Structural Health Monitoring Applications Research Articles

A flexoelectric actuator model with shear-lag and peel stress effects

Flexoelectric materials are being extensively studied in recent years for potential sensing, actuation, energy harvesting and structural health monitoring applications. However, no analytical model exists in the literature to predict the stress transfer between a flexoelectric transducer and the host structure, considering bonding compliance. In this article, we present an analytical model for the interfacial stresses and deformations of flexoelectric patch actuator(s) bonded to an isotropic host plate considering both shear-lag and peel stress effects at the interface(s). The model is valid for both piezoelectric and flexoelectric effects. The system’s one-dimensional governing equations are derived consistently from the three-dimensional equations for linear dielectric solids obtained using the bulk electric Gibbs energy density function and extended Hamilton’s principle. The transducer and substrate, modelled as classical Kirchhoff plates, undergo both extension and bending deformations. The formulation produces a seventh-order differential equation for the interfacial shear stress, which is solved analytically, satisfying all boundary conditions. The solution is validated by comparing with the available results for piezoelectric actuators with a non-rigid interface and those of flexoelectric actuators based on the pin-force-moment model assuming a rigid interface. Detailed numerical studies are presented to show the effect of the plate, actuator and adhesive parameters on the interfacial stresses and deformations. Finally, the size effect of the actuators at the micro-scale is illustrated.

Progress in FEM modeling on mechanical and electromechanical properties of carbon nanotube cement-based composites

Abstract Carbon nanotubes (CNTs) reinforced cementitious composite (CNRC) with excellent electrical and self-sensing properties, which enables it to serve as an intrinsic sensor for structural health monitoring (SHM). However, the requirements of modern industry for accurate calculation and performance design of engineering materials are not met by traditional experimental studies alone. The finite element method (FEM) has the advantages of simplicity of operation, accuracy, and cost-effectiveness, and it has been widely used in the property verification and prediction of various composite materials. In this article, the constitutive model, FEM modeling method, and simulation process of CNRC along with existing model types, innate relations, and model parameters are reviewed, and the corresponding mechanical, electrical, and electromechanical coupling properties of CNRC under different parameters are systematically analyzed by FEM method. By combining different uncertainty parameters and model types, the advantages and disadvantages of FEM for mechanical, electromechanical coupling, and SHM applications of CNRC modeling are explored. The results are in good agreement with those in the existing CNRC experiment, which effectively proves the reliability of the FEM method in CNRC research. This work is important to develop a sound theoretical model verification and performance prediction for early applications in SHM of CNRC.

A Robust Deep Learning-Based Damage Identification Approach for SHM Considering Missing Data

Data-driven methods have shown promising results in structural health monitoring (SHM) applications. However, most of these approaches rely on the ideal dataset assumption and do not account for missing data, which can significantly impact their real-world performance. Missing data is a frequently encountered issue in time series data, which hinders standardized data mining and downstream tasks such as damage identification and condition assessment. While imputation approaches based on spatiotemporal relations among monitoring data have been proposed to handle this issue, they do not provide additional helpful information for downstream tasks. This paper proposes a robust deep learning-based method that unifies missing data imputation and damage identification tasks into a single framework. The proposed approach is based on a long short-term memory (LSTM) structured autoencoder (AE) framework, and missing data is simulated using the dropout mechanism by randomly dropping the input channels. Reconstruction errors serve as the loss function and damage indicator. The proposed method is validated using the quasi-static response (cable tension) of a cable-stayed bridge released in the 1st IPC-SHM, and results show that missing data imputation and damage identification can be effectively integrated into the proposed unified framework.

A novel wave tomography method for defect reconstruction with various arrays

In order to reconstruct the possible defects on the plate surface with arrays, a new wave tomography method based on the method of moments is established in this paper. According to the relationship between the probe number and grid amount, two algorithms, that is, the neural network and principal component analysis (PCA), are proposed and used to solve the ill-conditioned inversion equations. The neural network makes imaging feasible even if input data are not enough, and the PCA can greatly improve the computational efficiency via reducing the matrix dimension. Both numerical simulations and experimental measurements are conducted with the algorithm’s correctness and high precision validated. After investigating the influence of probe number on imaging quality, it is demonstrated that the algorithm can exactly predict the defect location when the input scattering data is not enough or fewer probes are arranged. More probes are needed for reconstructing the specific shape and thickness, especially when multiple defects are included. The qualitative results and quantitative data are conducive to providing some reference for engineering applications in nondestructive testing and structural health monitoring.

