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Adhesive bonding is widely adopted in aeronautic structures to join composite materials or to repair damaged substrates. However, one of the most common failure modes for this type of joint is debonding under fatigue loading. In the past years, it has been proven that deboning quantification is feasible, given that abundant experimental data are available. In this context, using domain adaptation to assist diagnostic tasks based on labeled data from similar structures or simulations would be thoroughly beneficial. However, most domain adaptation methods are designed for classifications and cannot efficiently address regressions. A fuzzy-set-based joint distribution adaptation for regression method has been developed by the authors, tackling regression problems but being limited to single outputs. The novelty presented in this paper exploits clustering techniques to approach multi-output problems, adopting a modified multikernel maximum mean discrepancy to improve the domain discrepancy metric. The proposed method is applied to cracked lap shear specimens to assist debonding quantification. Several domain adaptations are investigated: from simulations to experiments, and from one specimen to another, proving that the accuracy of damage quantification can be improved significantly in realistic environments. It is envisioned that the proposed approach could be integrated into fleet-level digital twins for nominally identical but heterogeneous systems.

The inverse Finite Element Method (iFEM) is a model-based technique to compute the displacement (and then the strain) field of a structure from strain measurements and a geometrical discretization of the same. Different literature works exploit the error between the numerically reconstructed strains and the experimental measurements to perform damage identification in a Structural Health Monitoring framework. However, only damage detection and localization are performed, without attempting a proper damage size estimation. The latter could be based on machine learning techniques, however, an a priori definition of the damage conditions would be required. To overcome these limitations, the present work proposes a new approach in which the damage is systematically introduced in the iFEM model to minimize its discrepancy with respect to the physical structure. This is performed with a maximum likelihood estimation framework, where the most accurate damage scenario is selected among a series of different models. The proposed approach is experimentally verified on an aluminum plate subjected to fatigue crack propagation, which enables the creation of a Digital-Twin of the structure itself. The strain field fed to the iFEM routine is experimentally measured with an Optical Backscatter Reflectometry fiber and the methodology is validated with independent observations of lasers and the Digital Image Correlation.

This paper presents an experimental and numerical investigation on the influence of pre-existing impact damage on the low-velocity impact response of Carbon Fiber Reinforced Polymer (CFRP). A continuum damage mechanics-based material model was developed by defining a user-defined material model in Abaqus/Explicit. The model employed the action plane strength of Puck for the damage initiation criterion together with a strain-based progressive damage model. Initial finite element simulations at the single-element level demonstrated the validity and capability of the damage model. More complex models were used to simulate tensile specimens, coupon specimens, and skin panels subjected to low-velocity impacts, being validated against experimental data at each stage. The effect of non-central impact location showed higher impact peak forces and bigger damage areas for impacts closer to panel boundaries. The presence of pre-existing damage close to the impact region leading to interfering delamination areas produced severe changes in the mechanical response, lowering the impact resistance on the panel for the second impact, while for non-interfering impacts, the results of the second impact were similar to the impact of a pristine specimen.

The importance of proper CNT dispersion is still the main challenge in CNTs doped epoxy nanocomposites. Therefore, this study was aimed to investigate the effect of toroidal stirring-assisted sonic...

Delamination is a failure mechanism which is intrinsic of laminated fibre-reinforced plastics and possibly one of the major concerns of laminated composite structures, since, under certain conditions, delaminations can grow up to an hazardous extent without visible traces. In order to keep pace with recent condition-based maintenance requirements, proper validated diagnostic and prognostic methods which should be capable of operating on-line and in real time are required. In this respect, particle filters provide a consistent Bayesian framework, where the posterior distribution of the system degradation status is recursively approximated based on a time-growing stream of observations measuring the system response. However, the real-time operation capability of such methods is hindered by their requirements in terms of analysis time, which is mainly due to the complexity of the models they rely upon. Within this work, a particle filter framework, able to deal with the inherent stochasticity of fatigue delamination growth – while simultaneously relieving the computational burden associated with the evaluation of the trajectory likelihoods – is provided, leveraging on surrogate modelling strategies. Simultaneous diagnosis and prognosis of a simulated carbon fibre-reinforced plastics double cantilever beam specimen subject to fatigue delamination growth are performed, based on the observation of the strain field pattern acquired at some specific locations. The posterior probability density function of the delamination extent during propagation is updated at each inspection time as well as the probability density function of the remaining useful life. Ultimately, the adoption of the augmented state formulation allows for the estimation and updating of the joint probability density function of the parameters driving the stochastic delamination propagation model. Results demonstrate the feasibility and potential of the proposed approach as a tool able to monitor the progressing delamination while simultaneously providing estimates about the remaining useful life of composite structures.

