Structural Health Monitoring Applications Research Articles

Acoustoelastic guided and surface waves in waveguides with focus on non-destructive testing and structural health monitoring applications —A review of recent studies

Acoustoelastic guided and surface waves in waveguides with focus on non-destructive testing and structural health monitoring applications —A review of recent studies

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-Reußbeam, 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.

Development of multifunctional cementitious composite using biochar

The global emphasis on sustainability has increased the demand for eco-friendly construction materials, with biochar emerging as a promising additive for concrete. It enhances mechanical properties, reduces greenhouse gas emissions, and adds advanced multifunctionalities. Traditionally, achieving multifunctionality in cementitious composites involves using costly conductive fillers like carbon fibers and carbon black, which pose dispersion and health challenges. In contrast, biochar offers a cost-effective and carbon-negative alternative. Its high carbon content enables multifunctionality and sensing capabilities when integrated into cement. This study explores the multifunctional properties of biochar-based cementitious composites, focusing on self-sensing abilities, carbon sequestration, and their chemical and mechanical properties. Results indicate that adding biochar at 5 %, 10 %, and 15 % levels enhances compressive strength by 16.62 %, 46.20 %, and 51.17 %, respectively, while reducing ductility due to its porous nature at higher concentrations. Moreover, increasing biochar content from 5 % to 10 % correlated strain with fractional changes in resistivity (R2 = 0.99), highlighting its potential in self-sensing and structural health monitoring applications. BC15 samples also exhibited a 70 % higher CO2 absorption rate compared to controls, showcasing biochar's ability to improve concrete's functionalities and environmental performance. This study supports the wider adoption of biochar in construction to advance sustainable construction practices and contribute to global carbon reduction goals.

Carbon fiber and PVA fiber reinforced concrete: Electrical resistivity and piezoresistive properties

This study examines the effects of carbon fiber (CF) and polyvinyl alcohol fiber (PVAF) on the mechanical and electrical properties of fiber-reinforced concrete by using four-electrode system and piezoresistive testing platforms. The optimal combination of 0.6 % CF and 0.6 % PVAF significantly improved piezoresistive properties, with a 45 % increase in relative resistivity. Additionally, 0.6 % CF with 0.9 % PVAF achieved a 13 % boost in compressive strength. SEM analysis revealed enhanced interfacial bonding between fibers, leading to improved mechanical and electrical performance. These findings highlight the composite’s potential for structural health monitoring, electromagnetic shielding, and winter snow removal applications

Edge Integration of Artificial Intelligence into Wireless Smart Sensor Platforms for Railroad Bridge Impact Detection.

Of the 100,000 railroad bridges in the United States, 50% are over 100 years old. Many of these bridges do not meet the minimum vertical clearance standards, making them susceptible to impact from over-height vehicles. The impact can cause structural damage and unwanted disruption to railroad bridge services; rapid notification of the railroad authorities is crucial to ensure that the bridges are safe for continued use and to affect timely repairs. Therefore, researchers have developed approaches to identify these impacts on railroad bridges. Some recent approaches use machine learning to more effectively identify impacts from the sensor data. Typically, the collected sensor data are transmitted to a central location for processing. However, the challenge with this centralized approach is that the transfer of data to a central location can take considerable time, which is undesirable for time-sensitive events, like impact detection, that require a rapid assessment and response to potential damage. To address the challenges posed by the centralized approach, this study develops a framework for edge implementation of machine-learning predictions on wireless smart sensors. Wireless sensors are used because of their ease of installation and lower costs compared to their wired counterparts. The framework is implemented on the Xnode wireless smart sensor platform, thus bringing artificial intelligence models directly to the sensor nodes and eliminating the need to transfer data to a central location for processing. This framework is demonstrated using data obtained from events on a railroad bridge near Chicago; results illustrate the efficacy of the proposed edge computing framework for such time-sensitive structural health monitoring applications.

