Structural Health Monitoring Applications Research Articles

On the consistent classification and treatment of uncertainties in structural health monitoring applications

Abstract In this paper, we provide a comprehensive definition and classification of various sources of uncertainty within the fields of Structural Dynamics, System Identification, and Structural Health Monitoring (SHM), with a primary focus on the latter. Utilizing the classical input-output system representation as a main contextual framework, we present a taxonomy of uncertainties, intended for consistent classification of uncertainties in SHM applications: (i) input uncertainty; (ii) model form uncertainty; (iii) model parameter/variable uncertainty; (iv) measurement uncertainty; and (v) inherent variability. We then critically review methods and algorithms that address these uncertainties in the context of key SHM tasks: system identification and model inference, model updating, accounting for environmental and operational variability, virtual sensing, damage identification, and prognostic health management. A benchmark shear frame model with hysteretic links is employed as a running example to illustrate the application of selected methods and algorithmic tools. Finally, we discuss open challenges and future research directions in uncertainty quantification for SHM.

Structural Damage Detection Using PZT Transmission Line Circuit Model

Arrangements of piezoelectric transducers, such as PZT (lead zirconate titanate), have been widely used in numerous structural health monitoring (SHM) applications. Usually, when two or more PZT transducers are placed close together, significant interference, namely crosstalk, appears. Such an effect is usually neglected in most SHM applications. However, it can potentially be used as a sensitive parameter to identify structural faults. Accordingly, this work proposes using the crosstalk effect in an arrangement of PZT transducers modeled as a multiconductor transmission line to detect structural damage. This effect is exploited by computing an impedance matrix representing a host structure with PZTs attached to it. The proposed method was assessed in an aluminum beam structure with two PZTs attached to it using finite element modeling in OnScale® software to simulate both healthy and damaged conditions. Similarly, experimental tests were also carried out. The results, when compared to those obtained using a traditional electromechanical impedance (EMI) method, prove that the new approach significantly improved the sensitivity of EMI-based technique in SHM applications.

Ultraviolet light charged and self-powered mechanoluminescence of KMgF3: Tb3+ glass ceramics in various media

The light waveguiding effect of transparent glass ceramics (GCs) on mechanoluminescence (ML) signals is highly attractive for remote stress sensing and structural health monitoring applications. However, a prerequisite for generating ML in GCs is pre-irradiation with high-energy rays, which not only increases application costs but also poses a risk of GCs devitrification. Herein, we propose a safe, non-destructive, and convenient charging method for the ML of KMgF3: Tb3+ GCs, namely ultraviolet (UV) light charging. By effectively filling the intrinsic defects in KMgF3: Tb3+ nanocrystal using UV light, we achieved trap-controlled ML in rigid GCs. Moreover, this ML is attributed to thermoluminescence (TL) induced by frictional heating during mechanical stimulation. Additionally, when the GCs powder is combined with flexible polydimethylsiloxane (PDMS), a self-powered (without any pre-charging) ML induced by interfacial triboelectricity can also be achieved, which is similar to the results observed in most flexible ML systems. This work proposes a more straightforward and safer approach for applications such as mechanical sensing based on the ML of transparent GCs.

Numerical investigation of bandgap characteristics in integrated metallic metafilters for nonlinear guided wave applications

Guided waves propagating in nonlinear media, featuring second harmonic generation, represent a promising avenue for early-stage damage detection due to their high sensitivity and long-range propagation capabilities. However, nonlinear ultrasonic measurements are hindered by nonlinearities induced by the experimental system, necessitating careful calibrations that have restricted their application to laboratory settings. While several phononic crystal and metamaterial designs have been devised to enhance nonlinear-based ultrasonic testing, most are tailored for suppressing second harmonics within a frequency range of 100–300 kHz, primarily utilizing low-frequency excitation. In this paper, we propose a metallic ring-shaped metafilter designed to explore high-order bandgaps. To fully understand the bandgap characteristics, we begin by analyzing mode shapes, providing insights into the underlying wave mechanics. The efficacy of the designed filter is subsequently assessed through 3D time step elastodynamic simulations. In addition, this study underscores the significance of parameters such as the number of rings employed in the filter, signal duration, and bandgap width in optimizing its performance. Furthermore, the observed mode conversion phenomena from S0 to A0 guided wave modes underscore the filter’s capacity to influence guided wave propagation. The defect localization technique, based on the time difference of arrival of second-order wave modes, accurately predicts the defect location with an error margin of less than 0.2%. The present investigation showcases advancements in the sensitivity of nonlinear-based guided wave testing for characterizing microstructural changes, promising substantial potential for detecting incipient damage in practical structural health monitoring applications.