A Bulk Acoustic Wave Strain Sensor for Near-Field Passive Wireless Sensing.

Near-field passive wireless sensors can realize non-contact strain measurement, so these sensors have extensive applications in structural health monitoring. However, these sensors suffer from low stability and short wireless sensing distance. This paper presents a bulk acoustic wave (BAW) passive wireless strain sensor, which consists of two coils and a BAW sensor. The force-sensitive element is a quartz wafer with a high quality factor, which is embedded into the sensor housing, so the sensor can convert the strain of the measured surface into the shift of resonant frequency. A double-mass-spring-damper model is developed to analyze the interaction between the quartz and the sensor housing. A lumped parameter model is established to investigate the influence of the contact force on the sensor signal. Experiments show that a prototype BAW passive wireless sensor has a sensitivity of 4 Hz/με when the wireless sensing distance is 10 cm. The resonant frequency of the sensor is almost independent of the coupling coefficient, which indicates that the sensor can reduce the measurement error caused by misalignment or relative movement between coils. Thanks to the high stability and modest sensing distance, this sensor may be compatible with a UAV-based monitoring platform for the strain monitoring of large buildings.

Optimal sensor placement methodology of triaxial accelerometers using combined metaheuristic algorithms for structural health monitoring applications

Structural health monitoring applications present a valuable opportunity for cities to improve their infrastructures environmental life cycle, reduce operational maintenance cost and enhance their resiliency through early damage detection capabilities. The dilemma with structural health monitoring resides in balancing between the number of sensors to be installed and the amount of data that needs to be transferred, stored, and managed. These technical complications directly impact the cost efficiency of such solutions. Thus, limiting the wide range deployment of such technology. Henceforth, Optimal Sensor Placement (OSP) methodologies offers the possibility to optimize the sensor configuration that will ensure a proper and cost-efficient monitoring system to obtain the maximum modal information related to any infrastructure type.In this study, a novel “Modal Assurance Criterion (MAC)”-based methodology with the aim of the optimal sensor placement is presented using a 410 m high rise structure. The Modal Kinetic Energy (MKE) and a MAC-based objective function are combined in the suggested methodology. The optimization problem is solved using a new hybrid metaheuristic algorithm that combines Teaching–Learning-Based Optimization (TLBO), Artificial Bee Colonies (ABC), and Stochastic Paint Optimizer (SPO). With no user-defined parameters, the combination of algorithms exhibits up to 86% better fitness, avoids local optimums due to higher accuracy, and shows promising results for cost calculation optimization because it requires 50% to 70% fewer iterations compared to six evolutionary algorithms that are usually used.

Carbon black based self-sensing cement composite for structural health monitoring – A review on strength and conductive characteristics

Even though concrete buildings have high strength and durability, they are susceptible to damage from hazards, aging, etc. In concrete structures, carbon-based conductive compounds with the potential to increase the electrical conductivity of concrete are preferred. However, it was extremely difficult to handle the high cost that prevented the widespread use of conductive materials in concrete. Carbon black, a low-cost carbon-based conductive substance supports the development of self-sensing cement composite, which has significant applications in Structural Health Monitoring (SHM). This review concentrates on the alteration of compressive strength, split tensile strength, flexural strength and conductive characteristics of concrete properties using carbon black. Utilizing a scanning electron microscope (SEM), the volume and shape of porosities inside the carbon-black embedded cement composite were analyzed. The review revealed that self-sensing cement composite based on carbon black has high durability, cheap cost, and sensitivity. The analysis found that the carbon content, size, and conductivity of the carbon black utilized significantly impact the piezoresistivity of cement composites. Nevertheless, with an increase in carbon black concentration and under conventional curing, cement composites' compressive strength and flexural strength are significantly diminished. The conductivity of the cement composite is directly proportional to the concentration of carbon black; however, the piezoresistivity of the cement composite is not considerably attained beyond the percolation threshold. The review concludes that, despite the inclusion of carbon black with acceptable strength and good sensing characteristics, the carbon-black embedded self-sensing cementitious composite is an appropriate material for structural health monitoring of various concrete infrastructures under various environmental conditions.