Despite the promising application of Distributed Optical Fiber Sensors (DOFS) in monitoring damage in composite structures, their implementation outside academia is still unsatisfactory due to the lack of a systematic approach to assessing their damage detection performance. The existing tool developed for non-destructive evaluation, Probability of Detection (POD) curves, needs to be adapted for structural health monitoring applications to account for spatial and temporal dependency. Damage detection performance with DOFS is deeply related to the inherent variability sources of the system, the strain transfer properties of the optical fiber, and the loading conditions, which determine the damage-induced strain on the structure. This paper establishes a systematic approach based on the Length at Detection (LaD) method to qualify DOFS for damage detection in composites under different scenarios. Specifically, this study considers two DOFS with different strain transfer properties for monitoring delamination in carbon fiber reinforced polymers double-cantilever beam specimens under mode I quasi-static and fatigue loading. The POD curves derived from the LaD method confirm that this methodology can quantify the change in the detection performance due to the DOFS type and the loading conditions. The study also proposes a practical solution to compare POD curves obtained with different sample sizes, by introducing the concept of virtual specimens to simulate the lower confidence bound convergence.

In the past two decades, the aerospace industry has massively shifted from aluminum-made components to composite materials such as carbon fiber reinforced polymers (CFRP), striving for more fuel efficient and lighter aircrafts. Consequently, traditional joints have been replaced by adhesive bonded interfaces, which are also the most common choice to repair damaged components. Although adhesive bonding is the most efficient choice for permanent connections, it is not free of disadvantages: one of the most common failure modes, the debonding of the two laps, is very problematic to detect and predict in practice. Therefore, frequent inspections must be performed to ensure structural safety, increasing maintenance costs, and lessening the availability of the platforms. The development of innovative sensing technologies has allowed for a close monitoring of structural interfaces, and several structural health monitoring techniques have been proposed to monitor adhesive bonded connections. Sensitivity and correlation between measurements and debonding entity has been demonstrated in the literature: nevertheless, hardly any technique has been proposed and quantitively evaluated to estimate the debonding entity independently of the applied loads, such as misalignment-induced torsion, which is a major confounding influence in the traditional backface strain gauge technique. This paper proposes the inverse finite element method (iFEM) as a load and material independent approach to infer the debonding entity from strain measurements in adhesive-bonded joints. Two approaches to estimate the debonding entity with the iFEM are compared on cracked leap shear specimens representative of CFRP repair patches: one is based on anomaly indexes, the other on performing a model selection with multiple iFEM models including different damages. The latter demonstrates satisfactory performances; thus, it is considered a significant scientific advancement in this field.

Confounding influences, such as operational and environmental variations, represent a limitation to the implementation of Structural Health Monitoring (SHM) systems in real structures, potentially leading to damage misclassifications. In this framework, this study considers cointegration as a state of the art method for data normalisation in fatigue crack propagation scenarios, where monitoring is performed by a distributed network of strain sensors. Specifically, the work is aimed at demonstrating the effectiveness of cointegration on real engineering data in a new context, where the damage is continuously growing. Cointegration is applied at first in a controlled scenario consisting of a numerical strain simulation by means of a finite element model, modified in order to take realistic temperature fluctuations and sensor noise into account. Afterwards, detrending and anomaly detection performances are verified in two different experimental programmes on realistic aeronautical structures subjected to fatigue crack growth, including a full-scale fatigue test on a helicopter tail boom. Strain measurements are taken from a network of Fibre Bragg Grating (FBG) sensors, known to be extremely sensitive to temperature variations, hence delivering challenging scenarios for cointegration testing. Results are shown to be in good agreement with the experimental evidence, with the cointegration algorithm capable of detecting the onset of damage propagation within a 4 mm increment from a baseline condition.

