Structural Health Monitoring Applications Research Articles

Temperature and Twist Sensor Based on the Sagnac Interferometer with Long-Period Grating in Polarization-Maintaining Fiber.

We utilized a CO2 laser to carve long-period fiber gratings (LPFGs) on polarization-maintaining fibers (PMFs) along the fast and slow axes. Based on the spectra of LPFGs written along two different directions, we found that when LPFG was written along the fast axis, the spectrum had lower insertion loss and fewer side lobes. We investigated the temperature and twist characteristics of the embedded structure of the LPFG and Sagnac loop and ultimately obtained a temperature sensitivity of -0.295 nm/°C and a twist sensitivity of 0.87 nm/(rad/m) for the LPFG. Compared to the single LPFG, the embedded structure of the LPFG and Sagnac loop demonstrates a significant improvement in temperature and twist sensitivities. Additionally, it also possesses the capability to discern the direction of the twist. The embedded structure displays numerous advantages, including easy fabrication, low cost, good robustness, a wide range, and high sensitivity. These features make it highly suitable for applications in structural health monitoring and other related fields.

A proof of concept study on reliability assessment of different metal foil length based piezoelectric sensor for electromechanical impedance techniques

Lead zirconate titanate (PZT) patches gained popularity in structural health monitoring (SHM) for its sensing and cost effective. However, a robust installation of PZT patches is challenging due to the often-complex geometry and non-accessibility of structural parts. For tubular structures, the curved surface can compromise the perfect bonding of PZT patches. To alleviate the above-mentioned challenges, the non-bonded and reusable configuration of sensor received considerable interest in the field of SHM. However, ensuring the repeatability and reproducibility of Electro-Mechanical Impedance (EMI) measurements is crucial to establish the reliability of these techniques. This work investigated the repeatability and reproducibility measures for one of non-bonded configuration of PZT patch i.e., Metal Foil Based Piezo Sensor (MFBPS). In addition, the concept, application, and suitability of MFBPS for impedance-based monitoring technique of Civil infrastructure are critically discussed. This study evaluates the effect of length of MFBPS on piezo coupled admittance signature. Also, this study evaluates repeatability and reproducibility of EMI measurements via statistical tools such as ANOVA and Gage R&R analysis. The statistical index CCDM was used to quantify the deviations of impedance signals. The overall result shows that the repeatability of the EMI measurements improves with a metal foil length of 500 mm. Overall, this investigation offers a useful point of reference for professionals and scholars to ensure the reliability of MFBPS for EMI techniques, a variant of piezoelectric sensor for SHM applications.

A whole service time SHM damage quantification model hierarchical evolution mechanism

Accurate damage quantification for an individual aircraft by using structural health monitoring (SHM) technology poses a persistent challenge in practical engineering. Existing damage quantification models typically rely on prior data acquired from ground design and tests. However, an individual aircraft is subject to diverse uncertainties throughout its whole service time, such as time-varying environmental and operational conditions, different flight missions, and different damage morphologies. Consequently, employing a prior trained model for damage quantification inevitably introduces errors, thereby limiting the engineering applicability of SHM. To improve the damage quantification accuracy for individual aircraft under the influence of uncertainty, a new whole lifetime data-based damage quantification model hierarchical evolution mechanism is proposed in this paper. Multi-source data from the design, service, and maintenance stages of individual aircraft are adopted to continuously evolve the probabilistic damage diagnosis model, enabling it to characterize the specific data distribution and track the damage propagation. The proposed method is validated through fatigue tests of a landing gear beam structure, which is an important aircraft load-carrying component, with guided wave monitoring based hidden Markov model quantifying its crack propagation. The validation result demonstrates the effectiveness of the proposed method in ensuring the long-term reliability of the model and achieving more accurate damage quantification for an individual aircraft.

