Damage Localization in a CFRP Beam via Modal Frequency Shifts and Nearest Neighbor Classification

This paper presents a novel algorithm for Nearest Neighbor (NN) classifier. NN classification is a well-known method of pattern classification having the following properties: * it performs maximum-margin classification and achieves less than twice the ideal Bayesian error, * it does not require knowledge of pattern distributions, kernel functions or base classifiers, and * it can naturally be applied to multiclass classification problems. Among the drawbacks are A) inefficient memory use and B) ineffective pattern classification speed. This paper deals with the problems A and B. In most cases, NN search algorithms, such as k-d tree, are employed as a pattern search engine of the NN classifier. However, NN classification does not always require the NN search. Based on this idea, we propose a novel algorithm named k-d decision tree (KDDT). Since KDDT uses Voronoi-condensed prototypes, it consumes less memory than naive NN classifiers. We have confirmed that KDDT is much faster than NN search-based classifier through a comparative experiment (from 9 to 369 times faster than NN search based classifier). Furthermore, in order to extend applicability of the KDDT algorithm to high-dimensional NN classification, we modified it by incorporating Gabriel editing or RNG editing instead of Voronoi condensing. Through experiments using simulated and real data, we have confirmed the modified KDDT algorithms are superior to the original one.

Advanced composite materials have been integrated extensively into aircraft structures and have been emerged in civil infrastructure (e.g. bridges), in recent years. Composite materials are prone to initiation of hidden damage which makes it vital to detect damage at its onset. In this paper, first we introduce a procedure to fabricate structural carbon fiber reinforced polymer (CFRP) sheets at the university laboratory. Then, two structural health monitoring (SHM) systems will be developed to assess and monitor the performance of CFRP materials. The first SHM system is based on installing piezoelectric MFC sensors on CFRP sheets. These sensors are using guided lamb waves to detect possible damage in composites at its onset. The second SHM system will be developed using Fiber Bragg Grating (FBG) technology. This system will acquire signals from a fiber optic thermocouple and a fiber optic strain gauge. The damage will be introduced into CFRP sheets with an impact hammer. The damage detection will be performed by the two SHM systems for different damage severities. The pros and cons of each SHM system in composite damage detection will be investigated and the recommendations will be made for utilizing each system in real world composite applications.

Enhanced operational modal analysis and change point detection for vibration-based structural health monitoring of bridges

Civil infrastructure is witnessing an increasing number of degraded and deteriorated steel structures. With current retrofitting and rehabilitation techniques becoming outdated the implementation of carbon fibre reinforced polymer (CFRP) materials have excelled with their impressive strength to weight ratios and chemical resistance. Unfortunately the longevity of bonded CFRP systems is largely unproven under industrial conditions, in particular under combined environmental exposure and fatigue loading. Environmental exposure for such applications often involves elevated temperature and moisture. Moisture ingress can be detrimental to adhesive integrity and can provide the necessary environment for galvanic corrosion between steel and CFRP materials. Temperature changes, particularly elevated temperatures, can soften epoxy adhesives as they reach their glass transition temperature (Tg), in turn, significantly reducing their strength. Additionally, this rubberisation can further intensify the level of moisture absorption, compounding and exacerbating the degradation. Finally these deteriorated materials are likely to be more detrimentally affected by the application of strenuous loading which can significantly reduce their strength even further. Thus, the broad aim of this research is to better understand the durability and fatigue performance of CFRP/steel systems. The first stage of research into the bond performance of CFRP/steel was to quantify the amount of localised corrosion (pitting) created between CFRP and steel during submergence in simulated seawater solutions at elevated temperature. Pitting creates high stress concentrations and can become the site of premature cracking and fracture. Specimen submergence in seawater solutions showed that isolated pitting was insignificant, with general chloride corrosion being more substantial than localised galvanic damage. Consequently, the potential degradation of the adhered joint under such conditions appeared to be more influential on the durability and fatigue performance of CFRP/steel systems. The next stage focused on an investigation of the bond strength of CFRP patched steel double lap specimens after fatigue loading and environmental exposure. To investigate the most damaging and destructive scenarios to durability and strength of the joints, the service loading, fatigue loading, exposure temperature and submergence duration were altered. Normal modulus materials were unable to survive the application of high stress fatigue cycles after environmental exposure. On the other hand, high modulus sheeting specimens survived all loading and environmental conditioning, experiencing strength losses of only 10% on average. The final stage was to investigate methods to improve the bond integrity of double-lap joints to reduce the amount of degradation during submergence and loading. Firstly, a high Tg adhesive was implemented to maintain joint integrity and strength under elevated temperature. Furthermore, carbon nanotubes (CNT) were dispersed into common structural epoxies to increase the physical and mechanical properties of the adhesives, however their addition increased adhesive viscosity and decreased their workability. Also a chemical bond promoter, silane, was introduced to enhance the chemical bond of CFRP/steel joints to ease the strength losses resulting from submergence and fatigue. However, levels of strength reduction remained comparable between un-treated and silane pre-treated samples, implying that degradation is not necessarily due to an issue between the interface of steel and epoxy, but more likely within the bulk adhesive. Thus, silane pre-treatment is perhaps more effective when combined with multi-layered patches as they commonly witness more steel and adhesive interfacial failures. After bond performance was explored in detail, investigations progressed to examine the fatigue performance of CFRP repaired damaged steel after seawater submergence. Tri-layered CFRP patches were applied to pre-cracked steel plates prior to exposure. Configurations consisted of either single or double sided repair, with or without silane pre-treatment. Several double sided repaired specimens managed to survive in excess of 6 million cycles without any visible damage after exposure, while single sided specimens maintained at least an 80% increase in fatigue life over bare steel. Finally, a numerical model was developed to predict the fatigue life of pre-exposed CFRP repaired steel, which was validated via the experimental investigations. The influence of environmental conditioning was incorporated into linear elastic fracture mechanics theory to accurately predict the degradation and fatigue performance of exposed repairs. This research provides advanced understanding into the durability of adhered CFRP to steel. Investigations focused on the combination of fatigue loading and environmental exposure, which is likely for structural elements expected to utilise CFRP strengthening. Studies showed that CFRP systems were capable of surviving conditions that are more severe than those expected during their industrial life cycle. It can be concluded that CFRP can potentially provide revolutionary rehabilitation performance for steel structures, even under extreme environmental and loading scenarios.