Graphene nanoparticles as data generating digital materials in industry 4.0

One of the potential applications of 2D materials is to enhance multi-functionality of structures and components used in aerospace, automotive, civil and defense industries. These multi-functional attributes include sensing, energy storage, EMI shielding and property enhancement. In this article, we have explored the potential of using graphene and its variants as data generating sensory elements in Industry 4.0. We have presented a complete roadmap to cover three emerging technologies i.e. advance materials, artificial intelligence and block-chain technology. The utility of 2D materials such as graphene nanoparticles is yet to be explored as an interface for digitalization of a modern smart factory i.e. “factory-of-the-future”. In this article, we have explored how 2D material enhanced composites can act as an interface between physical and cyber spaces. An overview of employing graphene-based smart embedded sensors at various stages of composites manufacturing processes and their application in real-time structural health monitoring is presented. The technical challenges associated with interfacing graphene-based sensing networks with digital space are discussed. Additionally, an overview of the integration of associated tools such as artificial intelligence, machine learning and block-chain technology with graphene-based devices and structures is also presented.

In-Fiber Thermally Diffused Coupler and Fiber Bragg Grating Inscribed in Twin-Core Fiber for Sensitivity-Enhanced Vector Bending Sensing

A vector bending fiber sensor based on core-by-core inscribed fiber Bragg gratings in a twin-core fiber has been proposed and experimentally demonstrated. An in-fiber integrated vector bending sensor is realized by using the thermal diffusion technique to fabricate the coupler. The characteristics of the coupler fabricated by thermal diffusion are simulated and experimented. By inscribing fiber Bragg gratings with different reflection wavelengths in the two cores of a symmetrical twin-core fiber, the curvature sensitivity can be enhanced by tracking the wavelength difference between the fiber Bragg gratings of the two cores. The measured bending sensitivity of the fiber Bragg grating ranges from −161.6 pm/m−1 to +165.5 pm/m−1. The differential sensitivity of the two cores is twice that of a conventional single grating, and the temperature-induced crosstalk is also reduced. The bending sensor proposed in this paper has the advantages of high integration, enhancing the sensitivity and two-dimensional orientation recognizability, and reducing temperature crosstalk, which can be a promising candidate for structural health monitoring or wearable artificial electronics applications.

Evaluation of conductive smart composite polymeric materials for potential applicationsin structural health monitoring and strain detection

The presented work collects results from the evaluation of electrical response to mechanical deformation and formation of defects presented by different polymeric based composite materials with potential for applications in Structural Health Monitoring and Strain Detection. With the aim of showing the variety of key materials in sectors like civil aviation, wind energy, automotive or railway that present this ability, specimens of very different nature have been analyzed: a) thermoplastic commercial 3D printing filaments loaded with carbonic fillers; b) epoxy resin loaded with Carbon Nanotubes and c) long carbon fiber reinforced resin composite. Measurements of electrical properties of these materials were taken to evaluate their capability to detect the presence of structural defects of different sizes as well as its spatial location. On the other hand, simultaneous measurements of electrical resistivity and mechanical strain during tensile tests were performed to analyze the potential of materials as strain detectors. All composites studied have shown a positive response (modification of electrical performance) to external mechanical stimulus: induced damage and deformations.Graphical

Improved undamaged-to-damaged acceleration response translation for Structural Health Monitoring

