Structural Damage Monitoring Research Articles

Advanced Damage Monitoring in Beam Structures Using Grey Wolf Optimizer and Additional Masses

Dynamic characteristics are of significant interest to researchers in the field of damage detection. Among these, natural frequencies stand out due to their high accuracy and resistance to noise. However, relying solely on natural frequencies is often insufficient for determining the depth and location of damage. To address this limitation, additional masses can be strategically placed at different locations on structural elements, altering the natural frequencies. Each mass placement creates a distinct dynamic scenario with a unique frequency profile, enabling a more comprehensive analysis. In this study, additional masses were introduced at specific elements of the beam structure within the numerical model which were then strategically placed at various locations along the beam. The resulting shifts in natural frequencies served as inputs to the Grey Wolf Optimizer (GWO), which identified elements with stiffness reductions indicative of damage. A custom MATLAB code was developed to perform finite element analysis on the numerical model. The results were validated against previously published experimental data, demonstrating the method’s reliability with a 5% difference. A parametric study involving both simple and continuous span beams was performed. The procedure effectively detected damage severities of 10%, 25%, and 50%, with corresponding errors of 4.3%, 0.44%, and 0.02%, respectively.

Damage detection in 2D beams using wavelet and boundary elements

Damage detection in structures is crucial to prevent the collapse of structural elements. Research suggests using numerical methods to aid in damage detection in structures, typically based on finite element modeling (FEM) and comparisons of modal responses of structures before and after damage. This article proposes a new approach to identify damage using Wavelet Transform (WT) and Boundary Element Method (BEM), these methods can detect singularities present in displacement parameters caused by damage and, consequently, do not require the condition of the structure before damage. To validate the methodology, a simply supported and bi-supported beam with damage will be modeled using the open-source programs Bemlab2d and Bemcracker2d, along with an algorithm in Matlab language for manipulation of wavelet coefficients and damage localization. The results indicate that WT is a useful tool for identification and monitoring of structural damage.

Study on Acoustic Emission Characteristics and Damage Mechanism of Wind Turbine Blade Main Spar with Different Defects.

This paper aimed to understand the AE signal characteristics and damage mechanism of wind turbine blade main spar materials with different defects during the damage evolution process. According to the typical delamination and wrinkle defects in wind turbine blades, the GFRP composite with defects is artificially prefabricated. Through acoustic emission experiments, the mechanical properties and acoustic emission characteristic trends of wind turbine blade main spar composites with different defects under tensile loading conditions were analyzed, and the damage evolution mechanism of different defects was explained according to the microscopic results. The results show that the existence of artificial defects will not only affect the mechanical properties of composite materials but also affect the damage evolution process of the materials. The size and location of delamination defects and the different aspect ratio of the wrinkle defects have a certain influence on the damage mechanism of the material. K-means cluster analysis of AE parameters identified the damage models of GFRP composites. The types of damage modes of delamination defects and wrinkle defects are the same, and the range of characteristic frequency is roughly the same. This study has important reference significance for structural damage monitoring and damage evolution research of wind turbine blade composites.

A vine-copulas based multi-sensor fusion structural damage monitoring method and its application in dam engineering

As a civil engineer that is susceptible to the mechanism of slow and continuous deterioration, the dam must detect damage according to the static data of the arranged sensors, which is an important issue in structural health monitoring. Due to the characteristics of huge solid structures, damage with a slight degree and small range usually does not cause significant changes in the monitoring data of a single sensor. Therefore, it is difficult to effectively detect local damage by traditional monitoring methods that establish operating warning values for a single sensor. Currently, there have been some approaches to fuse sensor information that consider correlations between multiple sensors as well as the overall operational behavior of the dam. However, their objectives are focused on improving the accuracy of response variable prediction models without addressing the local damage identification aspect. In this study, we apply the Vine-Copula model for the first time in a multi-sensor information fusion dam damage detection method based on static monitoring data. In this way, it improves the sensitivity of local damage identification for huge solid structures. A prediction model is fitted with the measuring data of each sensor to obtain the residual series. Then the joint probability distribution of the multidimensional residuals is acquired by constructing the Vine-Copula model. Finally, the joint cumulative distribution function value is used as an alarm limit to determine whether the structure has an abnormal damage condition. The effectiveness and accuracy of the proposed method were demonstrated in both a numerical arch dam simulating local damage and an actual rockfill dam engineering with cracks at the crest.