Carbon fiber reinforced polymers (CFRPs) combine high load-carrying capacity with low specific weight, making them increasingly important for lightweight design in modern aerospace structures. Their use is expanding not only in primary components but also in repair patches for reinforcing aluminum alloy (AA) elements. However, suitable nondestructive evaluation (NDE) techniques for CFRP–AA assemblies remain limited. CFRPs are heterogeneous materials composed of carbon fibers and a polymer matrix, characterized by pronounced interfaces and anisotropic electrical conductivity due to fiber orientation. For evaluation purposes, CFRPs can be approximated as a homogeneous medium with an effective specific conductivity determined by composition and design. This study investigates the feasibility of measuring CFRP layer thickness on AA substrates using eddy current (EC) techniques. Conventional EC instruments designed for dielectric coatings are unsuitable due to the relatively high conductivity of CFRP. To address this, single-coil ferrite-core EC probes (8 mm diameter) were developed and operated at low frequencies in resonant mode. Experiments on specimens provided by the State Enterprise “ANTONOV” demonstrated that CFRP thicknesses up to 12 mm can be measured reliably. The results will contribute to the development of a dedicated EC instrument for CFRP thickness measurement. Moreover, the proposed technique is sensitive to delamination between CFRP and AA substrates or between CFRP layers, enabling potential application in in-service inspection and structural health monitoring. Such use requires initial baseline measurements at reference points for subsequent comparison during the service life of the structure.

The classification accuracy of a -nearest neighbor ( NN) classifier is largely dependent on the choice of the number of nearest neighbors denoted by . However, given a data set, it is a tedious task to optimize the performance of NN by tuning . Moreover, the performance of NN degrades in the presence of class imbalance, a situation characterized by disparate representation from different classes. We aim to address both the issues in this paper and propose a variant of NN called the Adaptive NN (Ada- NN). The Ada- NN classifier uses the density and distribution of the neighborhood of a test point and learns a suitable point-specific for it with the help of artificial neural networks. We further improve our proposal by replacing the neural network with a heuristic learning method guided by an indicator of the local density of a test point and using information about its neighboring training points. The proposed heuristic learning algorithm preserves the simplicity of NN without incurring serious computational burden. We call this method Ada- NN2. Ada- NN and Ada- NN2 perform very competitive when compared with NN, five of NN's state-of-the-art variants, and other popular classifiers. Furthermore, we propose a class-based global weighting scheme (Global Imbalance Handling Scheme or GIHS) to compensate for the effect of class imbalance. We perform extensive experiments on a wide variety of data sets to establish the improvement shown by Ada- NN and Ada- NN2 using the proposed GIHS, when compared with NN, and its 12 variants specifically tailored for imbalanced classification.

