Structural Damage Localization Research Articles

Research on structural damage identification and localization based on artificial neural network

Abstract Structural health monitoring is a research hotspot in engineering, and structural damage identification is one of the key problems in structural health monitoring research. This paper proposes a study on structural damage identification and localization based on artificial neural networks, and for the problem that the learning convergence speed of the BP neural network is too slow, a genetic algorithm is used to optimize the update of weights and thresholds during training and learning. In the finite element simulation, the structure's primary and secondary damage are taken as input nodes, and the optimized GA-BP neural network is used for training and identification. For the localization recognition of the primary damage of the structure, the maximum recognition relative errors of both the BP neural network and the GA-BP neural network did not exceed 5%, but the latter's accuracy was 2.63% higher than that of the former. For the localization recognition of secondary damage, the GA-BP neural network can effectively recognize 90% of the samples. The artificial neural network-based structural damage recognition localization has high recognition efficiency and accuracy, which is conducive to improving the robustness of the structural damage recognition system and is of significant help to real-time structural health monitoring.

The Wrinkles Characterization in GFRP Composites by Infrared Active Thermography.

An experimental study has been carried out to assess the effectiveness of infrared thermography in wrinkle detection in composite GFRP (Glass Fiber Reinforced Plastic) structures by infrared active thermography. Wrinkles in composite GFRP plates with different weave patterns (twill and satin) have been manufactured with the use of the vacuum bagging method. The different localization of defects in laminates has been taken into account. Transmission and reflection measurement techniques of active thermography have been verified and compared. The section of a turbine blade with a vertical axis of rotation containing post-manufacturing wrinkles has been prepared to verify active thermography measurement techniques in the real structure. In the turbine blade section, the influence of a gelcoat surface on the effectiveness of thermography damage detection has also been taken into account. Straightforward thermal parameters applied in structural health monitoring systems allow an effective damage detection method to be built. The transmission IRT setup allows not only for damage detection and localization in composite structures but also for accurate damage identification. The reflection IRT setup is convenient for damage detection systems coupled with nondestructive testing software. In considered cases, the type of fabric weave has negligible influence on the quality of damage detection results.

A rapid localization technique for detecting multiple damages in plate-like structures with unknown material properties

When using Lamb wave for damage detection in plate-like structures, factors such as unknown material properties, anisotropy and iterative operation for solving nonlinear equations make it difficult to accurately localize damages. This problem becomes even more difficult when multiple damages exist in the plate. To overcome these difficulties, a rapid technique for localizing multiple damages in plate-like structures without any prior knowledge of direction-dependent velocity profile is proposed in this paper. The time difference of arrival is obtained by performing cross-correlation processing on the damage features extracted from the received signal. Damage artifacts are removed using relative location determination and relative probability density analysis. Multi-damage localization at different locations were simulated. The proposed technique is validated experimentally for steel plates. Simulation and experimental results show that the proposed technique can realize multiple damage localization in plate-like structures with unknown material properties. With the help of L-shaped sensor clusters, damage locations for isotropic and anisotropic materials can be quickly predicted without solving nonlinear equations and without taking into account the dispersion effects. The results suggest that a suitable threshold range is set to 0.5–0.7 to remove damage artifacts.

Local wavenumber imaging with low-sampled wavefield for delamination characterization in composite plate

In this paper, a phase unwrapping-based local wavenumber imaging method is proposed for delamination detection in the composite plate. Local wavenumber imaging has shown significant advantages in damage localization and quantification. However, a dense of spatial measurements and a high requirement of sampling intervals are crucial for conventional spatial Fourier transform methods. To address these challenges, a phase unwrapping method adopting low-sampled wavefields for local wavenumber estimation is proposed for damage imaging. Specifically, the wavefield sampling process can divide the detection area into various grids while making each grid consist of four neighbor measurements. Under the far-field assumption, the wave path distances of grid measurements can be approximately obtained by projecting their real distances along the wave vector direction. Meanwhile, by revealing the phase differences of grid measurement pairs with the low-sampled wavefield data, the frequency-related wavenumbers are estimated subsequently. Hence, the local wavenumber can be recovered robustly by averaging the estimated wavenumbers from all measurement pairs in the grid. On this basis, the structural damage can be geometrically imaged and quantitatively evaluated. Both the simulation study and the experimental investigation have shown the efficiency of the phase unwrapping-based method for local wavenumber imaging, which demonstrates the strong capability of damage localization and quantification in composite structures with low-sampled wavefield.