Fatigue Damage Monitoring of Composite Structures Based on Lamb Wave Propagation and Multi-Feature Fusion

To address the challenges associated with fatigue damage monitoring in load-bearing composite structures, we developed a method that utilizes Lamb wave propagation and partial least squares regression (PLSR) for effective monitoring. Initially, we extracted diverse characteristics from both the time and frequency domains of the Lamb wave signal to capture the essence of the damage. Subsequently, we constructed a PLSR model, leveraging Lamb wave multi-feature fusion, specifically tailored for in-service fatigue damage monitoring. The efficacy of our proposed approach in quantitatively monitoring fatigue damage was thoroughly validated through rigorous standard fatigue tests. In practical applications, our model effectively mitigated the impact of multicollinearity among feature variables on model accuracy. Furthermore, the PLSR model demonstrated superior accuracy compared to the PCR model, given an equal number of principal components. To strike a harmonious balance between efficiency and precision, we optimized the size of the feature variable. The results show that the optimized PLSR model achieved an R-squared value exceeding 97% in predicting the in-service damage area. This underscores the robustness and reliability of our method in accurately monitoring fatigue damage in load-bearing composite structures.

Multi-source dynamic adaptive domain generalization network for crack detection under unknown temperature environment

The dispersion characteristics of Lamb waves are more complex under variable temperature environments, leading to deviation in signal distribution, which significantly reduces the performance of damage detection models. Domain adaptation methods are often applied to damage detection problems with differences in data distribution, but this method requires obtaining the distribution information of the target temperature domain in advance and cannot achieve online damage monitoring under unknown temperature environments. Therefore, this article proposes a multi-source dynamic adaptive generalization network (MDAGN) for crack detection under unknown temperature environments. This network can extend the damage detection model to damage detection tasks under unknown temperatures. The main idea is to optimize the discriminative boundaries of the feature space while mining the generalized damage features and specificity in the temperature domain. Design specific regressors for each source temperature domain to maintain the specificity of the temperature domain, and then fuse the damage detection results of multiple regressors through similarity measurement, enabling the model to extrapolate to temperatures outside the distribution and achieve domain generalization. This article collected structural damage data under unknown temperature environments to verify the effectiveness of this method. The results indicate that this method can effectively monitor cracks under unknown temperature environments, establishing an effective framework for structural damage monitoring under unknown working conditions.

A novel damage localization technique for type III composite pressure vessels based on guided wave mode-matching method

Damage monitoring during the service life of Type III composite overwrapped pressure vessels (COPVs) is crucial for ensuring their safe operation. This paper focuses on the damage localization in COPVs using ultrasonic guided waves structural health monitoring (SHM) techniques. Firstly, dispersion curves were plotted to establish a foundation for experiments and simulations based on the propagation theory of guided waves in multilayered anisotropic structures. Subsequently, a 3D finite element model of COPV was constructed to capture the propagation characteristics of guided waves within the COPV and their interaction with damage. The effects of different fiber angles and fiber layer numbers on guided waves propagation were analyzed, and their interrelationships were established. Furthermore, the “Wave Velocity Directionality” effect of the A0 mode was identified during its propagation in COPV. The monitoring signals obtained from experiments were analyzed to assess the impact of damage on the time-domain and frequency-domain signals of guided waves. Finally, a damage localization algorithm based on mode-matching was proposed, and its localization accuracy was verified in COPVs with different damage locations and fiber layer numbers. The results demonstrate the significant potential of the proposed damage localization algorithm in the damage monitoring of COPV structures.