Transfer learning for probabilistic localization of hidden cracks in concrete structures

AbstractThe utility of discriminative supervised learning models built using multiple training-data sources is investigated for hidden crack localization in concrete. Feed-forward neural network (FFNN) is chosen as the model architecture, and transfer learning is used to assimilate the information obtained from different sources (computational physics simulations and laboratory experiments). The labeled training data consists of values of a damage index and the known locations of hidden cracks. The classification models need to learn how the presence of damage (hidden cracks) affects the damage index at different sensors for different test conditions. To this end, diagnostic FFNN models are built by sequentially adding and training new hidden layers to assimilate labeled information from computer models (different model geometries, test conditions, crack lengths, crack locations) and laboratory experiments on a plain cement slab. These transfer learning-based models are then used to localize damage in concrete specimens that reflect real-world conditions (i.e., specimens with steel reinforcement and randomly distributed aggregate). The actual damage state in these specimens is determined by extracting cores and performing petrographic studies on the extracted cores. The damage probability estimated by transfer learning-based models is compared with the petrographic damage rating index (DRI) to identify the most suitable approach to train the diagnostic models. The transfer learning-based diagnostic methodology shows promise and could be used in various structural health monitoring applications, where sufficient labeled data are typically not available from a single data source.

A review on early-age cracking of concrete: Causes and control

The early-age cracking of concrete is one of the most critical parameters affecting the performance of concrete structures. It is attributed to both the internal and external influences such as early age restrained shrinkage, thermal deformation as well as the applied load. In this paper, a detailed review of the causes of early-age cracking is first presented, followed by the factors affecting the concrete cracking including raw materials quality problems, improper construction measures, external environmental influences and structural design flaws. Then the preventive measures for concrete cracking are summarized. The potential for the application and development of structural health monitoring and artificial intelligence techniques for early-age cracking of concrete is also discussed. Through the comprehensive review of the early-age cracking of concrete, the research direction for the subsequent research on early-age cracking of concrete is pointed out. The current problems and concerns of concrete cracking are also prospected.

Maximum Fourier spectrum cyclostationarity blind deconvolution and its application in structural health monitoring of power transformers

Abstract Power transformer is the most important equipment that affects whether the electric power system can be operated safely and normally, whose condition assessment problem has attracted considerable attention. Background noise frequently affects the effectiveness of nonintrusive techniques based on vibro-acoustic signals for structural health monitoring in power transformers. It is challenging to effectively eliminate background noise interference from time-frequency domain transformer defect detection techniques such as wavelet transformations, modal decomposition, etc. In this scenario, the Fourier spectrum cyclostationarity index (FSC) is designed based on the cyclostationarity index used for rotating machinery fault diagnosis to construct a maximum Fourier spectrum cyclostationarity blind deconvolution method (MFSCBD) for transformer fault detection in this paper. Firstly, the limitations of the traditional blind deconvolution (BD) in transformer fault detection are discussed in the mathematical principle. Then, a new BD framework based on Kepler optimization algorithm is proposed according to the principle of convex optimization to address the problems of difficulty in solving the complex blind objective function in the traditional differential BD framework and the ill-condition problem in the Rayleigh quotient BD framework. Subsequently, a synthetic nonstationary and nonlinear simulation signal is constructed for numerical verification, and a six-microphone array is designed to obtain the practical signals from the operating transformer to verify the performance of MFSCBD. Finally, the applications on the simulated and experimental signals of power transformers demonstrate that MFSCBD outperforms complete ensemble empirical mode decomposition with adaptive noise and successive variational mode decomposition to some extent for structural health monitoring.