The present paper investigates a modelling approach of experimentally tested thick panels made of Carbon Fibre Reinforced Polymers (CFRP). The coupons were made of 24 unidirectional (UD) laminae with a layup [45/0/-45/90]3s. The specimens were subjected to low velocity impact using a drop tower system. Several sensors, including a load cell and strain gauge, were utilized both for analysing the behaviour of the material against the impact and for performing a validation of the numerical models. Three energy levels were adopted: 8J, 10J and 12J. Numerical models were implemented into the finite element (FE) software LS-DYNA. A linear - elastic constitutive law with an instantaneous failure material was selected for mimicking the intralaminar behaviour of the carbon fibre composite. Enhanced Chang – Chang was adopted as the onset-of-failure criterion. This criterion is able to capture damage in different directions and permits the consideration (or not) of the shear behaviour in the failure equations. The capability of the model to capture the correct interface failure process was particularly emphasized and therefore cohesive elements with a bilinear traction – separation law were chosen for the reproduction of delamination. Finally, the experimental – numerical results were compared using first and foremost the overall delamination area and the curves force – time, force – displacement and absorbed energy – time as well as the strain measures obtained by the sensors.

In the paper the so called Transfer Impedance approach to low-velocity impact damage detection of composite structure is presented. The approach is based on application of PZT transducers for excitation and acquisition of elastic waves in a CFRP (Carbon Fiber Reinforced Polymer) composite panel. The method relies on the so called pitch - catch scheme of PZT transducers application, where pairs of sensors are used for the purpose of structure monitoring, i.e. one sensor is used as elastic waves generator and the other as receiver of excited waves. In the presented paper, sinusoid signal in a given frequency range is used for elastic waves excitation, which is different than in the standard pitch-catch approach. In the paper algorithm for signal analysis is presented, which allows to maintain full information carried by acquired signals in a given frequency for the purpose of damage detection. Stability of damage indication acquired for different pairs of PZT transducers of the network is analyzed in the paper as well as sensitivity of the approach to impact damage detection. Detailed description of the developed method, hardware implementation, experimental setup as well as description of the obtained results are delivered in the paper.

This paper presents a deep learning network that performs automatic detection of defects by inspecting full ultrasonic guided wave signals excited in plate structures. The findings show that the algorithm, which is an adaptation of WaveNet, and hence is based on causal dilated convolutional neural networks, is effectively able to learn features and/or patterns related to the presence of waves scattered from damage, thus eliminating the need for any feature engineering to be performed by human operators. The network outperformed the widely used conventional approach that combines the optimal baseline selection and baseline signal stretch compensation methods when tested on two different datasets. The first dataset consisted of finite element simulated Lamb wave signals acquired in a pitch-catch configuration on a steel plate across a 50 °C range of temperature variations, and the second was a publicly available experimental dataset of Lamb wave signals also acquired in pitch-catch mode on a composite plate with a 40 °C range of variations. The improvements over the conventional approach are particularly encouraging when analyzing signals at temperatures well outside the temperature range available in the set of baseline signals, hence suggesting that this class of algorithms can complement or substitute existing methods, especially when testing occurs at unseen environmental and operational conditions, or when the effects of sensor drift make conventional methods less effective.