Acoustic Emission Monitoring of Bond Deterioration in GFRP-Reinforced Concrete Members: An Entropy-Based Approach

The primary objective of acoustic emission (AE) monitoring is to accurately detect imminent critical damage, preventing premature failure by analysing the dynamic evolution of AE parameters. This study introduces AE entropy, a distinctive parameter derived from Shannon's entropy, employed to monitor the bond degradation between glass fibre-reinforced polymer (GFRP) rebars and the concrete interface in GFRP-reinforced concrete elements. Characteristic AE parameters have been synchronously collected during flexural bond tests, and information entropy and weighted information entropy, composed of typical AE characteristic parameters based on Shannon’s information entropy theory in the time domain, have been defined. Findings indicate that, with increasing bond stress, the specimens exhibit a trend characterized by "decreasing, stabilizing, and then increasing" in both AE characteristic parameters and weighted information entropy. The information entropy value shows fluctuations during any damage associated with GFRP-concrete bond deterioration. At peak bond stress values, information entropy values experience a rapid surge, indicating significant deterioration. The evolution process of debonding between GFRP rebar and concrete, switching between a chaotic state that develops randomly and an organized state that produces in order, follows specific laws (disordered–ordered–disordered), corresponding well to AE characteristic parameters weighted information entropy. Moreover, entropy values, both individual and weighted, when correlated with bond stress and slip variations in the test specimens, distinctly outline and predict various stress transfer mechanisms between the GFRP bar and concrete. In summary, this research underscores the utility of AE entropy as a valuable tool for damage monitoring in GFRP-reinforced structures. Its ability to capture microstructural deformations positions AE entropy as a promising candidate for improving critical damage identification and preventing premature failure

Cumulative second harmonic Lamb wave generation and propagation in composite laminates: an insight into the influence of material anisotropy and damping

Significant strides have been taken in the structural design of fibrous composites in recent decades. Early detection of material degradation and damage in such materials is crucial for various industrial applications, yet poses challenges. Second harmonic Lamb waves have demonstrated their superior sensitivity to microstructural defects, making them an effective tool for structural health monitoring (SHM). However, understanding of the second harmonic Lamb wave generation and propagation in composite laminates remains inadequate due to the complexities introduced by material anisotropy and damping, which impede the practicality of SHM applications. This study investigates the cumulative second harmonic Lamb waves in composite laminates at low frequencies, considering both material anisotropy and damping. Theoretical analyses were carried out to explore how anisotropy and damping affect the generation and propagation of second harmonic Lamb waves. A nonlinear material constitutive model was proposed. It was used to conduct numerical simulations that verified the properties of second harmonic Lamb wave generation and propagation at different wave propagation angles, with a focus on the cumulative effect. Experiments were carried out to further validate the properties of the second harmonic Lamb waves in a carbon fiber reinforced panel. The findings indicate that the Lamb waves propagating at 0° showcase exceptional cumulative effect, rendering them suitable for SHM applications. Additionally, material damping induces a cumulative second harmonic Lamb wave with an initial increase followed by a decrease, creating a predictable “sweet” zone of larger amplitude that is easy to measure for SHM applications. The elucidation of the generation and propagation characteristics of the second harmonic Lamb waves provides guidance for structural damage detection and monitoring applications.

A high-performance, sensitive, low-cost LIG/PDMS strain sensor for impact damage monitoring and localization in composite structures

