each guided wave mode at different propagation angles, which is one of the main limitations of most current ToF methodologies involving composites. This approach uses time-frequency analysis to calculate the difference of the ToF of the converted modes for each sensor signal. The damage location and the group velocity are obtained by solving a set of nonlinear equations. The proposed method can be used for composite structures with unknown lay-up and thickness. To validate the proposed method, experiments were conducted on both composite plates and stiffened composite panels. Eight piezoelectric (PZT) transducers were surface-bonded on each composite specimen and used in four pairs. The PZT transducers in each pair were bonded close to each other. In the PZT array, one PZT transducer from one PZT pair was used as the actuator and the other three pairs were used as sensors. A windowed cosine signal was used as the excitation signal. The locations of the delaminations in the composite specimens were validated using a flash thermography system. The accuracy of the proposed method in localizing delaminations was examined through comparison with the experimental measurements.

A Damage Localization Approach for Rahmen Bridge Based on Convolutional Neural Network

A signal energy change-based damage localization approach for beam structures

In this study, a reference-free damage characterization technique is developed not only to identify but also to locate and quantify damage in composite structures subject to varying temperature conditions. First, damage is characterized in terms of a damage index (m-value) defined as the ratio of damage size to the wavelength of the A0 mode within the damage. Then, a feasible solution space defining all possible combinations of the damage location and size are estimated without using any prior baseline data obtained from the pristine condition of a structure or different paths. When additional information such as the A0 mode group velocity within the pristine region of the structure becomes available, the estimates for the damage location and size are updated with better accuracy. The uniqueness of this study lies in that damage localization and quantification as well as identification are all performed without comparing current Lamb wave signals with the ones obtained from the pristine condition of the target structure, making the proposed technique more attractive for online monitoring. Numerical and experimental tests are presented to demonstrate the effectiveness of the proposed damage detection technique under varying temperature.

Structural health monitoring (SHM) is regarded as a very important field of development in engineering applications. Some benefits of the applications of a SHM system include the improvement of the security of the structure and the reduction of time and cost of its maintenance. Therefore, the service life of the structures can be lengthened. Besides, the implementation of a SHM system provides real time information about the current state of a structure, not only in civil applications but also in military applications, like aircraft, buildings, wind turbines and others. Gathering the maximum information about the current state of the structure to be diagnosed allows a better decision both in the preventive maintenance or the corrective maintenance. The information that can be gathered is the presence or not of damage, the size of damage or even the extent of damage. However, damage localization offers some very interesting possibilities since it allows to accurately determine where the damage is located thus saving time and effort. This work presents a damage detection and localization methodology by means of machine learning approaches which works by analyzing data from a piezoelectric sensor network attached to the structure under test. Validation of the methodology is performed by using an aluminum rectangular pipe with several damage positions

SUMMARY In this study, a data-driven approach for structural seismic damage detection and localization in a multiple-degree-of-freedom shear-type building structure subjected to strong ground motion is presented. The proposed method is based on the joint implementation of time–frequency analysis and fractal dimension (FD) characteristics. The approximate analytical wavelet transform is first used to obtain the time–frequency feature (TFF) of the transient response at the measured story. The TFF is defined as the real part of the wavelet coefficients. Next, the box-counting method is used to acquire the FD of the TFF within the fundamental frequency band. It is verified that the proposed FDs at all stories of the linear system are identical, whereas the FDs at the stories with nonlinearities will be different from those at the stories with linearity. Therefore, the nonlinearity of the structure caused by strong ground motion can be detected and localized by comparing the FDs at the measured stories. A numerical simulation on a 10-story shear-type building was conducted. The simulation results indicate that the aforementioned approach is capable of detecting and localizing single-location or multiple-location seismic damages in shear-type building structures under various seismic excitations and is robust to measurement noise. Finally, the sensor placement for this approach, the effect of damping ratio, and choice of interstory drifts or accelerations as the measured signals are investigated and discussed. Copyright © 2012 John Wiley & Sons, Ltd.

Bayesian‐optimized unsupervised learning approach for structural damage detection

Material defects localization in concrete plate-like structures – Experimental and numerical study

Based on the amplitude attribute analysis of ground penetrating radar (GPR) data, this paper applies GPR to the identification of internal damages and abnormal objects in stone walls of Tibetan ruins. The corresponding relationship between the characteristics of internal damages and abnormal objects and the radar data is explored through the Tibetan stone walls (TSWs) simulation test. The identification of location and size of the damages and abnormal objects in TSWs is realized based on the Mahalanobis distance abnormal data discrimination method. Using the normal distribution and the noncentral chi-square distribution, the identification law of types of the damages and abnormal objects is constructed, which takes root mean square (RMS) amplitude and interface reflection coefficient as the characteristic values. The field detection results of TSWs at three Tibetan sites are taken as an extension and supplement to the simulation results, and a set of identification atlas for the location, size and type of the internal damages and abnormal objects in TSWs is established. The application results of the identification atlas were verified by the field detection and the results show that the atlas has a high accuracy, which can significantly improve the efficiency of the identification, and can provide a basis for the performance research, protection and maintenance of TSWs.