The inverse Finite Element Method (iFEM) is receiving more attention for shape sensing due to its independence from the material properties and the external load. However, a proper definition of the model geometry with its boundary conditions is required, together with the acquisition of the structure’s strain field with optimized sensor networks. The iFEM model definition is not trivial in the case of complex structures, in particular, if sensors are not applied on the whole structure allowing just a partial definition of the input strain field. To overcome this issue, this research proposes a simplified iFEM model in which the geometrical complexity is reduced and boundary conditions are tuned with the superimposition of the effects to behave as the real structure. The procedure is assessed for a complex aeronautical structure, where the reference displacement field is first computed in a numerical framework with input strains coming from a direct finite element analysis, confirming the effectiveness of the iFEM based on a simplified geometry. Finally, the model is fed with experimentally acquired strain measurements and the performance of the method is assessed in presence of a high level of uncertainty.

Mechanical and aeronautical structures could experience unexpected loads during operations, potentially reducing their operability. A load monitoring system enables one to recover the actual load spectra of the component and track its aging continuously. However, complex loading can be difficult to be identified, as for complex aerodynamic loads due to operational maneuvers in aeronautical structures, especially when environmental conditions vary. This work proposes a method for recovering the full strain and displacement fields in the entire structure, leveraging on the Calibration Matrix approach and an equivalent set of lumped forces, including the thermal variation among the unknown parameters. A least-squares minimization of an error functional defined as a comparison between measured and numerical strain is exploited for load and temperature reconstruction, automatically compensating thermal effects on the strain measurements without requiring any training data sets. By assuming a linear relationship between strain and the unknowns through a calibration matrix, the minimization can be performed analytically offline, resulting in a computationally very efficient algorithm that can be operated in real-time at a reduced computational burden. Though the mathematical formulation is general for any arbitrary component geometry and loading condition, the method is tested with a full-scale Unmanned Aerial Vehicle (UAV) undergoing different operational maneuvers, both numerically and with actual flight tests. The results confirm the robustness of the method to real environmental disturbances, correctly reconstructing the strain and displacement fields due to aerodynamic loads through triangular equivalent forces and automatically adapting to different load scenarios and temperature changes.

Direct or indirect damages due to foreign object impacts on aeronautical structures, represent a major concern. The problem potentially intensifies with the adoption of composite materials, especially due to Barely Visible Impact Damage (BVID). In this context, understanding whether an impact event gives rise to delamination or debonding is highly desirable in view of the optimization of the maintenance strategies and, at the same time, of the safety margins associated to the operation of the structures. One possible method to achieve this goal is that of integrating damage monitoring systems within the vehicle architecture itself. By doing so, in fact, the enhanced structural health state awareness allows the implementation of Predictive Maintenance philosophies and the possibility to detect damage with size/severity and indentation smaller than the BVID currently applied by design and certification. In this work, a simple and a stiffened carbon fiber panel are subjected to Low Velocity Impacts using falling masses to generate a structural damage. A sensor network made of Piezoelectric elements (PZT) allows the application of Ultrasonic techniques, to monitor the damaged structure and calculate signal related features called Damage Indexes (DIs). The DI capability to identify the damage is then thoroughly investigated, with specific reference to: (i) effect of signal averaging, (ii) effect of reduced sensor network configurations and (iii) effect of sensor faults.

The inverse Finite Element Method (iFEM) is an algorithm able to compute the deformed shape of a structure starting from its geometry, the boundary conditions, and the strain data measured on discrete positions of the structure, without prior knowledge on material properties and load condition. A critical point of this technique is the sensor pattern optimization for a correct strain field reconstruction. Sensors cannot be applied on the whole structure due to the presence of the physical boundary conditions and logistic constraints, potentially leading to a wrong reconstruction of the deformed shape by the iFEM. Thus, the Smoothing Element Analysis (SEA) is used in this work for prior extrapolation of the strain field in locations where sensors are not available. A sensitivity analysis highlights the influence of the mesh size and different SEA hyper-parameters over the extrapolated strains, which are then provided as input to the iFEM for shape sensing. The method has been verified with experimental tests on a CFRP specimen sensorised with fiber optic, subjected to a compressive load, proving the iFEM can compute the deformed shape of the structure also in presence of a buckling condition. The iFEM results are validated with experimental displacement measures from lasers.