Substructural damage identification using autoregressive moving average with exogenous inputs model and sparse regularization

Substructuring approaches possess many superiorities over traditional global approaches in damage identification because large-size global structures are replaced by small and manageable substructures. This paper proposes a substructural time series model for locating and quantifying the damage in complex systems. A substructural autoregressive moving average with exogenous inputs (ARMAX) model is established to extract the frequencies and mode shapes of substructures as indicators for damage detection. The detection of structural damage is essentially an inverse problem, and the damage in structure bears sparse properties. The inverse problem of substructural damage identification is efficiently solved via sparse regularization, and structural damage can be located and quantified through the nonzero terms in the solution vector. The accuracy of the proposed method is demonstrated by the numerical simulation of a frame structure and shaking table test of a shear building structure. As the substructural properties are more sensitive to local structural damage than the global properties, the substructural ARMAX model is quite accurate and efficient to be used in the damage identification of a complex system.

An experimental study of the saturated impulse for metal plates under slamming

Engineering structures can suffer severe local structural damage when subjected to extreme dynamic pulse loadings such as slamming impact. However, there is paucity of information on how the loading pulses affect the large plastic deformations. This paper presents information on an experimental investigation into the dynamic pressure pulse generated by slamming and the plastic response of metal plate. In our experiments, plates made of aluminum and steel alloy, were dropped from various heights ranging from 0.1 m to 1.3 m, onto the calm water in a tank. Based on the experimental results, the effects of structural material and water impact velocity on the slamming pressures pulse are investigated in detail. The results indicate that not only the peak pressure but also the pressure pulse duration play important roles in governing the dynamic response of the impacted structures. It is shown that the maximum strain and deflection at the plate center increase almost linearly with the drop height of the drop model. In view of the lack of previous experimental evidence for the saturation phenomenon of structures under slamming, we compared the dynamic response at the center of the plate with the experimentally measured slamming pressure under various drop heights, so that we have evidently verified the saturation phenomenon of flat plates for the first time under slamming in drop tests. It is also revealed that the dynamic response of the flat plates under slamming becomes more prone to the saturation phenomenon with the increase of drop height.

A new convolutional neural network-based framework and data construction method for structural damage identification considering sensor placement

In the application of data driven structural damage identification (SDI) based on supervised deep learning technology, valid data demarcation is the foundation; a convolutional neural network model with learning ability and capability of processing rich signal information is the core. Based on this understanding, this work makes three contributions: Firstly, the structural damage location and severity are jointly demarcated, and the SDI problem is transformed into a multi-classification task. Secondly, a 3D signal processing convolutional neural networks (3DS-CNN) is designed with an attempt to identify the complex and slight damages using the most basic network structure. Thirdly, a ‘major and subsidiary’ data construction (MSDC) method integrating the key intrinsic mode function is proposed to construct 3D data. Then the proposed schemes are verified by two different structures. The results show that the 3DS-CNN has excellent damage identification ability for small-size data with noise pollution. MSDC method can enrich the feature information of the damage signals and help the network with deep feature excavation, even if the vibration signals are heavily polluted. Going one step further, the impact of sensor placement is discussed, and it is found that when external excitation is obvious, better SDI accuracy can be achieved even using a single sensor signal with slight noise. When the noise interference is obvious, the generalization ability and noise robustness of the network can be enhanced by optimizing sensor placement. In this case, the sensor placement criteria and the sensitive nodes of the structure should be comprehensively and carefully considered to avoid mutual ‘coupling’ interference of data between sensors.

Closed-loop load vectors for structural damage localization

The null vectors of a rank-deficient transfer matrix perturbation contain information on the spatial distribution of the perturbation. This property constitutes the basis of the well-established Dynamic Damage-Locating Vector (DDLV) method, which facilitates localization of stiffness-related damage in dynamical systems. The DDLV method operates by applying the null vectors of the transfer matrix perturbation – now referred to as DDLVs – as loads to the considered system, whereby damage will be confined to the subdomain exhibiting rigid-body motion in steady state. In real systems, where disturbances will render the transfer matrix perturbation non-singular, the DDLVs must be extracted from a quasi-null space, and this will impair the contrast between undamaged and damaged subdomains. The present paper proposes a closed-loop formulation of the DDLV method to enhance the localization robustness by improving the conditioning of the linear map that infers the damage location. The closed-loop formulation, which is feasible for minimum-phase systems where open-loop input–output data can be collected prior and posterior to damage formation, is implemented virtually and does not, therefore, impose any practical overhead on the original DDLV method. Numerical examples demonstrate how the proposed closed-loop formulation can lead to significant damage localization improvement compared to the original method.