Unpaired image-to-image translation is a popular research topic in computer vision and graphics. Recently, the authors of this paper took a similar approach and translated the domain of acceleration responses collected from a steel grandstand structure. In doing so, the undamaged response is translated to damaged, and the damaged response to undamaged. For that, a variant of the CycleGAN model is trained with undamaged (bolt tightened at the joint) and damaged (bolt loosened at the joint) responses from a single joint in the structure. However, the success of the domain translation on the test joints was very limited. Thus, this study investigates improvements to the model and the training procedure for further accuracy. First, the model in this study gets a more extensive training procedure to increase the model’s domain knowledge. During the training, a novel signal coherence-based index is considered to account for the similarity of frequency domains of the original and the translated data. Second, the Gated Linear Units, skip-connections, and Mish activation function are used to minimize the gradient loss and to learn the broader features in the data. Third, the total loss function of the generator is supplemented with a new frequency domain-based loss to better capture the frequency content of the data. Fourth, random decaying noise is added to the inputs for better generalization in the test data. Last, the model is evaluated using modal parameters such as natural frequencies, damping ratios, and singular value decomposition of the estimated spectral densities. The improvements presented in this study demonstrate a successful domain translation of acceleration responses for the tested joints compared to past study. The findings of this paper show that domain translation can be advantageous in Structural Health Monitoring applications, such as having access to the damaged or undamaged response of the structure while it is in pristine or unhealthy condition.

Development and evaluation of conductive ultra-lightweight cementitious composites for smart and sustainable infrastructure applications

The stability of electrical conductivity of self-sensing cementitious composites under different environments is the key to the application of structural health monitoring using electrical resistivity/impedance tomography. In addition, the lightweight of concrete is becoming a new requirement for sustainable development. This study aims to develop long carbon fibre (LCF) reinforced ultra-lightweight cementitious composites (ULCC) to address these issues. The mechanical properties and electrical and thermal conductivities of composites were investigated. The conductivity of LCF-reinforced ULCC under different testing methods (AC and DC), frequencies, temperatures, water content in the mixture, and curing ages were examined. The electromechanical properties of LCF-reinforced ULCC under compression (monotonic, cyclic loading under different loading peaks and loading rates) and tension were also tested. Except for the content, the effects of the pre-heating treatment of carbon fibre on the conductivity and electromechanical properties were compared. The experimental results revealed that LCF can remove the polarization effect and maintain the stability of the electrical conductivity of LCF-ULCC under different environments. The LCF can build a complete and continuously 3D conductive pathway, and free electrons can transport stably in the matrix, which can diminish the effect of electrolytic ions on the conductivity. The LCF-reinforced ULCC can show sensitive electromechanical ability where LCF is better used for self-sensing under tension when compared with compression. The conductivity of composites increases with increasing LCF content but initially increases and then decreases with increasing the pretreatment heating temperature of LCF. It is efficient to increase the conductivity and mechanical properties of composites by heating LCF to 300 °C. The composites (Oven-dry density: 990–1100 kg/m3) are a good thermal insulation material and the thermal conductivity is between 0.35 and 0.45 W/mK. Finally, microstructural analysis was conducted and the mechanisms of the improved electrical conductivity and thermal insulation of the composites were discussed.

Development of fiber-based piezoelectric sensors for the load monitoring of dynamically stressed fiber-reinforced composites

Continuous load monitoring of fiber-reinforced composites represents a complex challenge for the composite sector. In this paper, the development and characterization of piezoelectric sensors for structural health monitoring applications based on polyvinylidene fluoride (PVDF) are presented. The basic sensor structures are melt spun bicomponent filaments in a core-sheath configuration. The core is a volumetric mixture of polypropylene (PP) and Pre-Elec PP, a PP-compound modified with carbon black. The sheath is made of PVDF. Three variants with increasing volumetric Pre-Elec ratio in the core (50 vol.%, 60 vol.% and 70 vol.%) are manufactured and analyzed by conducting optical and resistance measurements as well as tensile tests. In the subsequent process step, the bicomponent filaments are braided with copper fine wires. The PP core and copper braiding of the coaxial tricomponent yarn are used as inner and outer electrode, respectively. By generating an electrostatic high voltage field between these electrodes, the PVDF interlayer is subjected to contact polarization to initialize the piezoelectric effect. The piezoelectric characteristics and thereby the sensory potential of the produced sensor yarns are quantified and analyzed on the fiber and composite scale by investigating their piezoelectric behavior during cyclic tensile tests. The results of the different sensor yarn variants are compared with each other by evaluating the measured voltage signal as a function of the induced cyclic stresses.