Investigating the mechanical properties of composites containing exfoliated graphite/epoxy sensors: Experimental testing and simulation

One approach to structural health monitoring (SHM) involves embedding sensors within a composite material. However, the integration of these sensors can potentially introduce flaws that might affect the composite’s mechanical properties. This research aims to explore the impact of embedding exfoliated graphite (EG)/epoxy sensors on the mechanical characteristics of composite systems through laboratory experiments and numerical simulations. Sensor strips composed of varying volume fractions of EG/epoxy were fabricated. Tensile test specimens were prepared by embedding these sensors in the epoxy matrix oriented both lengthwise and widthwise. Baseline specimens of EG/epoxy without sensors were also created for comparison. Tensile tests were performed on the samples to evaluate the effects of the embedded sensors on the composite’s elastic modulus and tensile strength. The results indicated a slight improvement in both elastic modulus and tensile strength with the introduction of EG. Crucially, the orientation of the sensors within the samples had a significant impact on the composite’s mechanical properties. Samples with widthwise-aligned sensors showed reduced tensile strength due to delamination along the sensor edges. Finite element simulations using a viscoelastic model based on the experimental data were conducted to analyze the effect of sensor alignment on mechanical properties. The findings revealed that a grid pattern alignment of sensors significantly enhanced mechanical performance compared to lengthwise or widthwise alignment, particularly at 0.1% and 0.3% EG volume fractions, highlighting the effectiveness of a grid pattern for embedding sensors in SHM applications.

Study and modeling of the ultrasonic ambient noise induced by the fluid-structure interaction in a pipe

An elastic guided wave-based structural health monitoring (SHM) system consists in examining the integrity of a structure through the propagation of elastic waves. In this context, passive methods turn initial hindrances into advantages by making use of the ambient ultrasonic noise in a structure in operation, through the cross-correlation. Although they have been empirically applied in several configurations, no model is available to predict the link between the flow regime and the convergence of the ambient noise correlation towards the impulse response of the system. Considering the different parameters involved in the case of a pipe, a physical approach of this issue may be studied through the turbulent boundary layer, which has long been described by the Corcos model. Corcos-like models have essentially been applied so far on the audible range, below 1 kHz, where we are interested in frequencies around 100 kHz, typical for SHM applications. The semi-empirical model allows to determine a wall boundary pressure, which, applied at the pipe's inner boundaries, acts as a source term for ultrasonic elastic waves propagating in the pipe. Our goal is then to study if such a model can be used at these high frequencies to reproduce the experimental observations.

A quadrilateral inverse plate element for real-time shape-sensing and structural health monitoring of thin plate structures

The inverse finite element method (iFEM) emerged as a powerful tool in shape-sensing and structural health monitoring (SHM) applications with distinct advantages over existing methodologies. In this study, a quadrilateral inverse-plate element is formulated via a sub-parametric approach using bi-linear and non-conforming cubic Hermite basis functions for engineering structures, which can be modeled as thin plates. Numerical validation involves dense and assumed sparse sensor arrangements for in-plane, out-of-plane, and mixed general loading conditions. iFEM analysis reveals efficient monotonic convergence to analytical and high-fidelity finite element reference solutions. After successful numerical validation, defect detection analysis is performed considering minute geometric discontinuities and structural stiffness reduction because of latent subsurface defects under tensile and transverse loading conditions. The inverse formulation successfully resolves the presence of simulated defects under a sparse sensor arrangement. The proposed inverse-plate element is accurate in the full-field reconstruction of shape-sensing profiles and reliable in defect identification and quantification in thin plate structures.

Hydro-steel structure digital twins: Application in structural health monitoring and maintenance of large-scale reservoir

In the context of frequent accidents during hydro-steel structures (HSS) operations due to harsh environments and extended service conditions, a novel approach is proposed to reduce the frequency of structural failure incidents and ensure safe and reliable operation. The approach begins with introducing a comprehensive DT modeling framework. Subsequently, detailed DT modeling and DT-based SHM methods are developed. Finally, a platform with perception, interaction, analysis, and decision-making for intelligent health monitoring and maintenance of HSS is constructed and validated in China’s large-scale reservoir project, Luhun Reservoir. The platform includes functions of condition monitoring, fault feature recognition, health status assessment, and maintenance strategies optimization. The integration of DT technology has led to significant improvements in health monitoring and maintenance quality, which includes data collection, model optimization, comprehensive evaluation, and decision-making. This approach has also demonstrated its effectiveness by reducing the operation and maintenance response time and enhancing the overall efficiency and reliability.