Health monitoring of composite structures in aircraft is critical, as these structures are commonly utilized in weight-sensitive areas and innovative designs that directly impact flight safety and reliability. Traditional monitoring methods have limitations in monitoring area, strain limit, and signal processing. In this paper, a multifunctional sensor has been developed using acid-treated laser-induced graphene (A-LIG) with a multi-layer three-dimensional conductive network. Compared to untreated laser-induced graphene, the sensitivity of A-LIG sensor is increased by 100%. Furthermore, PDMS is used to fill the pores, which improves the fatigue performance of the A-LIG sensor. To obtain clear monitoring results, a data conversion algorithm is provided to convert the electrical signal obtained by the sensor into a strain field contour cloud map. The impact test of the A-LIG/PDMS sensor on the carbon fiber panel of the aircraft wing box segment verifies the effectiveness of its strain sensing. This work introduces a novel approach to fabricating flexible sensors with improved sensitivity, extended strain range, and cost-effectiveness. The sensor exhibits high sensitivity (gauge factor, GF ≈ 387), is low hysteresis (∼53 ms), and has a wide working range (up to 47%), and a highly stable and reproducible response over multiple test cycles (>18 000) with good switching response. It presents a promising and innovative direction for utilizing flexible sensors in the field of aircraft structural health monitoring.

Study on the truss damage measurement based on Bayesian updating

Bayesian updating can integrate new observations in real time. This online updating property makes the Bayesian approach particularly suitable for dynamic monitoring of structural damage. In this paper, the node displacements are obtained by the finite element software OpenSees, assuming that the bars are damaged and the stiffness is reduced. Rejection Sampling method is used to determine the stiffness drop interval. During the sampling process, different load sizes, load locations, errors and types are selected to realize the updating of the stiffness of the top chord bar, bottom chord bar, vertical web bar, and diagonal web bar. A Pratt-type truss is used as an example, combined with displacement data. The proposed stiffness damage measurement method is demonstrated by updating the stiffness of four types of bars separately. The results shows that damage in structures can be accurately identified by Bayesian updating methods. This method not only pinpoints the location of the damage, but also quantifies the extent of the damage.

Peri-Ultrasound Modeling to Investigate the Performance of Different Nonlinear Ultrasonic Techniques for Damage Monitoring in Plate Structures

Abstract Peri-ultrasound modeling which is based on nonlocal peridynamics is found and proven to be effective for modeling nonlinear waves propagating and interacting with damages in structures. This work presents the peri-ultrasound modeling to investigate the performance of three commonly used nonlinear ultrasonic (NLU) techniques—wave mixing, higher harmonic generation (HHG), and sideband peak count-index (or SPC-I) for monitoring damages (or cracks) in three-dimensional (3D) plate structures. Cracks can be defined as “thin cracks” and “thick cracks” according to the horizon size mentioned in peridynamics. Peri-ultrasound modeling results reveal that the SPC-I results are consistent with other reported numerical modeling and experimental results available in the literature. However, the modulation indicator (MI) from the wave mixing model only shows consistent trends for thin cracks but not for thick cracks and its reliability is affected by the initial excitation bandwidth. The relative acoustic nonlinearity factor β from the HHG technique shows consistent trends for thick cracks but not for thin cracks. It can be concluded from the obtained parametric analysis results that the SPC-I technique is more robust and reliable for monitoring damages in engineering structures.

Damage Severity Assessment of Multi-Layer Complex Structures Based on a Damage Information Extraction Method with Ladder Feature Mining.

Multi-layer complex structures are widely used in large-scale engineering structures because of their diverse combinations of properties and excellent overall performance. However, multi-layer complex structures are prone to interlaminar debonding damage during use. Therefore, it is necessary to monitor debonding damage in engineering applications to determine structural integrity. In this paper, a damage information extraction method with ladder feature mining for Lamb waves is proposed. The method is able to optimize and screen effective damage information through ladder-type damage extraction. It is suitable for evaluating the severity of debonding damage in aluminum-foamed silicone rubber, a novel multi-layer complex structure. The proposed method contains ladder feature mining stages of damage information selection and damage feature fusion, realizing a multi-level damage information extraction process from coarse to fine. The results show that the accuracy of damage severity assessment by the damage information extraction method with ladder feature mining is improved by more than 5% compared to other methods. The effectiveness and accuracy of the method in assessing the damage severity of multi-layer complex structures are demonstrated, providing a new perspective and solution for damage monitoring of multi-layer complex structures.