Perturbation approach for damage localization in beam-type structures: analytical, experimental and numerical exposition

ABSTRACT Structures are usually designed to undergo some yielding, cracking and damage during any catastrophic events like earthquakes. It is necessary to identify these damage locations to avoid the failure of the structure. In this study, a novel method of damage localization for a linear one-dimensional mathematical model of Euler-Bernoulli beam is developed. The damage is modeled as a normalized box-car function. The first-order perturbation in the form of a small change in the stiffness is introduced to the structure. This study employed an effective boxcar filtration technique in damage localization. The proposed formulation shows a distinct peak at the damage location. Further, a two-point roving technique is employed on the experimental model of an overhanging beam under impact loading to check the effectiveness of the proposed localization procedure under real measurement conditions. For its entirety, the finite element model for different end conditions through numerical simulations is also briefly addressed. It is observed that the results obtained from the experimental investigation and the simulation studies are in agreement with the proposed formulation. The proposed methodology does not consider the effect of noise, which can be addressed as the future scope of the present study.

Research on Vibration Fatigue Damage Locations of Offshore Oil and Gas Pipelines Based on the GA-Improved BP Neural Network

To study vibration fatigue damage localization of offshore oil and gas pipelines, aiming at the location error caused by uncertainty of the initial parameters in backpropagation (BP) neural network training, an improved BP neural network based on the genetic algorithm (GA) is proposed to locate pipeline damage. This approach was verified by experiments and simulations. First, a BP neural network for structural damage location was constructed, and a method to optimize the BP neural network parameters based on the GA was established. Then, a finite element model was established based on the measured data from modal tests of a physical pipeline model, and a large number of vibration neural network training samples were obtained using the finite element model. Finally, to show that the improved BP neural network based on the GA had a better damage location accuracy, the location results of the original BP and GA BP methods were compared for cases with no noise and with 5% noise. The results show that the average error of the BP neural network based on the GA was less than 3%, which was 11.6% lower than that of the original BP neural network.

Output-only structural identification with random decrement technique

Most of existing time domain methods fail to identify the unknown structural parameter in the ambient condition, because the force location is unknown and the number of unknown forces is usually larger than the number of sensors. A novel output-only structural identification method based on the random decrement (RD) technique and covariance matrix adaptation evolutionary strategy (CMA-ES) is presented. The RD functions of a structure under ambient excitation are extracted and treated as free vibration responses for structural parameter identification. The unknown structural parameters are identified by minimizing the discrepancy between the measured and calculated RD functions with the CMA-ES. Unlike traditional output-only methods, which require the estimation of the input excitation in the process, the proposed method does not require the identification of the input forces, thereby significantly reducing the computational cost. Numerical studies on two- and three-dimensional trusses and experimental studies on a steel frame are presented to demonstrate the accuracy and efficiency of the proposed approach. The effects of measurement noise and measurement duration on the identified results are examined. Results show that the proposed method can obtain satisfactory results even with 20% measurement noise and structural damage less than 10% can be detected. Comparison studies with the genetic algorithm (GA) and particle swarm optimization (PSO) are presented to verify the computational efficiency of the proposed method. Results also show that the proposed CMA-ES has a faster convergence speed than the GA and PSO method. The results demonstrate that the proposed technique can be used to detect damage severity and location in structures under ambient conditions.

Damage localization for composite structure using guided wave signals with Gramian angular field image coding and convolutional neural networks

Accurate detection of damage in composite structures is of great significance to ensure safe service and avoid catastrophic accidents. In this paper, a novel damage diagnosis method, integrating guided wave (GW)-based structural health monitoring (SHM) with Gramian angular field (GAF) image coding and convolutional neural networks (CNNs), is proposed to improve the accuracy of damage localization in composite structures. Firstly, an improved piecewise aggregate approximation (PAA) algorithm is proposed to achieve guided wave data compression and obtain a series of damage indexes (DIs) characterizing the damage for effectively improving the localization accuracy. Then, the multi-path fused DI sequences are converted into two-dimensional (2-D) images using GAF to take full advantage of the significant benefits of CNN in machine vision. Secondly, a CNN model is constructed to learn high-level feature representations and conduct damage location regression. Finally, the proposed method is tested and assessed on measured GW signal datasets with the addition of zero-mean Gaussian noise. The experimental results show that the mean relative error of the proposed method for damage localization on the invisible datasets is 3.59%. The proposed method demonstrates better generalization and localization performance than other selected state-of-the-art damage localization techniques.