Carbon Fiber Reinforced Polymers (CFRPs) have become an essential part of designing and engineering lightweight rigid bodies, predominantly in the aerospace and automotive industries. Typical epoxy based CFRPs exhibit virtually no plasticity with minimal strain to failure. Although CFRPs have high specific strengths and elastic moduli, the brittle fracture mechanism presents unique challenges in failure detection for the US Army’s vertical lift vehicle components since failure can occur catastrophically. The Army currently uses a “safe-life” interval-based service methodology where components are replaced with regards to a usage spectrum rather than the component’s actual state of structural health. This paper explores a method for solving this problem by investigating the possibility of embedding Terfenol-D particles (∼100 microns in diameter), a magnetostrictive material, into the CFRP’s ply interphase for embedded non-contact, real-time, structural health monitoring. For baseline results, the change in localized (32 mm2 field of view) magnetic flux was only 0.02% for an applied load of 0–100% of the material’s ultimate tensile strength (UTS). For quasi-static testing procedure on specimen 5714 (15 wt.% Terfenol-D embedded CFRP) on a 0–40% loading interval of the material’s UTS, there was an observed localized (32 mm2 field of view) magnetic flux gradient of more than 5 mT (4%) with a reversible flux of 100%. For quasi-static testing procedure on specimen 5714 (15 wt.% Terfenol-D embedded CFRP) on a 0–70% loading interval of the material’s UTS, there was an observed localized (32 mm2 field of view) magnetic flux gradient of more than 3 mT (2%) with a reversible flux of only 25%. Terfenol-D embedded CRFPs have shown promising results for detecting instantaneous strain and degradation levels. Acoustic emission (AE) and X-ray computed tomography (CT) scanning were used to validate the observed results.

The application of Lamb waves to monitor fatigue crack propagation and to detect fatigue crack initiation in carbon fibre reinforced polymer (CFRP) strengthened steel plates is investigated. A literature review of recent developments and advances of Lamb wave-based structural health monitoring (SHM) applications is presented. The characteristics of Lamb waves, current signal processing and feature extraction technique, and damage-detection techniques are studied. The mechanism of nonlinear Lamb waves, especially contact acoustic nonlinearity (CAN), is investigated, along with latest numerical and experimental achievements and findings of nonlinear Lamb wave-based SHM. This study consists of two parts, monitoring fatigue crack propagation and detecting crack initiation. Two methods are proposed accordingly. The first uses linear Lamb waves to monitor the fatigue crack propagation. 3-dimensional Finite element (FE) models are developed in Abaqus to assess the proposed linear method, along with Lab-based experimental validation. Based on the results, it is concluded that Lamb waves are sensitive to the concealed fatigue cracks, and Lamb wave-based SHM is a promising technique to monitor fatigue crack propagation. The second method employs nonlinear Lamb waves to detect the initiation of fatigue crack because of their remarkable sensitivity to small-scale damage. The principles of wave nonlinearity are presented and the mechanics of CAN is discussed. The proposed nonlinear method is first assessed in steel plates. The effects of CFRP laminates on Lamb waves are investigated and the signals captured in steel plates with/without CFRP laminates are compared. The simulation and experimental results are presented and discussed.

The guided wave technique is commonly used in structural health monitoring as the guided waves can propagate far in the structures without much energy loss. The guided waves are conventionally generated by the surface-mounted piezoelectric wafer active sensor (PWAS). However, there is still lack of understanding of the wave propagation in layered structures, especially in structures made of anisotropic materials such as carbon fiber reinforced polymer (CFRP) composites. In this paper, the Rayleigh-Lamb wave strain tuning curves in a PWAS-mounted unidirectional CFRP plate are analytically derived using the normal mode expansion (NME) method. The excitation frequency spectrum is then multiplied by the tuning curves to calculate the frequency response spectrum. The corresponding time domain responses are obtained through the inverse Fourier transform. The theoretical calculations are validated through finite element analysis and an experimental study. The PWAS responses under the free, debonded and bonded CFRP conditions are investigated and compared. The results demonstrate that the amplitude and travelling time of wave packet can be used to evaluate the CFRP bonding conditions. The method can work on a baseline-free manner.