Direct or indirect effects provoked by foreign object impacts on aeronautical structures, represent a major concern for military and civil aviation. The problem potentially intensifies with the adoption of composite materials, especially if Barely Visible Impact Damages (BVID) are generated in the structure. The knowledge of whether an impact event has happened and if it has produced a damage, is highly desirable allowing maintenance improvements and the management of risky situations. This can be achieved developing an Impact Monitoring (IM) system, eventually integrable with other monitoring systems for the implementation of a Predictive Maintenance (PM) philosophy.

Ultrasonic guided waves-based Structural Health Monitoring (SHM) is a promising solution to perform damage diagnosis in plate-like structures. In this field, the Reconstruction Algorithm for Probabilistic Inspection (RAPID) is one of the most widely used tomographic algorithms to perform active damage detection and localisation . Even though this algorithm is easily implementable, very versatile and satisfactorily accurate, it presents some disadvantages, such as the production of artefacts determined by non-uniform distributions of the sensing network density. In the present article, a processing and spatial filtering technique is proposed, which strongly mitigates the aforementioned problem. The proposed methodology is validated using a numerical database created for a large carbon fibre reinforced polymer (CFRP) with a through-the-thickness hole.

On-line prognosis of fatigue cracks in the structure is challenging due to various uncertainties affecting fatigue crack initiation and growth. This paper proposes an on-line prognosis strategy for fatigue cracks by incorporating the mixture proposal particle filter (MPPF) and structural health monitoring (SHM) results. In this method, a dynamic crack evolution model is proposed to deal with the situation that more than one crack occurs and grows in the structure. Meanwhile, crack sizes monitored by the SHM technique are incorporated to construct an effective mixture proposal of the importance probability density, which is the key for sampling new particles. Further, posterior estimations of the fatigue crack sizes and the crack evolution model parameters are evaluated with these particles, based on which the prognosis of fatigue crack growth is carried out. A leave-one-out validation is performed on the dual crack growth problem of the hole-edge-cracked structure, demonstrating the effectiveness of the proposed method.

Transverse cracking and crack induced delaminations growth are generally considered as the most relevant damage modes developing during the fatigue loading of composite laminates. So far, non-interactive damage progression schemes have been considered, arguing that transverse cracks saturate before the initiation of induced delaminations, despite concurrent damage accumulation has been experimentally acknowledged, also suggesting mutual interaction. In the present paper a simultaneous and interactive damage progression scheme based on fracture mechanics is proposed. A two-dimensional variational mechanics analysis approach is used to obtain the individual energy release rates and the interaction between damage modes is theoretically acknowledged. Also, a unique parameter is proposed accounting for both the damage modes accumulation levels, ruling the growth rates of the individual damage modes, and the characteristic damage state is questioned. Theoretical findings are then verified using multi-scale fatigue damage data from GFRP cross-ply laminate specimens subjected to uniaxial fatigue loading.

The inverse Finite Element Method (iFEM) is a model-based technique for structural shape sensing based on a pattern of input strain data experimentally acquired on the structure. It is independent of the external load and material properties, making it a valid approach for composite materials monitoring. In this framework, the iFEM shape sensing capability is experimentally investigated on a carbon fiber reinforced polymer plate subjected to a compressive buckling condition. As logistic constraints impede strain sensor installation in all the desired locations, Smoothing Element Analysis (SEA) and polynomial fittings are used to pre-extrapolate the whole plate's strain field. Finally, after a sensitivity analysis on the input strain pre-extrapolation, the iFEM displacement reconstruction is validated with three lasers' independent measurements and direct FEM results, investigating the local and global shape sensing capability.