Multiple local particle filter for high-dimensional system identification

Nonlinearity and high dimensionality emerge as two primary challenges in the realm of system identification within the context of structural health monitoring (SHM) applications. Particle filter (PF) has been demonstrated efficient for nonlinear identification, but it suffers from the curse of dimensionality and behaves poorly in high-dimensional problems. The idea of state and measurement partitioning has been used in many PF algorithms to simplify high-dimensional identification problems into the identification of several lower-dimensional subgroups, but with very few applications to SHM problems. In this context, by combining multiple particle filters (MPF) with the decay of correlations property, this paper develops a novel multiple local particle filter (MLPF) for high-dimensional identification problems. A whole state vector is partitioned into several state subgroups, each consisting of fewer state components and then estimated by one PF through a novel likelihood including the local state and measurement vectors. The feasibility and efficiency of the proposed method are tested through a benchmark toy example, a case study of a twenty-story Bouc-Wen frame structure under ground motion, and an experimental study of fatigue delamination growth in composites.

Tailoring electrochemically exfoliated graphene electroactive pathways in cementitious composites for structural health monitoring of constructions.

Manipulating and exerting a nanoscale control over the structure of multicomponent materials represents a powerful strategy for tailoring multifunctional composites for structural health monitoring applications. The use of self-sensing, electroactive cementitious composites in large-scale applications is severely hindered by the absence of clear directives and a thorough understanding of the electrical conduction mechanisms taking place within the cement matrix. Here we report on a nanoscale approach towards this goal which is accomplished via the development of a novel, multifunctional cementitious composite incorporating electrochemically exfoliated graphene (EEG). The use of commercially available poly(carboxylate ether)-based superplasticizer allowed us to embed in the cement mortar up to 0.8 wt% of EEG which is fully dispersed in the matrix. The multiscale investigation made it possible to assess the effect of such high dosages of EEG on the mechanical performance and hydration degree of cementitious composites. We used electrochemical impedance spectroscopy to monitor the formation of electroactive EEG-based percolation paths for charge transfer within cement mortar, the latter displaying resistivities of 2.67 kΩ cm as well as EEG-cement-EEG capacitive paths with capacitance of 2.20 × 10-10 F cm-1 for composites incorporating 0.6 wt% of EEG. Noteworthy, we have proposed here for the first time an electrical equivalent circuit for the impedance spectroscopy analysis of cementitious composites with high loadings of graphene, exceeding the percolation threshold. These findings underscore the potential of nanoscale structures for civil engineering applications and more specifically may open new avenues for the technological application of graphene-based cementitious composites in self-sensing structures.

Nanocarbon-based sensors for the structural health monitoring of smart biocomposites.

Structural health monitoring (SHM) is a critical aspect of ensuring the safety and durability of smart biocomposite materials used as multifunctional materials. Smart biocomposites are composed of renewable or biodegradable materials and have emerged as eco-friendly alternatives of traditional non-biodegradable glass fiber-based composite materials. Although biocomposites exhibit fascinating properties and many desirable traits, real-time and early stage SHM is the most challenging issue to enable their long-term use. Smart biocomposites are integrated with sensors for in situ identification of the progress of damage and composite failure. The sensitivity of such smart biocomposites is a key functionality, which can be tuned by the introduction of an appropriate filler. In particular, nanocarbons hold promising potential to be incorporated in SHM applications of biocomposites. This review focused on the potential applications of nanocarbons in SHM of biocomposites. The aspects related to fabrication techniques and working mechanism of sensors are comprehensively discussed. Furthermore, their unique mechanical and electrical properties and sustainable nature ensure seamless integration into biocomposites, allowing for real-time monitoring without compromising the material's properties. These sensors offer multi-parameter sensing capabilities, such as strain, pressure, humidity, temperature, and chemical exposure, allowing a comprehensive assessment of biocomposite health. Additionally, their durability and longevity in harsh conditions, along with wireless connectivity options, provide cost-effective and sustainable SHM solutions. As research in this field advances, ongoing efforts seek to enhance the sensitivity and selectivity of these sensors, optimizing their performance for real-world applications. This review highlights the significant advances, ongoing efforts to enhance the sensitivity and selectivity, and performance optimization of nanocarbon-based sensors along with their working mechanism in the field of SHM for smart biocomposites. The key challenges and future research perspectives facing the conversion of nanocarbons to smart biocomposites are also displayed.

GPS-Based Network Synchronization of Wireless Sensors for Extracting Propagation of Disturbance on Structural Systems.