The identification and severity of structural damages in carbon fiber reinforced polymer (CFRP), especially in the early stage, is critical in structural health monitoring (SHM) of composite materials. Among several approaches used to accomplish this goal, ultrasound inspection using Lamb waves has been taking place within non-destructive testing (NDT) methods. Likewise, the use of digital signal processing techniques for structural damage diagnosis has become popular due to the fact that it provides relevant information through feature extraction. In this context, this paper presents an alternative strategy based on the use of root mean square deviation (RMSD) and correlation coefficient deviation metric (CCDM) representative indices to extract the most sensitive information related to damage in CFRP plates through ultrasonic NDT signals in specific frequency ranges. In the experimental analysis, CFRP coupons were subjected to two types of damages: cracking and delamination. The signals, generated by piezoelectric transducers attached to the host structure using the pitch-catch method of Lamb waves, were subject to signal processing parameters based on the proposed approach. The results reveal that the proposed method was able to characterize the different types of damage in CFRP, as well as their severity in specific frequency bands. The results indicate the feasibility of the proposed method to detect and characterize damage in composite materials in a simple way, which is attractive for industrial applications.

Improving nearest neighbor classification with cam weighted distance

Structural health monitoring (SHM) of carbon fibre reinforced polymers (CFRP) provides a valuable way to assess the condition of these materials, which possess desirable traits, but exhibit little elastic failure. Further complications arise due to their multi component nature, which means they can fail in a variety of ways. High-fidelity magnetostrictive SHM technology exhibits higher cost when utilising top-end materials that contain rare-earth elements. At the expense of fidelity, cost can be reduced by using Fe-based ribbons and cheap transducers. This reduction in accuracy can be overcome by coupling a detection array with the development of specific detection schemes for each failure type to reduce signal complexity. In this work, we investigate the failure of CFRP coupons in Mode 1 and 2 failure, using FeSiB ribbons as the actuator either bonded between the CFRP ply or co-cured on the surface. During the measurement, a set of 4 pancake coils are used to measure the change in inductance, as the CFRP fails, thus demonstrating that this magnetostrictive-coil set-up can be used to measure cracks forming and propagating within the CFRP, hence it is a promising SHM system. For the first time, it is demonstrated that there is a correlation between the induction signal and the elastic energy stored within the CFRP coupon.

In this study, we experimentally investigate the impact damage and failure mechanisms of foam-core sandwich composites with carbon fiber reinforced polymer (CFRP) and/or glass fiber reinforced polymer (GFRP) face sheets. Specimens are conditioned and impacted over a wide temperature range (from room temperature of 23°C down to -70°C) using Instron CEAST 9350 impact test machine. This work also explores the use of hybrid composite face sheets, with various configurations of layering CFRP and GFRP in the face sheets of foam-core sandwich composites. Results show that exposure to low temperature causes more severe damage, particularly in specimens with CFRP face sheets, due to their extreme brittleness. GFRP face sheet specimens are able to achieve larger deformation and absorb greater impact energy even at low temperature conditions. There is a significant benefit in using hybrid configurations, by striking a balance between brittle (CFRP) and ductile (GFRP) materials and harnessing their mechanical advantages of high strength and high fracture toughness, respectively. Results also demonstrate that hybridization is a solution to reduce temperature effects in impact deformation. Careful placement of CFRP and GFRP in layup order can harness the best impact performance. X-ray micro-computed tomography (μCT) images reveal substantial delamination at the interfaces between CFRP and GFRP layers. It is also observed that CFRP layers fail by brittle fracture, while delamination is the major damage mode within GFRP layers. Other complex failure mechanisms in the composite face sheets (such as matrix crack and fiber breakage) and foam core (core crushing, core shearing and interfacial debonding) are also presented.

A combined finite element and hierarchical Deep learning approach for structural health monitoring: Test on a pin-joint composite truss structure

Recent works have shown that combining several classifiers is an effective method to improve classification accuracy. Many ensemble approaches have been introduced such as bagging and boosting that have reduced the generalization error of different classifiers; however, these methods could not increase the performance of Nearest Neighbor (NN) classifier. In this paper, a novel weighted ensemble technique (WNNE) is presented for improving the performance of NN classifier. In fact, WNNE is a combination of several NN classifiers, which have different subsets of input feature set. The algorithm assigns a weight to each classifier, and uses a weighted vote mechanism among these classifiers to determine the output of ensemble. We evaluated the proposed method on several datasets from UCI Repository and compared with NN classifier and Random subspace method (RSM). The results show that our method outperforms these two approaches.