An investigation on fatigue crack growth monitoring of bonded joints has been conducted employing a novel carbon nanotube (CNT) adhesive film, chosen for their proven exceptional properties for sensing purposes. Single lap shear fatigue tests have been carried out at a 7 kN load, load ratio 0.1 and 10 Hz frequency. A correlation between the electrical response of the adhesive joints and the fatigue crack growth has been established by means of voltage acquisition through the thickness in combination with crack length monitoring by microscopy analysis. Three phases can be distinguished during the fatigue tests: phase one without crack initiation with a steady electrical response; phase two in which cracks start propagating, with a slight increase of the electrical resistance, and phase three, corresponding to final propagation to failure with a drastic increase of the electrical resistance. A correlation between the electrical response, the estimated crack area and the measured edge-crack size is achieved, allowing to get a deeper understanding of crack growth phenomenon as a function of the number of cycles. Thus, the potential and the applicability of the proposed technique for monitoring crack growth in lap joint fatigue tests have been demonstrated.

Many studies performed on multifunctional properties of epoxy based nanocomposites reinforced with carbon nanotubes (CNTs) and nanoclay (NC) whereas their synergetic effects on piezoresistive behaviour of ternary state nanocomposites still remains unaddressed. Therefore, the hybrid effects of double-walled CNTs (DWCNTs) and NC on the mechanical, electrical and piezoresistive performances of the epoxy were addressed in this study. Nanocomposites were prepared in two different states, i.e. the binary state (DWCNTs/epoxy) and the ternary states (DWCNTs-NC/epoxy). SEM, FESEM, and XRD were used for the microstructural analysis of the materials while tensile and mode I fracture tests were performed for mechanical and piezoresistive characterizations. The addition of NC to CNTs doped epoxy resulted in a better CNT dispersion, hindering CNT re-agglomeration. A significant increase in KIC (94%) and GIC (254%) compared to the neat epoxy was obtained for the hybrid nanocomposites loaded at 1 wt% NC due to crack bridging and crack deflection. The electrical conductivity of the ternary state materials increased by 700% and 400% with respect to the binary nanocomposite, for 0.5 wt% and 1 wt% NC loadings, respectively. The hybrid nanocomposites also manifested higher piezoresistivity and a more robust signal in tensile and fracture tests, respectively.

A key issue affecting the performances of every human-conceived engineering system is its degradation, fatigue crack growth being one of the major structural deterioration phenomena. Fatigue crack growth is usually modelled as a stochastic process: uncertainty sources lie both in the item and in the physical degradation process variability. Fatigue crack growth deserves close attention, especially considering that condition-based maintenance methodologies are recently experiencing a major drive to increase their technology readiness level, requiring validated diagnostic and prognostic methodologies which should be capable of operating online and in real-time. In this regard, particle filters provide a consistent Bayesian framework, where the posterior distribution of the system degradation state is recursively approximated based on a time-growing stream of observations measuring the system response, enabling, in general, increasingly informed lifetime estimates. However, the real-time operation capability of such methods is hindered by their requirements in terms of computational power, which is mainly due to the complexity of the structural models they rely upon. Within this work, a comprehensive particle filter framework, able to deal with fatigue crack growth uncertainty sources while simultaneously addressing the computational burden issue, is proposed. The algorithm structure enables to simultaneously perform the diagnosis and prognosis of fatigue crack growth, while the adoption of the augmented state formulation allows to address scenarios where the degradation process of fatigue crack growth fails to meet the degradation model ruling the particle filter. Artificial neural networks–based surrogate modelling is adopted at different stages and embedded within the particle filter algorithm, relieving the computational burden associated with the evaluation of the trajectory likelihoods as well as enabling a fast estimation of the remaining useful life. Both simulated and experimental data sets regarding fatigue crack growth in an aluminium aeronautical panel are used for the algorithm testing, additionally proving the validity and effectiveness thereof by means of common prognostic performance metrics.

The current paper was aimed to investigate the effect of addition of MWCNTs on mechanical properties of epoxy. Tensile, fracture and impact tests were carried out for mechanical characterization while the weight concentration of the MWCNTs was 0.5 wt.%. Results showed that addition of MWCNTs resulted in decreasing tensile strength of the nanocomposites, whereas an improvement appeared for fracture and impact tests. In fact, fracture toughness and impact strength of the MWCNTs/epoxy increased by 65 and 117 percentage, respectively, compared with the pristine epoxy.