Wireless sensor networks (WSNs) have gained a positive popularity for structural health monitoring (SHM) applications. The underlying reason for using WSNs is the vast number of devices supporting wireless networks available these days. However, some of these devices are expensive. The main objective of this paper is to develop a cost-effective WSN based on low power consumption and long-range radios, which can perform real-time, real-scale acceleration data analyses. Since a detection system for vibration propagation is proposed in this paper, the synchronized monitoring of acceleration data is necessary. To meet this need, a Pulse Per Second (PPS) synchronization method is proposed with the help of GPS (Global Positioning System) receivers, representing an addition to the synchronization method based on real-time clock (RTC). As a result, RTC+PPS is the term used when referring to this method in this paper. In summary, the experiments presented in this research consist in performing specific and synchronized measurements on a full-scale steel I-beam. Finally, it is possible to perform measurements with a synchronization success of 100% in a total of 30 samples, thereby obtaining the propagation of vibrations in the structure under consideration by implementing the RTS+PPS method.

Self-sensing concrete masonry structures with intrinsic abilities of strain monitoring and damage detection

Self-sensing cementitious materials are emerging technologies for Structural Health Monitoring (SHM) of civil structures. Their application for SHM of concrete masonry elements was not investigated in previous studies. The effects of triaxial strain/stress states of masonry joints and units on their self-sensing response are still unknown. Therefore, this work aimed to investigate mechanical and self-sensing properties of masonry elements produced with self-sensing mortar joints, solid concrete bricks and/or hollow concrete blocks fabricated with carbon black nanoparticles. The intrinsic abilities of strain monitoring and damage detection were evaluated in piezoresistive tests of masonry prisms, based on variations in the type of unit, mortar bedding approach, bonding arrangement, relative strength of mortar and units, joint thickness and location of self-sensing regions. These variations did not affect significantly the gauge factor of cementitious sensors embedded into the prism units. In contrast, the gauge factor of self-sensing horizontal or vertical joints was statistically affected by most of these variations. Stages of balance and abrupt increases in fractional changes in electrical resistivity (FCRs) during the plastic regime of the prisms provided an anticipated warming associated with the propagation of vertical and diagonal cracks associated with tensile splitting and crushing failure of units. The strong non-linearity on the electrical output of self-sensing joints provided an evidence of the mortar pore collapse. In conclusion, the self-sensing units and mortar joints were found to be promising alternatives for strain monitoring and damage detection in smart concrete masonry structures.

Havacılık Kompozit Malzemelerinde Mikroskobik Teknikler Kullanılarak Yapısal Sağlık İzleme Uygulamaları

Structural Health Monitoring (SHM) is a process that involves the observation and analysis of a system over time using periodically sampled response measurements to monitor changes to the material and geometric properties of engineering structures such as bridges, buildings, and aerospace composite structures. The goal of SHM is to detect changes in the structural behavior or condition that may indicate damage or degradation before a catastrophic failure occurs. SHM involves the implementation of damage detection strategies for structures of high importance. It is commonly used in civil engineering, aerospace engineering, and mechanical engineering applications to ensure the safety and reliability of structures. It improves the safety of aerospace composite structures by detecting damage at an early stage, preventing damage from occurring, improving reliability, and extending the life of the structure. SHM applications enable aircraft to spend less time on the ground and carry more passengers and cargo, thereby reducing operational costs. It can be utilized in various fields such as monitoring the health condition of aircraft tail and wing areas in the aviation industry, preventing damage and deterioration of car parts and components under operating conditions in the automotive sector, monitoring the health condition of bridges and tunnels in the transportation sector, and monitoring the health condition of wind turbines and other structures in the energy sector. Aerospace composite structures can suffer from several complex nonlinear damage modes, including impact damage, delamination, matrix cracking, fiber breakage, and voids. This study provides general and useful information on how structural health applications of aviation composites can be supported by microscopic techniques. In order to better understand the subject, an example aircraft composite structural component containing impact damage, which was mentioned above, was examined using microscopic techniques. In this investigation conducted using Stereo and Scanning Electron Microscopes (SEM), the identification of potential damage sources and the assessment of damage severity are explained in detail.