A probabilistic machine learning framework for stiffness tensor estimation of carbon composite laminate

The potential uses of carbon composite material are vast, particularly in the civil, mechanical and aero-structures. Nonetheless, the practical utilization of carbon composite faces challenges due to complex experimental methods and imprecise algorithms for inverting material properties. Characterization of full set of stiffness tensor for an anisotropic material in particular orthotropic material can be considered as a complex non-linear inversion problem. The inversion process requires a robust optimization algorithm and forward models. The complexity of this inversion process impedes the practical utility in large-scale automation and in real-time structural health monitoring (SHM) application. To cater this, in this work an advanced probabilistic machine learning framework based on multi-output Gaussian process regression (moGPR) is proposed as an inversion algorithm. The inversion algorithm proposed is based on probabilistic framework that utilises the dispersion of Lamb waves as an input for optimal estimation of stiffness tensor. Experimental measurements of full wavefield data of propagating waves are conducted by scanning laser Doppler vibrometer. Further, an optimal training dataset is generated by employing a forward model based on stiffness matrix method (SMM). In the presence of measurement uncertainties, the effectiveness of the optimal prediction algorithm is showcased through a comparison between the estimated parameter and its ground truth. This comparison reveals that the proposed inversion algorithm is both efficient and robust, delivering satisfactory performance even in the presence of substantial measurement uncertainties.

Compressive stochastic subspace identification: A modal parameter identification method suitable for compressive sampling

Stochastic subspace identification (SSI) and its variants are widely used in conventional structural health monitoring (SHM) owing to their high computation robustness and estimation accuracy. Conventional SHM is based on a Nyquist (high-rate and uniform) sampling dictated by Shannon–Nyquist sampling theorem. As data quantity of SHM is increasing with duration and equipment at an ever-growing rate and new applications of SHM with strict constricts on sampling are emerging one by one, sub-Nyquist sampling, also known as compressive sampling has increasingly been used in SHM in recent years. Therefore, despite the success of SSI method and its algorithmic variants, a significant drawback lies in their handling of sub-Nyquist data. To overcome this drawback, we set out to extend the applicable scenarios of SSI from Nyquist (high-rate/uniform) sampling to sub-Nyquist (low-rate/nonuniform) sampling. In this paper, we propose a compressive stochastic subspace identification (CSSI) which can identify modal parameters from sub-Nyquist/compressed data. Specifically, CSSI involves two aspects: covariance matrix reconstruction and system parameter identification. We find the complete covariance matrix in classic SSI can also be estimated/reconstructed by sub-Nyquist samples. More importantly, this reconstruction can be physically guaranteed through special sampling patterns without sparsity prior. Based on the above findings, we develop a compressed covariance sensing-based covariance matrix reconstruction technique and derive the optimal sub-Nyquist sampling pattern such that the reconstruction is hassle-free and efficient. Then, we perform modal parameter identification on the basis of the reconstructed covariance matrix. Finally, the effectiveness of CSSI is validated by simulations and experiments. In short, CSSI, as a new extension of SSI, can extract modal parameters at lower sampling rates than what is prescribed by the classic SSI. The implication of this is the possibility that CSSI for operational modal analysis will require fewer measurements thus it would reduce the storage requirement and the bandwidth for transmitting data.

Smart structural health monitoring using computer vision and edge computing

Structural health monitoring (SHM) provides real-time data on the condition and performance of infrastructure, enabling timely and cost-effective maintenance interventions, and hence enhanced safety and extended service life. The computer vision-based non-contact sensor has emerged as a promising alternative to conventional contact-type sensors for structural displacement measurement and SHM. Many of the currently reported vision-based structural displacement measurement systems typically temporarily set up a video camera from a distance to the structure. The collected images or videos are usually stored locally and post-processed offline to obtain structural displacement responses, which is cumbersome and limited to short-term SHM applications. The recent development of technologies empowered by the Internet of Things (IoT) and edge computing has enabled real-time video processing and analysis at the source, minimizing latency, reducing bandwidth requirements, and enabling prompt decision-making, thereby enhancing efficiency and responsiveness compared to traditional offline video recording and processing systems. In this paper, an edge computing vision-based displacement measurement system (EdgeCVDMS) is developed. Video recording, processing, and displacement response identification are entirely performed on an edge device integrated with the vision-based displacement tracking algorithm, thereby greatly reducing the amount of data transmitted to the cloud server. The feasibility and applicability of the developed sensing system are experimentally validated on a laboratory-scaled transmission tower structure. The proposed EdgeCVDMS is cost-effective, easily deployable, and of great potential to be applied for the condition assessment of a larger population of aging civil infrastructure.