Effect of epoxy resin addition on the acoustic impedance, microstructure, dielectric and piezoelectric properties of 1–3 connectivity lead-free barium zirconate titanate ceramic cement-based composites

In this work, 1–3 connectivity barium zirconate titanate ceramic cement-based composites were fabricated using Portland cement and epoxy resin as the matrix. Barium zirconate titanate (BZT) of 40–60 % by volume was used while epoxy was used with cement at 0–7% by volume. Dielectric and piezoelectric properties, and other properties such as acoustic impedance, density and microstructure were investigated. It was found that epoxy resin can be used in combination with BZT to achieve a suitable acoustic impedance value (9–11 × 106 kg/m·s2) matching that of concrete for structural health monitoring application. Thus, when epoxy resin was used at 7 %, BZT volume can be increased to 60 % where the highest d33 value of 93 pC/N was found and remain within the acoustic matching range. In addition, when epoxy resin was increased, both piezoelectric charge coefficient (d33) and piezoelectric voltage coefficient (g33) were also found to increase. This is likely due to the lower porosity thus denser matrix when epoxy resin was used in addition to cement.

An unsupervised learning framework for health diagnosis by incorporating multiscale data

Integrating real-time evaluations of health states with Structural Health Monitoring (SHM) data and environmental observations is crucial, particularly under large datasets. Not only is the volume of real-time data analysis substantial, but traditional algorithms like wavelet analysis often require extensive prior work and significant manual input, limiting the application of SHM and big data technologies in structural analysis. To overcome this limitation, this study introduces an unsupervised analytical framework coupling a Denoising Sparse Wavelet Network (DeSpaWN) and a dynamic-inner Principal Component Analysis (DiPCA) algorithm. The DeSpaWN is designed to extract sparse representation from multiscale sensor signals, and the features are subsequently analyzed using the DiPCA to assess health diagnoses. The DeSpaWN is specifically tailored for monitoring high-frequency time series dynamically and adaptively, enabling reconstruction and extraction of signals across various frequency domains. Additionally, the DiPCA effectively handles large-scale time-series data, providing precise evaluations by accounting for dynamic latent correlations. To validate the method, a 5-Degree of Freedom (DoF) structure model is established, equipped with five virtual sensors at each DoF and an anemometer at the top DoF. The sensor signals, obtained under different health states and subjected to artificial excitations, are input into the proposed framework. Validation results demonstrate the effectiveness in synthesizing multi-sensor data and assessing structural integrity. The framework's practical utility is showcased through a real-world case study that assesses the structural integrity of a wind turbine. By integrating real and virtual sensor data collected over a year and cross-referencing this with the fault log, the study validates the algorithm's efficacy in processing multisource heterogeneous data and its precision in structural health diagnosis. The study substantiates real-time tracking of health states for SHM-equipped structures, underscoring its potential to enhance safety and reliability in civil infrastructure.

Autonomous Sensing Architected Materials

AbstractIntegrating autonomous sensing materials into future applications necessitates developing advanced multiscale multiphysics predictive models. This study introduces an experimentally informed predictive framework for autonomous sensing architected materials, combining theoretical and computational methodologies. By incorporating stress‐dependent electrical resistivity through anisotropic piezoresistive constitutive effects, alongside considering material, geometric, and contact nonlinearities, the proposed multiscale model captures the architecture‐dependent piezoresistive responses of lattice composites produced via additive manufacturing of polyetherimide (PEI)/carbon nanotube (CNT) nanoengineered feedstock. The PEI/CNT composite exhibits exceptional strength (105 MPa), stiffness (3368 MPa), and strain sensitivity (gauge factor ≈13), translating into remarkable piezoresistive characteristics for the PEI/CNT lattice composites, surpassing existing works (gauge factor ≈3 to 11). This multiscale finite element model accurately predicts both macroscopic piezoresistive responses and the influence of architectural and topological variations on electric current paths, validated via infrared thermography analysis. Additionally, an Ashby chart for the gauge factor of PEI/CNT lattice composites suggests their prediction through a scaling law similar to mechanical properties, underscoring the tunable strain and damage sensitivity of these materials. The combined experimental, theoretical, and numerical findings offer critical insights into optimizing piezoresistive composites through architected design, with profound implications for smart orthopedics, structural health monitoring, sensors, batteries, and other multifunctional applications.