Carbon fiber reinforced composites with self‐sensing and self‐healing capabilities enabled by CNT‐modified nanofibers

AbstractCarbon fiber reinforced polymer (CFRP) composites with self‐sensing and self‐healing capabilities was developed in this work. Uniform dispersion of CNTs within a core‐shell nanofiber structure was achieved through a combination of electrospinning and subsequent surface spraying of CNTs. The resulting core‐shell nanofibers were incorporated into CFRP composites using a vacuum‐assisted resin transfer molding process. Mechanical testing demonstrated improved mechanical properties in the composites treated with CNT spraying. Real‐time, precise monitoring of structural damage was enabled through the measurement of electrical signal changes caused by composite deformation in static loading experiments. Furthermore, self‐healing experiments demonstrated that the thermally conductive network formed by CNTs facilitated more uniform heat transfer within the composites. During the healing process, resistive heating of damaged specimens was applied, resulting in a remarkable healing efficiency of 95.2%. The experiments indicate that the composite developed in this paper possess excellent self‐sensing and self‐healing capabilities.Highlights Damage self‐sensing and self‐healing CFRP composites were developed. CNTs‐modified nanofibers were applied to endow the composite specific abilities. Resistivity signals were collected for real‐time damage monitoring. The self‐healing efficiency of the designed composite reached up to 95.2%. Self‐sensing and self‐healing occurred autonomously within closed‐loop structure.

Hole-edge crack monitoring in attachment lug with large bolt hole based on guided wave and circular piezoelectric sensor array

The crack damage monitoring of aircraft structures is very significant for ensuring aircraft safety, reducing maintenance costs and extending service life. Due to the extreme service environment, the attachment lug is prone to initiate crack damage at the hole edge, which leads to crack propagation and fracture failure. Structural health monitoring technology based on piezoelectric guided wave has been widely studied, promoting the development of crack monitoring. However, at present, research on hole-edge crack damage monitoring of attachment lugs still needs to be further carried out. It is difficult to monitor small cracks at the initial stage of crack propagation, and the accuracy of crack monitoring needs to be improved. By focusing on the accuracy of the crack monitoring in the attachment lug, a crack damage monitoring method based on the circular piezoelectric sensor array is proposed in this paper. Combined with damage alarming and localization imaging, this method comprehensively evaluates the hole-edge crack damage monitoring situation and improves the monitoring effect. The method is verified by an experiment in attachment lug, and this verification includes small crack monitoring and crack propagation monitoring. The experimental results demonstrate that this method can achieve correct damage alarming results, and the maximum localization error of crack damage is only 3.02 mm, which provides a research idea for the accurate monitoring of crack damage at the hole edge.

A metamaterial-assisted coda wave interferometry method with nonlinear guided waves for local incipient damage monitoring in complex structures

Nonlinear guided waves exhibit high sensitivity to material microstructural changes, thus attracting increasing attention for incipient damage monitoring applications. However, conventional nonlinear guided-wave-based methods suffer from two major deficiencies which hinder their applications: (1) mostly relying on the first arrivals of wave signals, they apply to limited inspection areas in simple structures in order to avoid wave reflections from structural discontinuities or boundaries; (2) they are prone to numerous deceptive nonlinear sources in the measurement system which might overwhelm damage-induced signal components. To tackle these challenges, we propose a metamaterial-assisted coda wave interferometry (CWI) method using second harmonic Lamb waves, applicable to the monitoring of local incipient damage in complex structures. Embracing the metamaterial concept, a so-called meta-screen is designed, whose geometry and layout can be flexibly tailored to target specific inspection zones in a structure. Capitalizing on its customized bandgap features, the proposed meta-screen allows for the passing of fundamental waves while preventing the second harmonic components generated by deceptive nonlinear sources from penetrating into the inspection area. Through numerical analyses on a plate with a rib stiffener, the efficacy of the meta-screen and the influence of occasional disturbance and regular pollution are evaluated. Experimental validations on an adhesive structure also confirm the superior sensitivity of the nonlinear coda waves to incipient damage, which is further enhanced by the deployment of the meta-screen alongside improved robustness against deceptive nonlinear sources outside the inspection area. The proposed metamaterial-assisted CWI method with second harmonic Lamb waves holds great promise for local incipient damage monitoring of complex structures.