Summary Anew damage identificationmethodbasedonFractal theoryandwaveletpacket transform is presented in the paper. The damage identification method utilize frequency-based domain message, time-based domain message and space based domain message effectively. NeXT method were first employed to reduce loading effect of random vibration measured from structures and then the vibration sinal were decomposed into the certain level wavelet packet components. For every time array of wavelet packet component then need to calculate the mean FD values as the ultimated FD values to reduce noiseeffect. The location of damage in the beam can be determined by the dramatic fluctuation appearing on contour of estimated FD and the extent of the damage can be estimated by FD-based damage index of δ. As a validation, the proposed method is applied to detect damage in a simply supported beam by numerical study. The successful detection of the damage in the beam demonstrates that the method is capable of assessing both the location and size of the damage. Noise stress testsare also carried out to demonstrate the robustness of the method under the influence of noise by considering all the information of every wavelet packet component synthetically.

Structural health monitoring (SHM) takes advantage of the recent advances in structural behavior monitoring to assess the performance of a structure, identifying the damage in the early stage. In this study, a carbon nanotube thread strain sensor was fabricated into a three-dimensional (3D) braided composite preform for the strain sensing of SHM. We present the results of an investigation on a carbon nanotube thread network in 3D composites for damage detection, in which the variation of the electrical resistance of the carbon nanotube thread embedded into 3D composites was recorded. Based on the variation of the electrical resistance, the location and size of damages were estimated by the response surface method, providing an effective solution for the estimation of the location and size of damages in 3D composites.

Damage localization in plate-like structures using time-varying feature and one-dimensional convolutional neural network

Bridges, crucial for surface transportation networks, carry continuous vehicle loads in varying weather conditions. Throughout the service life, bridges accumulate damage due to increased traffic, fatigue, harsh environmental conditions, and natural hazards. The accumulation of damage poses a potential fatality risk if not promptly monitored. Therefore, regular safety assessments and immediate post-disaster evaluations have become standard practice. In this context, the bridge weigh-in-motion (B-WIM) system emerges as a promising alternative to traditional bridge health monitoring (BHM) methods. B-WIM utilizes moving vehicle loads to estimate the bridge influence line (BIL), which provides critical structural information for effective BHM. A recent state-of-the-art review highlights ongoing research primarily focused on detecting and localizing damage along the span, rarely along the width, and with limited studies addressing damage quantification. However, localization and quantification of damage across the bridge width are seldom studied. This paper proposes an approach for novel localization of damage across the bridge width and quantification of damage using a relationship between flexural rigidity and deflection influence line (DIL). To achieve this, the concept of bridge influence surface (BIS), a 2D expansion of individual BILs across the bridge’s width, is utilized. Through a brief description of the B-WIM algorithm, this paper introduces the flexural rigidity estimation (FRE) method for damage quantification. In addition, a static-like DIL reconstruction is proposed to enhance robustness under dynamic bridge response. The proposed approach is initially verified with a 2D finite-element (FE) beam model, followed by a detailed simulation study using a full-scale 3D FE model of a real bridge. Results from comprehensive numerical analyses are further verified through experimental investigation on a scaled-down model of the same bridge. The findings demonstrate promising BIS-based damage localization across the width and FRE-based damage severity assessment. These insights contribute to the enhancement of BHM systems and outline potential avenues for future research.

A Multistage Elliptical Parametric (MEP) method is developed in this research for damage size evaluation in anisotropic composite laminates. The Lamb waves are actuated and sensed using piezoelectric (lead zirconate titanate, PZT) transducers arranged as a network of square cells. The dynamic response signals are processed using a continuous wavelet transformation (CWT) based on the Gabor wavelet for accurate time of flight (ToF) measurements. A numerical method is developed for the construction of non-elliptic path loci for each actuator-sensor pair. The damage is localized first using the least squares method, and then the damage edge points are located on each non-elliptic path loci as points which have the minimum distance from the damage location. The MEP method based on the damage edge points is implemented in multiple stages for damage size evaluation in a cross-ply laminate. Each stage is designed on the basis on excitation of specific prearranged transducers inside the square cell. The results indicate that the MEP method can quantitatively estimate the size of an elliptical damage in cross-ply laminates.

Damage detection and localization in civil or mechanical structures is a subject of active development and research. A few vibration‐based methods have been developed so far, requiring, for example, modal parameter estimates in the reference and damaged states of the investigated structure, and for localization in addition a finite element model. For structures in operation, temperature has been shown to be a major nuisance to the efficiency of such methods because the modal parameters are varying not only with damage but also due to temperature variations. For detection, a few rejection approaches have been developed. Besides the increased complexity, environmental variation is hardly taken into account in localization approaches. In this paper, we propose a sensitivity‐based correction of the identified modal parameters in the damaged state with respect to the temperature field in the reference state, on the basis of a sensitivity analysis with respect to temperature dependent Parameters of the finite element model in the reference state. The approach is then applied to the stochastic dynamic damage locating vector method, where its improved performance under nonuniform temperature variations is shown in a numerical application on a beam.