Performance evaluation of graphite-integrated smart-engineered cementitious composite for health monitoring of structural components

This research explores the electro-mechanical characteristics of graphite powder (GP) integrated Self-Sensing Cement Composite (SCC). In addition to the development, the created SCC is used in beams and columns to evaluate their structural performance under flexural and compressive load, respectively. Field Element Scanning electron microscopy (FESEM) analysis is conducted to examine the morphological characteristics of the optimized SCC. X-Ray diffraction (XRD) analysis is used to examine the formation of chemical constituents responsible for the property variation. The research findings indicate that the resistivity of composites containing 25% GP is lowered by 43.90%. Due to the hydrophilicity between GP and cement, the strength of SCC is lowered as the concentration of GP increases. The SCC embedded in the flexural zone of beams has the capacity to detect deformation with an accuracy of 98%. The electrical resistivity of SCC in columns rapidly decreases with increasing deflection as a result of SCC compression, indicating that SCC can monitor column deformation with an efficiency of about 96% up until the elastic regime. After characterization, strength, and resistivity evaluations, GP is finally validated as a promising material for SCC development and application in structural health monitoring.

Self-learning locally-optimal hypertuning using maximum entropy, and comparison of machine learning approaches for estimating fatigue life in composite materials of the aerospace industry

Applications of Structural Health Monitoring (SHM) combined with Machine Learning (ML) techniques enhance real-time performance tracking and increase structural integrity awareness of civil, aerospace and automotive infrastructures. This SHM-ML synergy has gained popularity in the last years thanks to the anticipation of maintenance provided by arising ML algorithms and their ability of handling large quantities of data and considering their influence in the problem.In this paper we develop a novel ML nearest-neighbors-alike algorithm based on the principle of maximum entropy to predict fatigue damage (Palmgren–Miner index) in composite materials by processing the signals of Lamb Waves – a non-destructive SHM technique – with other meaningful features such as layup parameters and stiffness matrices calculated from the Classical Laminate Theory (CLT). The full data analysis cycle is applied to a dataset of delamination experiments in composites. The predictions achieve a good level of accuracy, similar to other ML algorithms, e.g. Neural Networks or Gradient-Boosted Trees, and computation times are of the same order of magnitude.The key advantages of our proposal are: (1) The automatic determination of all the parameters involved in the prediction, so no hyperparameters have to be set beforehand, which saves time devoted to hypertuning the model and also represents an advantage for autonomous, self-supervised SHM. (2) No training is required, which, in an online learning context where streams of data are fed continuously to the model, avoids repeated training—essential for reliable real-time, continuous monitoring.

Sandwich Face Layer Debonding Detection and Size Estimation by Machine-Learning-Based Evaluation of Electromechanical Impedance Measurements

The present research proposes a two-step physics- and machine-learning(ML)-based electromechanical impedance (EMI) measurement data evaluation approach for sandwich face layer debonding detection and size estimation in structural health monitoring (SHM) applications. As a case example, a circular aluminum sandwich panel with idealized face layer debonding was used. Both the sensor and debonding were located at the center of the sandwich. Synthetic EMI spectra were generated by a finite-element(FE)-based parameter study, and were used for feature engineering and ML model training and development. Calibration of the real-world EMI measurement data was shown to overcome the FE model simplifications, enabling their evaluation by the found synthetic data-based features and models. The data preprocessing and ML models were validated by unseen real-world EMI measurement data collected in a laboratory environment. The best detection and size estimation performances were found for a One-Class Support Vector Machine and a K-Nearest Neighbor model, respectively, which clearly showed reliable identification of relevant debonding sizes. Furthermore, the approach was shown to be robust against unknown artificial disturbances, and outperformed a previous method for debonding size estimation. The data and code used in this study are provided in their entirety, to enhance comprehensibility, and to encourage future research.