A transfer learning approach for acoustic emission zonal localization on steel plate-like structure using numerical simulation and unsupervised domain adaptation

The detection and localization of damage in metallic structures using acoustic emission (AE) monitoring and artificial intelligence technology such as deep learning has been widely studied. However, a current challenge of this approach is the difficulty of obtaining sufficient labeled historical AE signals for the training process of deep learning models. This problem can be approached through the implementation of transfer learning. The innovation of this paper lies in the development of a transfer learning approach for AE source localization on a stainless-steel structure when no historical labeled AE signals are available for training. A finite element model is developed to generate numerical AE signals for the training. Unsupervised domain adaptation (UDA) technology is utilized to reduce the distribution difference between the numerical and the realistic AE signals and to derive the localization results of the unlabeled realistic AE signals. The results suggest that the proposed approach is capable of localizing AE signals with high accuracy in the absence of labeled training data.

Towards a deep learning-based unified approach for structural damage detection, localisation and quantification

Ultrasonic guided waves have been extensively employed for characterising structural damage thanks to their sensitivity to defects. Although they are easy to excite and acquire, heavy processing is often required to extract single-valued indicators of damage presence, or damage indices, from the acquired signals. Traditionally, damage indices have been elaborated through tomographic algorithms to generate damage probability maps, even though limitations affect the performance of such approach. Recently, the potentialities of machine learning have been leveraged to improve the accuracy of frameworks processing guided waves for damage diagnosis. However, most methods still require extracting damage indices from the acquired signals, which may bring to loss of diagnostic information and reduced accuracy. Furthermore, damage position and extent are usually roughly estimated through classification, while regression should be employed instead. In this context, this work aims (i) to test the capabilities of different supervised machine learning algorithms to localise and quantify damage through regression and (ii) to carry out a critical discussion about possible limitations of using damage indices instead of unprocessed signals. Results are compared to identify which algorithm performs better and if machine learning can improve the accuracy of damage diagnosis compared to traditional imaging methods. An experimentally validated numerical case study was used to test the capabilities of the proposed machine learning-based framework and to bring evidence of the accuracy of the algorithms involved to characterise damage with properties not seen during training.

A time-reversal assisted probabilistic fusion strategy for damage localization in plate-like structures using Lamb waves

Precise damage identification and quantification is of great significance to ensure structural performance and provide early-warning for safety maintenance. In this research, a time-reversal assisted probabilistic strategy is developed to accurately localize damage via baseline-free manner in plate-like structures by fusing damage prediction data obtained from the elliptical trajectory location method (ETLM) and reconstruction algorithm for probabilistic inspection of defects (RAPID) algorithm. Damage features, including time difference for ETLM and correlation coefficient index (CCI) for RAPID algorithm, are extracted from the time-reversal focusing signal using the Hilbert transform (HT). To make full use of the damage-related information contained in the time-reversal focusing signals and improve the localizing accuracy, a decision-level data fusion based on Bayesian inference is proposed to fuse damage features and reconstruct damage information. A series of numerical and experimental studies, including different configurations of damage cases and transducer distributions, were conducted to investigate the performances of the proposed time-reversal assisted probabilistic method on damage localization. Results shows that the proposed method could successfully achieve accurate damage localization via baseline-free manner and significantly reduce the damage artifacts compared to traditional methods.

Structural Nonlinear Damage Identification Method Based on the Kullback–Leibler Distance of Time Domain Model Residuals

Under external load excitation, damage such as breathing cracks and bolt loosening will cause structural time domain acceleration to have nonlinear features. To solve the problem of time domain nonlinear damage identification, a damage identification method based on the Kullback–Leibler (KL) distance of time domain model residuals is proposed in this paper. First, an autoregressive (AR) model order was selected using the autocorrelation function (ACF) and Akaike information criterion (AIC). Then, an AR model was obtained based on the structural acceleration response time series, and the AR model residual was extracted. Finally, the KL distance was used as a damage indicator to judge the structural damage source location. The effectiveness of the proposed method was verified by using a multi-story, multi-span stand model experiment and a simulated eight-story shear structure. The results show that the proposed structural nonlinear damage identification method can effectively distinguish the structural damage location of multi-degree-of-freedom shear structures and complex stand structures, and it is robust enough to detect environmental noise and small damage.