A novel single mode fiber optic temperature sensor combined with the FLRDS technique

A single mode optical fiber loop was employed as a temperature sensor to observe changes in optical loss regarding to ringdown time (RDT) by high sensitive the fiber loop ringdown spectroscopy (FLRDS) technique which has real-time and fast response measurement capability due to allowing trapped light pulse multiple interactions with the measurands. Two different fiber loops of 45 ± 5 m and 120 ± 5 m lengths were embedded one by one into a copper, circular and closed housing. Continuous monitoring of RDTs was carried out by changing the temperature in the range of 25–200 °C with the steps of 25 °C for the first time by using bare fiber without any modification as a temperature sensor. The FLRDS system for temperature sensing has simple design without extra components such as an optical time domain reflectometer (OTDR), long-period fiber grating (LPFG) or fiber Bragg grating (FBG) as sensorhead. The FLRDS system was diligently optimized to achieve the lowest baseline as %0.49. Since the RDT of the FLRDS system was changed due to the thermal expansion of the fiber, continuous monitoring of the temperature was the first time recorded by utilizing this kind of FLRDS temperature sensor. These kind of FLRDS temperature sensors have high potential to be employed in mining, nuclear facilities, railways, underwater structures, biomedical, medicine, structural health monitoring, transportation and communication applications with simple system setup, lower cost, higher sensitivity, portability, real-time and continuous monitoring for early detection.

Exploring the Relationship between Mechanical Properties and Electrical Impedance in Cement-Based Composites Incorporating Gold Nanoparticles.

Structural health monitoring applications have gained significant attention in recent research, particularly in the study of the mechanical-electrical properties of materials such as cement-based composites. While most researchers have focused on the piezoresistive properties of cement-based composites under compressive stress, exploring the electrical impedance of such materials can provide valuable insights into the relationship between their mechanical and electrical characteristics. In this study, we investigated the connection between the mechanical properties and electrical impedance of cement-based composites modified with Au nanoparticles. Cylindrical samples with dimensions of 3 cm in diameter and 6 cm in length were prepared with a ratio of w/c = 0.47. The Au nanoparticles (Au NPs) were synthesized using pulsed laser ablation in liquids, and their size distribution was analyzed through dynamical light scattering. Mechanical properties were evaluated by analyzing the Young modulus derived from strain-stress curves obtained at various force rates. Electrical properties were measured by means of electrical impedance spectroscopy. The experimental results revealed a notable reduction of 91% in the mechanical properties of Au NPs-cement compounds, while their electrical properties demonstrated a significant improvement of 65%. Interestingly, the decrease in mechanical properties resulting from the inclusion of gold nanoparticles in cementitious materials was found to be comparable to that resulting from variations in the water/cement ratios or the hydration reaction.

Anisotropic mechanical and sensing properties of carbon black-polylactic acid nanocomposites produced by fused filament fabrication

3D-printable conductive polymers are gaining remarkable attention for diverse applications, including wearables, pressure sensors, interference shielding, flexible electronics, and damage identification. However, the relationship between the anisotropy of their mechanical and electrical properties remains rather unexplored. This study focuses on characterizing Polylactic Acid/Carbon Black nanocomposites manufactured through fused filament fabrication. It aims to investigate the effect of the orientation of 3D printing layers on the mechanical properties, failure mechanisms, and self-sensing capabilities of the 3D printed material. To this end, we use a coupled health monitoring system in which electrical resistance measurements are applied to diagnose the damage state of 3D-printed samples during tensile testing. The results provide novel insights into the strong dependence of the material behavior on 3D printing pattern orientation, suggesting avenues for optimizing mechanical and electrical anisotropy through a multi-objective approach. Additionally, they offer guidelines for designing self-sensing components for structural health monitoring applications and strain gauge sensors with superior performance.