Concurrent Prospects to Develop Activated Charcoal Reinforced Self-Sensing Cement Composites for Structural Health Monitoring Applications

This research examines the electromechanical characteristics of self-sensing cement composite (SCC) containing activated charcoal (AC) at various concentrations (5%, 10%, 15%, 20%, and 25%). The developed composite is placed in various zones in the beam to monitor beam behaviour, as well as in the column center to monitor column deflection. The research reveals that AC reduced the compressive strength and resistivity of cement composites by generating local hydration and carbon accumulation. The performance index approach optimizes AC concentrations at 25% and 20% without and with 10% silica fume (SF), respectively. The embedded SCC has monitored the deflection of beams and columns with a maximum correlation between electrical resistivity and deflection at 99% and 96%, respectively. According to the findings, the AC can generate SCC, which might be utilized to monitor the deflection of beams and columns.

Noncontact Sensing Techniques for AI-Aided Structural Health Monitoring: A Systematic Review

Engineering structures and infrastructure continue to be used despite approaching or having reached their design lifetime. While contact-based measurement techniques are challenging to implement at a large scale and provide information at discrete locations only, noncontact methods are more user-friendly and offer accurate, robust, and continuous spatial information to quantify the structural conditions of the targeted systems. Advancements in optical sensors and image-processing algorithms increased the applicability of image-based noncontact techniques, such as photogrammetry, infrared thermography, and laser imaging for structural health monitoring (SHM). In addition, with the incorporation of artificial intelligence (AI) algorithms, the assessment process is expedited and made more efficient. This article summarizes the efforts made in the last five years to leverage AI-aided noncontact sensing techniques for applications in SHM with an emphasis on image-based methods. Future directions to advance AI-aided image-based sensing techniques for SHM of engineering structures are also discussed.

Ultra-long chaotic FBG sensing with high-order random fiber lasing amplification.

We propose and demonstrate an ultra-long chaotic fiber Bragg grating (FBG) sensing system based on wavelength-scanning correlation optical time-domain reflectometry (COTDR) assisted by sixth-order random fiber lasing amplification (RFLA). Cascaded random Raman fiber lasing generated in the long fiber span can provide up to sixth-order distributed Raman amplification for the chaotic probe light and its echo signal without ruining the chaotic behavior, which can significantly extend the sensing distance of COTDR. As a result, a 152-km-long wavelength-scanning COTDR is experimentally demonstrated to simultaneously realize FBG sensing and location with a spatial resolution as high as 6 cm, which is the longest COTDR to the best of our knowledge. Temperature sensing of the specific FBG is performed, and the temperature sensitivity of the proposed system is 0.25 dB/°C with a good linearity. The proposed chaotic FBG sensing system with high-order RFLA can act as a new platform for ultra-long, large-capacity FBG sensing, which has potential applications in overhead transmission powerline monitoring and structural health monitoring.

Dynamic response of a damaged bridge model traversed by a heavy point mass

The dynamic response of a single-span, damaged bridge model traversed by a point mass whose magnitude is comparable to that of the underlying girder is studied for the purpose of detecting structural damage. Damage is represented in the form of vertical cracks running across the girder's web, which modifies the eigenproperties of the original intact girder. The weight of the mass during its traverse time also modifies the eigenproperties of the girder and its motion produces an additional damping effect. Both features are modelled as Dirac delta distributions, one accounting for the spatial discontinuity in the slope of the deflection, and the other for the temporal discontinuity as the point mass moves forward with constant velocity. Although each type of discontinuity has been treated in the past, their combined effect has not been satisfactorily addressed. Next, a novel time-marching scheme is introduced for treating the temporal discontinuity, where the mass, stiffness and damping of the girder are all updated at the end of a given time interval. Then, the equations of motion are cast as a first-order, matrix differential equation. A parametric study has shown that two eigenvalue-eigenfunction pairs are sufficient for convergence purposes. Following modal synthesis, the transient velocities at any point on the girder during both forced and free vibration regimes are processed by the short-time Fourier transform as a means for detecting changes in the resulting spectograms that are attributable to damage. Finally, support failure is also examined by the introduction of flexible translational springs at the ends. The methodology developed here can be used to identify damage from the recorded kinematic spectrograms at any station on the span of the girder due to a travelling mass. In closing, this work finds applications in structural health monitoring of structures, which is a contemporary and challenging problem facing the engineering profession.