This study presents an investigation of the influence of Single-Walled Carbon Nanotubes (SWCNTs) and Double-Walled Carbon Nanotubes (DWCNTs) on tensile and impact strengths of epoxy based nanocomposites. Two different CNTs weight percentages were employed including 0.5 and 0.75 wt.% in order to verify their effects on microstructures and mechanical properties. Scanning Electron Microscopy (SEM) and Field Emission Scanning Electron Microscopy (FESEM) were used to analyze the microstructural characteristics of the CNT doped epoxy including presence of defects such as air bubbles, tiny pores and aggregates. DWCNTs/epoxy nanocomposites presented higher amount of pores due to lack of functionalization, while SWCNTs/epoxy ones showed more entanglement mostly at high CNT loading due to its larger aspect ratio. Results showed that tensile strength was slightly enhanced (6 %) for 0.5 wt.% SWCNT, whereas for other nanocomposites it reduced in comparison with the neat epoxy, due to presence of manufacturing defects in the material. Impact strength significantly improved by 51 % and 31 % at 0.5 wt.% SWCNTs and 0.5 wt.% DWCNTs, respectively, resulting from activation of crack bridging and CNTs pull-out mechanisms. Different behaviours between tensile and impact tests are related to homogeneous stress distribution and localized stress concentration in tensile and impact tests, respectively.

Since the invention of Carbon Nanotubes (CNTs), the self-sensing capability of Multi-walled Carbon Nanotubes (MWCNT) based nanocomposites has been widely analyzed, whilst Single-Walled Carbon Nanotubes (SWCNTs) have been studied less extensively. Moreover, brittleness of epoxy based materials made them vulnerable to crack propagation, necessitating the monitoring of crack initiation and propagation. While research activities on strain monitoring of CNT based epoxy subject to tensile and flexural tests are present in the literature, works on the crack growth sensing capability under fracture toughness tests have not been reported. The present paper was therefore aimed to investigate the piezoresistive characteristics of epoxy-based nanocomposite loaded at different SWCNTs contents i.e. 0.25, 0.5 and 0.75 wt%, under tensile and mode I fracture toughness tests in order to compare and correlate the developed microstructures, including well-dispersed CNTs and aggregates, with the electromechanical properties of the epoxy based nanocomposites. SEM and FESEM analysis were conducted to investigate the fracture surface morphology and the state of the CNTs dispersions. A uniformly dispersed CNTs as well as proper cohesive bonding were obtained at CNTs content of 0.25 and 0.5 wt% whereas CNTs loading of 0.75 wt% resulted in excessive amount of aggregates accompanied with weak CNTs/epoxy bonding. Tunneling effect between neighboring CNTs was the dominant conductive mechanism, responsible for achieving proper sensitivity. For tensile tests, at a relatively low strain (less than 1%), the highest sensitivity was obtained at a SWCNTs content of 0.25 wt%, however showing a nonlinear trend in normalized resistance versus strain, i.e. the sensitivity increased by increasing strain. In fracture test, the SWCNT-doped materials were found to be capable of detecting damage initiation and extension by showing an abrupt increase in normalized electrical resistance upon the onset of crack growth while the piezo-resistivity before failure was either linear or nonlinear depending on the CNTs loading.

Owing to the high strength-to-weight and stiffness-to-weight ratios, composite materials are today essential building blocks for a wide range of industrial applications. However, their complex microstructures make it difficult to predict their failure mechanisms and residual lives under varying external loads. In situ health monitoring systems have received much attention in recent years as promising solutions for the above-mentioned limitations of composite materials. Here, we suggest a coupled health monitoring system in which infrared thermography and electrical resistance measurements are simultaneously applied for diagnosing the damage state of composite samples during tensile testing. In addition, we build a multiphysics simulation framework to model the interplay between the physical phenomena occurring in the three damage stages, involving crack propagation, variation in the temperature profile, and electrical resistance. The coupled electro-thermal monitoring system allows the estimation of the “damage stress ( σ D )”, which represents the onset of a micro-damage and may be correlated to the fatigue strength of the material and the assessment of the damage evolution process in glass fiber-reinforced polymer composites under quasi-static tensile loading. The proposed simulation framework enables the simultaneous understanding of the multiple physical phenomena occurring in the composite at the instance of crack nucleation and propagation.