Energy efficient and low-latency spiking neural networks on embedded microcontrollers through spiking activity tuning

In this work, we target the efficient implementation of spiking neural networks (SNNs) for low-power and low-latency applications. In particular, we propose a methodology for tuning SNN spiking activity with the objective of reducing computation cycles and energy consumption. We performed an analysis to devise key hyper-parameters, and then we show the results of tuning such parameters to obtain a low-latency and low-energy embedded LSNN (eLSNN) implementation. We demonstrate that it is possible to adapt the firing rate so that the samples belonging to the most frequent class are processed with less spikes. We implemented the eLSNN on a microcontroller-based sensor node and we evaluated its performance and energy consumption using a structural health monitoring application processing a stream of vibrations for damage detection (i.e. binary classification). We obtained a cycle count reduction of 25% and an energy reduction of 22% with respect to a baseline implementation. We also demonstrate that our methodology is applicable to a multi-class scenario, showing that we can reduce spiking activity between 68 and 85% at iso-accuracy.

STRAIN TRANSFER ANALYSIS OF A FOUR-LAYER EMBEDDED FIBER OPTIC SENSING MODEL IN AN ELASTIC STATE

Optical fiber grating strain sensors are currently utilized in a variety of structural health monitoring applications. The encapsulated fiber optic sensor is unable to completely detect the strain of the structure, so the strain transfer theory should be established to maximize the strain sensing of fiber. It is required to explore the embedded four-layer fiber optic sensing model to create a more plausible strain transfer error hypothesis. Based on the three-layer fiber optic sensing model, the Goodman assumption and Fourier series approach were presented to study the strain transfer efficiency of the four-layer model in the elastic state. First, the physical quantity to be analyzed is determined, and finally the radius, interlayer bonding coefficient and elastic modulus are selected as the parameters affecting the strain transfer efficiency. The length range of efficiency evaluation is 0–2.5[Formula: see text]m, and the transfer efficiency under different radii is above 0.90 when the length [Formula: see text][Formula: see text]m. The interlayer bonding coefficient [Formula: see text] between [Formula: see text][Formula: see text]N/m3 and [Formula: see text][Formula: see text]N/m3 has little impact on the transfer efficiency, and the same is true for [Formula: see text], so it cannot be considered in practice. When [Formula: see text] is between [Formula: see text][Formula: see text]N/m3 and [Formula: see text][Formula: see text]N/m3 and the length [Formula: see text][Formula: see text]m, the strain transfer coefficient reaches 95%. The influence of elastic modulus on the transfer efficiency is very significant when [Formula: see text][Formula: see text]m. The four-layer model performs similarly to the three-layer model within the paste length range of 1.0[Formula: see text]m, but has a superior strain transfer effect when the pasted length exceeds 1.0[Formula: see text]m. As the radius of the protective layer rises, the effect of strain transfer deteriorates.

A0 mode Lamb wave propagation in a nonlinear medium and enhancement by topologically designed metasurfaces for material degradation monitoring

This paper intends to provide an application example of using metamaterials for elastic wave manipulation inside a nonlinear waveguide. The concept of phase-gradient metasurfaces, in the form of artificially architectured structures/materials, is adopted in nonlinear-guided-wave-based structural health monitoring (SHM) systems. Specifically, the second harmonic lowest-order antisymmetric Lamb waves (2nd A0 waves), generated by the mutual interaction between primary symmetric (S) mode and antisymmetric (A) mode waves, show great promise for local incipient damage monitoring. However, the mixing strength is adversely affected by the wave beam divergence, which compromises the 2nd A0 wave generation, especially in the far field. To tackle this problem, a metasurface is designed to tactically enhance the 2nd A0 waves through manipulating the phases and amplitudes of both primary waves simultaneously. After theoretically revealing the features of the 2nd A0 wave generation in a weakly nonlinear plate, an inverse-design strategy based on topology optimization is employed to tailor-make the phase gradient while ensuring the high transmission of the primary waves, thus converting the diverging cylindrical waves into quasi-plane waves. The efficacy of the design is tested in a 2nd-A0-wave-based SHM system for material degradation monitoring. Results confirm that the manipulated S and A mode waves can propagate in a quasi-planar waveform after passing the surface-mounted metasurface. Changes in material properties inside a local region of the host plate can be sensitively captured through examining the variation of the 2nd A0 wave amplitude. The concept presented here not only showcases the potential of metamaterial-enhanced 2nd A0 waves for material degradation monitoring, but also illuminates the promising direction of metamaterial-aided SHM applications in nonlinear waveguides.