Smart Carbon Fiber-Reinforced Polymer Composites for Damage Sensing and On-Line Structural Health Monitoring Applications.

High electrical conductivity, along with high piezoresistive sensitivity and stretchability, are crucial for designing and developing nanocomposite strain sensors for damage sensing and on-line structural health monitoring of smart carbon fiber-reinforced polymer (CFRP) composites. In this study, the influence of the geometric features and loadings of carbon-based nanomaterials, including reduced graphene oxide (rGO) or carbon nanofibers (CNFs), on the tunable strain-sensing capabilities of epoxy-based nanocomposites was investigated. This work revealed distinct strain-sensing behavior and sensitivities (gauge factor, GF) depending on both factors. The highest GF values were attained with 0.13 wt.% of rGO at various strains. The stability and reproducibility of the most promising self-sensing nanocomposites were also evaluated through ten stretching/relaxing cycles, and a distinct behavior was observed. While the deformation of the conductive network formed by rGO proved to be predominantly elastic and reversible, nanocomposite sensors containing 0.714 wt.% of CNFs showed that new conductive pathways were established between neighboring CNFs. Based on the best results, formulations were selected for the manufacturing of pre-impregnated materials and related smart CFRP composites. Digital image correlation was synchronized with electrical resistance variation to study the strain-sensing capabilities of modified CFRP composites (at 90° orientation). Promising results were achieved through the incorporation of CNFs since they are able to form new conductive pathways and penetrate between micrometer-sized fibers.

Enhancing structural damage evaluation of PC girder bridges through digital twinning Bayesian model updating

Developing a unified analytical framework to assess the structural performance of deteriorated prestressed concrete (PC) girder bridges is important for their maintenance. This study aims to construct a physics-based digital twin framework for PC girder bridges incorporating Bayesian model selection and updating, a high-fidelity three-dimensional (3D) finite element (FE) model, and a method for simulating prestressed tendon damage. In this study, three PC girder specimens were designed and tested to evaluate the effects of tendon rupture near the anchorage region on structural performance. Bayesian model selection was used to determine the most plausible model class based on the estimated model evidence. The most probable values (MPVs) of the model parameters, derived from the estimated posterior probability density functions (PDFs), were then used to calibrate the FE model, which reflects a healthy state of the PC girder bridge. This updated FE model serves as the baseline for conducting what-if scenario simulations. Subsequently, nonlinear segments of the constitutive model, obtained from coupon tests, and a simulation method for tendon rupture were incorporated into the updated FE model. Nonlinear static simulation results obtained through the updated model were found to be consistent with both the observed crack pattern and the load–deflection curve. The reliability of the digital twinning FE model for assessing tendon damage and for predicting the residual load-bearing capacity was verified through comparison with measured data. Therefore, the digital twinning Bayesian model updating approach shows potential applications in continuous structural health monitoring (SHM) of PC bridges.

Robust elastic wave sensing system with disordered metasurface and deep learning

Elastic wave sensing is a crucial information acquisition technology with extensive applications in structural health monitoring, nondestructive testing, and other fields. However, traditional elastic wave sensing systems face challenges such as poor performance, high power consumption, and limited adaptability in complex environments. Here, a robust elastic wave sensing system integrating disordered metasurface and deep learning is demonstrated, enhancing the sensing performance in the environments with harsh noise or unknown signals. The scheme fully utilizes the complementary advantages of disordered metasurface and deep learning in physical encoding and intelligent decoding respectively. The meticulously designed disordered metasurface efficiently encodes elastic waves, and a single sensor acquires the encoding signals, enabling low-power information acquisition. The deep learning model performs adaptive and rapid intelligent decoding of the encoding signals, achieving efficient and robust information sensing while overcoming the sensing limitations of traditional compressed sensing in complex scenarios with low SNR and unknown signals. A series of experimental results demonstrate that, even under severe noise interference (known signal SNR≥−15dB, unknown signal SNR≥−7dB), the system can sense location information in elastic waves with a millisecond-level sensing speed and an accuracy above 90%. Furthermore, the successful application of the sensing system in vibration-tracking imaging and mechanical reading–writing further validates its practicability and robustness. This work may open up new avenues for the potential application of intelligent sensing in the fields of structural health monitoring, nondestructive testing, and human–machine interaction.