A novel approach model design for signal data using 1DCNN combing with LSTM and ResNet for damaged detection problem

In the current era, time-series data is widely used in different domains. In Structural Health Monitoring (SHM), the utilization of time-series data is also extensive. However, traditional Machine Learning (ML) methods, such as Artificial Neural Networks, applied in SHM, are no longer effective enough to process and accurately diagnose structural damages based on time-dependent data. To address this issue, this study proposes a novel deep learning approach that integrates 1D Convolutional Neural Networks (1DCNN), Long Short-Term Memory Networks (LSTM), and Residual Networks (ResNet) to enhance the efficiency of damage detection in constructions. The proposed method amalgamates 1DCNN for feature extraction, LSTM for recognizing long-term dependencies, and ResNet to counteract the vanishing gradient problem during deep network training. The effectiveness of the proposed method is assessed through the Z24 bridge dataset, one of the most comprehensive datasets in the field of SHM. The results indicate that the proposed approach achieves an accuracy of 81.5%, significantly outperforming traditional 1DCNN (78.7%), LSTM (79.3%) networks, and the combined 1DCNN-LSTM network (80.6%). This underscores the effectiveness of integrating deep learning techniques. The proposed 1DCNN-LSTM-ResNet method demonstrates considerable potential in practical applications for SHM, with superior accuracy and efficiency.

IDENTIFICATION OF DAMAGE IN STEEL BEAM BY NATURAL FREQUENCY USING XGB MODEL

Beams have played a significant role in engineering applications and they have been commonly used for modelling civil problems. Different models and methods have been developed to identify the damage to the beams. In this article, the extreme gradient boosting (XGB) model was developed to predict the location, width and depth of the saw-cut of steel beams by the change of natural frequencies. The natural frequencies of a steel beam in different scenarios were identified by the finite element method (FEM). The criterions to evaluate the accuracy of the models were the R squared (RSQ) and the mean square error (MSE). The result indicated that combining the FEM method with XGB would hold significant potential for applications in structural health monitoring.

Guided waves in long range nondestructive testing and structural health monitoring: Principles, history of applications and prospects

The early work on long range guided wave nondestructive testing (NDT) was largely done in industry, and multiple applications were developed before a surge in academic interest starting in the early 1990s. This paper reviews the history of the field, covering the topics that must be addressed in the design of a test procedure, as well as applications in both NDT and Structural Health Monitoring (SHM). The use of guided waves in long range NDT is relatively mature, and largely limited to simple structures in applications that are difficult or impossible to address by conventional point inspection methods. Point methods are impractical in most large area SHM applications where it is necessary to detect isolated defects since it is not possible to cover the whole area of interest with transducers. Guided waves therefore have great potential in SHM as they enable a large area of structure to be monitored from a limited number of transduction locations, so reducing the added weight, cost and complexity of the attachment to the structure. It is also possible to improve the sensitivity of the test relative to that obtained in one-off inspection by monitoring the change in signals with time using baseline subtraction. Current SHM deployments are largely in the same types of structure as those where guided wave NDT is used, but there is substantial potential for increasing the range of applications. The paper concludes with a review of the research and development needs for this potential to be realised.

An adaptive cepstrum feature representation method with variable frame length and variable filter banks for acoustic emission signals

Acoustic emission technology is widely employed in defect detection, structural health monitoring, fault diagnosis, and other applications due to its benefits of non-contact, real-time, sensitivity, and flexibility. To implement efficient and timely intelligent monitoring, it is crucial to promptly and precisely gather the time-varying properties in the non-stationary acoustic emission signals. The current work proposes an adaptive cepstrum feature representation method that combines variable frame length and variable filter bank. The variable frame length is implemented based on the spectral distribution correlation, and the optimal frame length and frame rate are selected by iterative operations. The variable filter bank, on the other hand, characterizes the local measure features of variable frame length signals by setting dynamic weight distributions. The proposed adaptive cepstrum algorithm can be used as an auxiliary tool to initially analyze complex non-stationary signals, and thus effectively identify the time-varying and frequency components in acoustic emission signals. The superiority of the proposed method has been confirmed on simulation data. Furthermore, the practical performance of the proposed method is evaluated and illustrated based on the burst signal from laser shock peening processing and the mixed signal from pipeline leakage. The proposed method can be extended to other broadband signals and can be used for feature pre-processing before deep learning network modeling to improve recognition accuracy due to its high time–frequency resolution.