Damage Monitoring and Localization Imaging of Aluminum Alloy Thin-Walled Structure Based on Remote Bonding Fiber Bragg Gratings Sensing.

In this paper, the damage monitoring investigation based on the remote bonding fiber Bragg grating sensing is performed on the aerospace aluminum alloy thin-walled structure with prefabricated damage. Firstly, an ultrasonic excitation-fiber Bragg gratings (UE-FBGs) sensing experimental platform is established for the simulation of defects monitoring, in which the sensors are placed at a certain distance from the bonding area. Secondly, different arrangements of exciters and receivers are utilized for the original signals and the damage signals. Subsequently, the raw signals are processed by filter and feature extraction in order to denoise the signals and acquire the parameters sensitive to the damage. Finally, an improved Reconstruction for Image Defects (RAPID) algorithm is used to locate and reconstruct the pre-existing damage. The results show that the proposed system improves the sensitivity of the FBG receiver signal and the accuracy of the damage imaging.

Damage monitoring of masonry structures using the acoustic emission technique – From tensile and shear characterization tests to shear wall tests

This study evaluated the ability of the acoustic emission technique (AET) to monitor the mechanical damage in masonry structures through multiscale experimental tests ranging from the mesoscopic scale (unit–mortar–unit samples) to the macroscopic scale (shear wall).At the mesoscopic scale, the AET was applied to a series of stone–assembly laboratory specimens subjected to tensile and shear loading. Initially, a mechanical analysis was conducted to characterize two types of cracking, namely mode I (tensile) and mode II (shear). Various acoustic emission (AE) parameters were analyzed in relation to damage accumulation to understand their responses to each type of loading. The representation of AE amplitude versus duration was distinctive in discriminating signals originating from different sources.To validate these findings on the macroscopic scale, we conducted a full-scale test for a shear wall subjected to compression, using both the AET and digital image correlation (DIC). Compared with the DIC results, AET demonstrated a strong performance in damage evaluation and source distinguishment of damage in masonry at the macroscopic scale, which involves more complex failure mechanisms.

Research on damage warning based on cointegration theory

In practical engineering applications, structural fracture failure is caused by slow plastic deformation due to excessive structural stress under large static load.The accurate monitoring and early warning of the structural damage can effectively ensure the efficient service of the structure before the complete fracture of the structure. In this paper, an early-warning method of structural damage based on cointegration theory is proposed. The strain of key positions in structural parts is monitored under the tensile action of large loads, and the damage is judged by the change of the cointegration relationship between strains. Finally, the effectiveness of this method is verified, and the effective monitoring of structural damage is realized through fracture early warning.

Damage-Accumulation-Induced Crack Propagation and Fatigue Life Analysis of a Porous LY12 Aluminum Alloy Plate.

Rivets are usually used to connect the skin of an aircraft with joints such as frames and stringers, so the skin of the connection part is a porous structure. During the service of the aircraft, cracks appear in some difficult-to-detect parts of the skin porous structure, which causes great difficulties in the service life prediction and health monitoring of the aircraft. In this paper, a secondary development subroutine in PYTHON based on ABAQUS-XFEM is compiled to analyze the cracks that are difficult to monitor in the porous structure of aircraft skin joints. The program can automatically analyze the stress intensity factor of the crack tip with different lengths in the porous structure, and then the residual fatigue life can be deduced. For the sake of safety, the program adopts a more conservative algorithm. In comparison with the physical fatigue test results, the fatigue life of the simulation results is 16% smaller. This project provides a feasible simulation method for fatigue life prediction of porous structures. It lays a foundation for the subsequent establishment of digital twins for damage monitoring of aircraft porous structures.