Combining transfer learning and numerical modelling to deal with the lack of training data in data-based SHM

Structural health monitoring (SHM) involves continuously surveilling the performance of structures to identify progressive damage or deterioration that might evolve over time. Recently, machine learning (ML) algorithms have been successfully employed in various SHM applications, including damage detection. However, supervised ML algorithms often require labelled data for multiple possible damage states of the structure for successful damage identification. Although it may be feasible to gather such data for low-value structures, obtaining damage data for expensive structures such as aircraft could be highly challenging. Herein, this data insufficiency is addressed by combining Finite Element (FE) models with domain adaptation, specifically transfer component analysis (TCA) and joint domain adaptation (JDA). The proposed methodology is showcased in two case studies, a Brake–Reuß beam, where damage scenarios correspond to different torque settings on a lap joint and a wingbox laboratory structure where damage is introduced as saw-cuts. Supervised learning algorithms in the form of Artificial Neural Networks (ANNs) and K-Nearest Neighbours (KNNs) are trained based on FE data after domain adaptation is applied and are then tested with the experimental data. It is shown that even though the performance of classifiers in distinct scenarios of dual, three, four and five-class cases is sensitive to choices in the training stage, the use of TCA or JDA allows for the use of FE data for training and significantly reduces the need for expensive experimental damage data to be used for training. These results can pave the way for a broader use of ML algorithms in SHM of critical and/or expensive structures.

Atypical second harmonic A0 mode Lamb waves in non-uniform plates for local incipient damage monitoring

Plates with non-uniform thickness, such as stiffened and notched plates, are commonly seen in engineering applications. Monitoring incipient damage in these structures is crucial to ensure their safety during service. Second harmonic Lamb waves hold great promise for structural health monitoring applications. However, the mechanisms underpinning the generation of the second harmonic Lamb waves in non-uniform plates are still not well understood due to the complex wave field. To tackle this issue, a theoretical analysis is first conducted to highlight the so-called atypical second harmonic A0 mode waves (2nd A0 waves) generated at the structural non-uniform section. Their existence, as well as their potential for local incipient damage monitoring applications, is then confirmed by finite element simulations. Experiments are carried out on a notched aluminum plate to monitor the incipient plastic damage induced by bending. Three mechanisms contributing to the generation of the atypical 2nd A0 waves in the non-uniform plate are identified: mode conversion from the second harmonic S0 mode waves, asymmetric nonlinear driving forces at the non-uniform section, and the mutual interaction between the mode-converted fundamental A0 and S0 waves. This study shows that the reported atypical 2nd A0 waves provide an effective means for monitoring local incipient damage in non-uniform structures.

Achieving precise multiparameter measurements with distributed optical fiber sensor using wavelength diversity and deep neural networks

The development of advanced distributed optical fiber sensing systems that are capable of performing accurate and spatially resolved multiparameter measurements is of great interest to a wide range of scientific and industrial applications. Here, we propose and experimentally demonstrate a wavelength diversity based advanced distributed optical fiber sensor system to accomplish multiparameter sensing while greatly enhancing measurement accuracy. A suite of deep neural network (DNN) algorithms are developed and verified for data denoising, rapid Brillouin frequency shift estimation, and vibration data event classification. As a proof-of-concept, we demonstrate the effectiveness of the proposed advanced wavelength diversity distributed fiber sensor system assisted by DNN for simultaneous, independent measurements of static strain, temperature, and acoustic vibrations over a 25 km long sensing fiber at 3 m spatial resolution. These results suggest the potential for an intelligent multiparameter monitoring system with enhanced performance in advanced structural health monitoring applications.