A global-local damage localization and quantification approach in composite structures using ultrasonic guided waves and active infrared thermography

The paper emphasizes an effective quantification of hidden damage in composite structures using ultrasonic guided wave (GW) propagation-based structural health monitoring (SHM) and an artificial neural network (ANN) based active infrared thermography (IRT) analysis. In recent years, there has been increased interest in using a global-local approach for damage localization purposes. The global approach is mainly used in identifying the damage, while the local approach is quantifying. This paper presents a proof-of-study to use such a global-local approach in damage localization and quantification. The main novelties in this paper are the implementation of an improved SHM GW algorithm to localize the damages, a new pixel-based confusion matrix to quantify the size of the damage threshold, and a newly developed IRT-ANN algorithm to validate the damage quantification. From the SHM methodology, it is realized that only three sensors are sufficient to localize the damage, and an ANN- IRT imaging algorithm with only five hidden neurons in quantifying the damage. The robust SHM methods effectively identified, localized, and quantified the different damage dimensions against the non-destructive testing-IRT method in different composite structures.

Structural robustness analysis of RC frames under seismic and blast chained loads scenarios

In this paper the structural robustness assessment of concrete frame buildings under blast and under earthquake blast hazard chain scenarios is investigated. A deterministic methodology for connecting the robustness with the blast hazard intensity and for conducting the robustness analysis under earthquake-triggered blast is presented and applied to a 3D RC frame building by implementing nonlinear time history analyses considering both plastic behavior and large displacements. A preliminary sensitivity analysis on a 2D frame is conducted to identify the critical analysis parameters influencing the results. The robustness curves (residual structural capacity versus the level of damage occurring in the structure), evaluated both for the blast-only and for the earthquake-blast chained cases, are compared by considering different explosion locations inside the building (location of the blast-induced structural damage). Results show that neglecting the chained load scenarios would lead to the identification of an erroneous location as critical for the structural robustness performance.

An automated vibration-based structural damage localization strategy using filter-type feature selection

Damage detection techniques have been widely explored over the last years driven by the advances of computational intelligence technologies. To understand the structure’s dynamic behavior, some studies use modal-based methods, which are traditionally applied to reflect changes caused by damage. More recently, strategies using raw vibration measurements have gained notoriety and are proving to be a promising approach. In this context, the aim of this paper is to present a novel automated methodology for damage localization based on the extraction of features from dynamic data using domain knowledge and on an unsupervised filtering process. Feature extraction is performed simultaneously in time, frequency, and quefrency domains to diversify information retrieval. In machine learning (ML), this filtering procedure is called feature selection (FS) and is applied herein with the goal to decrease redundancy and increase the relevance of the feature set by eliminating a portion of it in view of a predefined criterion. The main concept is that the proposed method can customize its features concerning the structure, providing generality about any type of geometry, material, and excitation it encounters. The damage-sensitive index is computed through an outlier analysis, where percentile intervals are created based on the filtered features from the structure’s healthy state. Thus, any anomalies surrounding this initial state should be automatically located. The method was successfully tested in five applications, two of them being full-scale bridges, which shows a promising performance for real-world monitoring situations. Some of its significant achievements are single and multiple damage location, structural reinforcement detection, quantification of severity in progressive damage scenarios, and robustness to noise due to varying traffic and/or environmental conditions. Such a reliability is tied to an efficient default parameters setup, enabling an automated damage localization algorithm.

A chaos game Optimization-based model updating technique for structural damage identification under incomplete noisy measurements and temperature variations

Dealing with structural damage identification (SDI) considering limited noise-contaminated measurements and varying environmental conditions is still a challenge due to the difficulties regarding the localization and quantification of structural damage. If the challenging issue is not considered in SDI process, the SDI techniques may become unreliable and have limitations in their implementation in in-service structures. In this regard, the article proposes an optimization-based model updating technique for SDI taking into consideration both incomplete noisy measurements and temperature variations. First, it is assumed in the finite element model of the monitored structure that the relation between temperature conditions and structural materials is temperature-dependent to synthetically generate damage-induced vibration data under varying environmental temperatures. Then, the inverse SDI problem is recast into a constrained optimization problem and a newly developed algorithm called Chaos Game Optimization (CGO) is adopted for solving this problem with high efficiency. The efficiency of the CGO algorithm is assessed by comparison with other four parameter-less optimization algorithms. Finally, three engineering numerical examples are conducted to demonstrate the performance of the proposed technique. The results of the numerical investigations indicated that for the monitored structures made of metallic material (i.e., steel and aluminum), the proposed technique can accurately localize and quantify single and multi-damage even in incomplete and noisy modal data and environmental temperature changes. Meanwhile, for the monitored structures made of concrete material, the ambient temperature variations significantly affect the damage detectability and localizability of the proposed technique.