Experimental and Numerical Study on Vibration-Based Damage Detection and Localization in Laminated Composite Plates

Damage detection in composite materials is crucial for ensuring the safety and reliability of engineering structures. Conventional methods often face challenges in accurately identifying damage in plate-like structures, particularly in scenarios involving multiple damages or small-scale delamination. This study focuses on investigating the detection and localization of delamination in composite plates by employing both experimental and numerical modal analysis. An eight-ply woven Glass-Epoxy composite laminate with and without damage was prepared with the aid of hand lamination technique. Laminate was fixed to a Clamped-Free-Free-Free (CFFF) boundary condition for experimental modal analysis by introducing controlled damage to examine its impact on modal properties. To validate the natural frequencies (NFs) of damaged and undamaged composite laminates, a numerical analysis was conducted using ANSYS Parametric Design Language (APDL). Further, to advance the understanding of using modal shapes and their spatial derivatives for damage localization in composite plates under various damage situations, post-processing of simulation results was conducted using MATLAB. Finite Difference Method has been employed to calculate the derivatives, and a novel damage index (DI) is proposed to enhance damage localization capabilities. The results affirm that the proposed DI is effective and precise in identifying damage in plate-like structures, both for individual and multiple damage scenarios. This research study presents a novel approach for identifying and pinpointing damage in composite plates, thereby making a valuable contribution to the field of structural health monitoring (SHM) applications

Additively Manufactured Wireless Sensors for Rugged IoT, Structural Health Monitoring, Smart Agriculture and Smart Cities Applications

In this talk, inkjet-/3D-printed antennas, interconnects, ‘smart’ encapsulation and packages, RF electronics, microfluidics and sensors fabricated on glass, PET, paper and other flexible substrates are introduced as a system-level solution for ultra-low-cost mass production of Millimeter-Wave Modules for Communication, Energy Harvesting and Sensing applications. Prof. Tentzeris will touch up the state-of-the-art area of fully-integrated printable broadband wireless modules covering characterization of 3D printed materials up to E-band, novel printable ‘ramp’ interconnects and cavities for IC embedding as well as printable structures for self-diagnostic and anti-counterfeiting packages. The presented approach could potentially set the foundation for the truly convergent wireless sensor ad-hoc networks of the future with enhanced cognitive intelligence and ‘rugged’ packaging. Prof. Tentzeris will discuss issues concerning the power sources of ‘near-perpetual’ RF modules, including flexible miniaturized batteries as well as power-scavenging approaches involving thermal, EM, vibration and solar energy forms. The final step of the presentation will involve examples from shape-changing 4D-printed (origami) packages, reflectarrays and mmW wearable (e.g. biomonitoring) antennas and RF modules. Special attention will be paid on the integration of ultrabroadband (Gb/sec) inkjet-printed nanotechnology-based backscattering communication modules as well as miniaturized printable wireless (e.g.CNT) sensors for Internet of Things (IoT), 5G and smart agriculture/biomonitoring applications. It has to be noted that the talk will review and present challenges for inkjet-printed organic active and nonlinear devices as well as future directions in the area of environmentally-friendly (‘green’) RF electronics and ‘smart-skin’ conformal sensors.

Strain Measurement with Optic Fibers for Structural Health Monitoring of Woven Composites: Comparison with Strain Gauges and Digital Image Correlation Measurements.

In this work, the strains measured with optic fibers and recorded during tensile tests performed on carbon/epoxy composite specimens were compared to those recorded by strain gauges and by Digital Image Correlation (DIC). The work aims at investigating the sensitivity of embedded and glued optic sensors for structural health monitoring applications in comparison with strain gauges and the full field strain map of the DIC. Acrylate, polyimide optic fibers, and three strain gauge sizes are considered to compare the three techniques. Results show hard polyimide-coated sensors are more sensitive to the material pattern than soft acrylate-coated fibers, which also require extensive adhesion length. The work shows a comparable size of strain gauges and material meso-structure is also critical for properly assessing material properties. The Young's modulus computed with the three different techniques is used to define a strategy that supports the selection and the proper size of the adopted strain measuring system for structural health monitoring of composite materials.