Application of Long-Period Fiber Grating Sensors in Structural Health Monitoring: A Review

Structural health monitoring (SHM) is crucial for preventing and detecting corrosion, leaks, and other risks in reinforced concrete (RC) structures, ensuring environmental safety and structural integrity. Optical fiber sensors (OFS), particularly long-period fiber gratings (LPFG), have emerged as a promising method for SHM. Various LPFG sensors have been widely used in SHM due to their high sensitivity, durability, immunity to electromagnetic interference (EMI) and compact size. This review explores recent advancements in LPFG sensors and offers insights into their potential applications in SHM.

Flexible self-powered triboelectric nanogenerator sensor for wind speed measurement driven by moving trains

Flexible self-powered sensors have been extensively applied to the Internet of Things, structural health monitoring (SHM), and intelligent transportation. It would be more demanding for the power supply to these sensors during the long-term maintenance of the rail transit system. The wind pressure/velocity generated by high-speed trains poses a substantial threat to safety of human, and new sensors without an external power supply should be developed to monitor wind pressure/velocity in the trackside. Flexible self-powered wind triboelectric nanogenerator (W-TENG) sensor with a single-electrode mode based on conductive hydrogel is designed to wind pressure/velocity monitoring without power supply by harvesting wind energy. It is devoted the relationship between the output voltage of the sensors and the wind pressure/velocity driven by high-speed trains. Material selection and structural design methods are adopted to enhance the energy harvesting efficiency and sensing accuracy of self-powered W-TENG sensors. Open-circuit current of 2.8 μA and open-circuit voltage of 12 V are achieved, and the output voltage signal has the linear relationship with trackside wind pressure/velocity. Field tests are implemented to evaluate the performance of self-powered W-TENG sensors in wind pressure/velocity measurement caused by moving trains, providing an idea to SHM application in intelligent transmit systems.

Enhancing bridge damage assessment: Adaptive cell and deep learning approaches in time-series analysis

In recent years, the application of Deep Learning (DL) for damage detection in Structural Health Monitoring (SHM) using time-series data has garnered significant attention from the global scientific community. By incorporating numerous filters and units in their architecture, these models facilitate feature extraction making the processing of large time-series datasets in SHM more effective. However, optimizing of the number of filters and units is a challenge, often relying on scientists' experience or previous models without a systematic method for measurement or automatic selection. Therefore, this study proposes a novel framework to enhance the accuracy and efficiency of SHM for damage detection in bridge structures by using metaheuristic algorithms, Electric Eel Foraging Optimization (EEFO) algorithm, to optimize hyperparameters of DL model. This DL model combines the advantages of each traditional model. Specifically, 1D Convolutional Neural Network (1DCNN) is employed for features extraction, Gated Recurrent Units (GRU) for identifying long-term dependencies, and Residual Networks (ResNet) for avoiding vanishing gradient problem which often happens in DL model during training, referred to as 1DCNN-GRU-ResNet. The time-series dataset generated from Cua Rao bridge is used to demonstrate the effectiveness of the proposed method. 1DCNN-GRU-ResNet after optimization (1DCNN-GRU-ResNet-opt) achieves 91.6 %, significantly surpassing traditional 1DCNN (82.5 %), GRU (79.9 %), 1DCNN-GRU (85.7 %), and 1DCNN-GRU-ResNet (89.3 %) in the test set. The proposed 1DCNN-GRU-ResNet-opt approach demonstrates considerable potential in practical applications for SHM, offering high accuracy and efficiency.