Sensor placement and structural health classifiers are fundamental components of Structural Health Monitoring (SHM) systems, as they largely define system detection (or classification) performance. Optimal sensor placement strategies are designed to maximize the ability to detect damage or to minimize lifetime costs, given limited resource availability. However, usually choosing one strategy over the other and non-optimal detector implementation may provide poorly performing solutions in terms of detection performance or total cost, even though both are critical objectives for a cost-effective SHM system implementation. The work proposes a unique and coherent framework for optimal detector and sensing network design for SHM. After an optimal detector is defined based on the Neyman-Pearson likelihood ratio test, classification performance indexes are used in a multi-objective optimization paradigm for optimal sensor placement. Specifically, the optimization considers maximizing the classification performances and, simultaneously, minimizing a measure of total cost or risk in a Bayesian sense. Even though the approach is general for any structure and sensor measurement process, the method is numerically verified with a cracked plate under tension and monitored by measurements of local strain serving as the surrogate SHM system. The results are also validated by comparing the multi-objective optimal design to engineering judgment and single-objective-based solutions in terms of probability of detection and costs. The advantages of an optimization scheme are emphasized with respect to an engineering scheme and, above all, how a multi-objective optimization strategy reflects a conjunct saving in costs and improvement in detection performances.

In the last years, scientific and industrial communities have put a lot of efforts into the development of a new framework for the assessment of structural integrity, generally known as Structural Health Monitoring (SHM), which should allow real-time, automatic evaluations of the state of the structures based on a network of permanently installed sensors. In the context of mechanical, aerospace and civil structures, several approaches have been proposed to address the SHM problem, yet, it remains often difficult to diagnose damages and estimate the structural health when dealing with varying operating and environmental conditions. Particle Filters have already been proposed as a time-domain-based method in the field of SHM, showing promising results as estimators of hidden, not directly observable states, such as those typically related to damages. At the same time, neural networks-based autoencoders have been proposed for structural damage detection, demonstrating to be capable of capturing damage-related features from vibration measurements. This work aims at exploiting the individual advantages offered by the two approaches by combining them in a novel algorithm for structural damage detection and localization, robust with respect to changing environmental conditions. The algorithm is further equipped with a fault indicator module stemming from the introduction of an automatic threshold and both deterministic and probabilistic fault indicators, thus offering a complete, valuable tool for supporting decision making with limited human intervention. The method is demonstrated with reference to a numerical MDOF system whose parameters are taken from a literature benchmark case study.

Epoxy coatings doped with graphene nanoplatelets (GNP) with average thickness close to 200 μm have been manufactured on glass fibre laminate substrate. Their electrical conductivity was close to 0.001–0.01 S/m because the GNP percentages added (8–12 wt% GNP) were higher than the electrical percolation threshold of this GNP/epoxy system. The electrical current increases exponentially with the applied voltage due to the self-heating of the samples. Therefore, these materials don't follow the Ohm's law. Interestingly, the electrical resistance remains constant, or even decreases, at cryogenic temperatures. Self-heating of GNP/epoxy coatings due to Joule's effect was also studied, analysing the effect of the applied voltage. The coating doped with the highest GNP content presented more efficient heating due to its higher electrical conductivity and therefore higher transported electrical current. The application of a relatively high voltage, 750–800 V, induced the self-heating of materials, which was used for anti-icing and de-icing applications. Different thermoelectrical tests at low temperatures, between −10 and −30 °C, have been designed and carried out, confirming the high efficiency of these materials as an anti-icing and de-icing system (ADIS) which required low electrical power, close to 2.5 W, showing a short time to melt the ice and high reproducibility.