Real-time monitoring of self-sensing cementitious composite incorporating hybrid silicon carbide and graphite for enhanced structural health monitoring

This manuscript presents a comprehensive exploration of Silicon Carbide-Graphite Cement Composites (SCGCCs) by investigating their mechanical, electrical, and self-sensing properties. The study systematically varies SiC and graphite content, leading to remarkable insights. In terms of mechanical strength, specimens S50G1 and S50 stand out, exhibiting impressive enhancements of 15.6 % and 9.8 % in compressive strength, respectively, showcasing the pivotal role of particle content in bolstering structural integrity. Electrical resistivity analysis uncovers a percolation threshold at 25 % SiC and 2 % graphite, resulting in a substantial 97 % reduction in resistivity. This critical finding underscores the delicate balance required for optimal conductive networks within the composite material. The self-sensing capabilities of SCGCCs under flexural stress reveal a synergistic effect between SiC and graphite, with specimen S25G2 displaying the lowest voltage drop. The compressive stress analysis unveils a distinctive conductive path reconstruction behavior, offering valuable insights into the material's response to varying stress levels. Advanced CT scan porosity analysis further refines our understanding, indicating a significant reduction in porosity from 4.2 % to 3.1 % in specimen S25. Furthermore, specimens with lower graphite and SiC proportions exhibit efficient void compaction, resulting in a well-integrated microstructure with porosity as low as 2.5 %. These findings not only contribute to the fundamental understanding of SCGCCs but also offer practical applications in smart materials and structural health monitoring. Moreover, the inherent advantage of real-time monitoring within the context of smart mortar extends to the prognostication of an advanced warning signal for impeding the complete failure of the smart concrete matrix.

Ultra-wideband microwave leakage monitoring for stringer debonding detection in carbon composite fuselage structures

Ultra-wideband guided electromagnetic waves have been proposed recently for non-destructive testing and structural health monitoring applications. However, many possibilities are still unexplored given by the multitude of possible microwave waveguides. The present work introduces the concept of microwave leakage monitoring to detect stringer debondings in hollow carbon composite structures. The principle is to monitor the wave propagation characteristics within the cavity formed by the stringer and the airframe skin using transmission measurements. Changes in the transmission path, e.g. occurring from microwave leakage in the debonded region, can be assessed through a damage indicator approach.To proof this concept, an experimental campaign was carried out according to the building block approach typically used in aeronautics. First, a small-scale specimen with a debonded stringer was investigated in the laboratory. Then, a large-scale fuselage structure was used for technology demonstration enabling stringer debonding with increasing severity through static and fatigue loading. The paper shows that the microwave characteristics in the waveguide (stringer tunnel) is affected by the debonding conditions according to its severity and depth. This allows designing a structural health monitoring system based on guided electromagnetic waves trapped in the tunnel.

A convolutional neural network deep learning method for model class selection

AbstractThe response‐only model class selection capability of a novel deep convolutional neural network method is examined herein in a simple, yet effective, manner. Specifically, the responses from a unique degree of freedom along with their class information train and validate a one‐dimensional convolutional neural network. In doing so, the network selects the model class of new and unlabeled signals without the need of the system input information, or full system identification. An optional physics‐based algorithm enhancement is also examined using the Kalman filter to fuse the system response signals using the kinematics constraints of the acceleration and displacement data. Importantly, the method is shown to select the model class in slight signal variations attributed to the damping behavior or hysteresis behavior on both linear and nonlinear dynamic systems, as well as on a 3D building finite element model, providing a powerful tool for structural health monitoring applications.

Methodologies and Challenges for Optimal Sensor Placement in Historical Masonry Buildings

As ageing structures and infrastructures become a global concern, structural health monitoring (SHM) is seen as a crucial tool for their cost-effective maintenance. Promising results obtained for modern and conventional constructions suggested the application of SHM to historical masonry buildings as well. However, this presents peculiar shortcomings and open challenges. One of the most relevant aspects that deserve more research is the optimisation of the sensor placement to tackle well-known issues in ambient vibration testing for such buildings. The present paper focuses on the application of optimal sensor placement (OSP) strategies for dynamic identification in historical masonry buildings. While OSP techniques have been extensively studied in various structural contexts, their application in historical masonry buildings remains relatively limited. This paper discusses the challenges and opportunities of OSP in this specific context, analysing and discussing real-world examples, as well as a numerical benchmark application to illustrate its complexities. This article aims to shed light on the progress and issues associated with OSP in masonry historical buildings, providing a detailed problem formulation, identifying ongoing challenges and presenting promising solutions for future improvements.