Structural Integrity Monitoring Research Articles

Vascular bundle-structured polymeric composites with fire-safe, self-detecting and heat warning capabilities for power batteries thermal management

The trend of miniaturization and integration poses challenges to the thermal management of electronic devices, requiring high thermal conductivity and potential fire safety, etc. In this study, inspired by plant vascular structure, we developed a polymer composite with a vertical vascular bundle structure via a sacrificial template method and subsequent assembly of transition metal carbides/nitrides (MXene) nanosheets and phytic acid (PA) coordinated cobalt ions (Co2+) complex. The embedded MXene and PA@Co exhibit multilayer multiscale structural features, forming heat transfer channels and protective cells within the composite. The resultant composites possess high out-of-plane thermal conductivity (∼1.54 W‧m−1‧k−1) and excellent flame retardancy, including self-extinguishing, and significantly reduced heat and smoke release. Interestingly, the MXene vascular bundle structure imparts heat early warning capabilities and intelligent damage self-detection, suggesting an effective means of preventing early-stage fires and real-time monitoring of composite structural and functional integrity. Such biomimetic strategies enable new insights into the designing of multifunctional, intelligent polymer composites.

An Efficient Computational System For Defect Prediction through Neural Network And Bio-inspired Algorithms

Detecting and locating damage is essential in maintaining structural integrity. While Artificial Neural Networks (ANNs) are effective for this purpose, their performance can be significantly improved through advanced optimization techniques. This study introduces a novel approach using the Grasshopper Optimization Algorithm (GOA) to enhance ANN capabilities for predicting defect aluminum plates. The methodology begins by deriving input parameters from natural frequencies, with defect locations as the output. A Finite Element Model (FEM) is used to simulate data by varying defect locations, creating a comprehensive dataset. To validate this approach, experimental data from vibration analyses of plates with different defect locations is collected. We then compare the performance of our GOA-optimized ANN against other metaheuristic algorithms, such as Cuckoo Search Algorithm (CSA), Bat Algorithm (BA), and Firefly Algorithm (FA). Notably, CSA's performance is slightly close to GOA. The results show that our GOA-based method outperforms these traditional algorithms, demonstrating superior accuracy in damage prediction. This advancement holds significant potential for applications in structural integrity monitoring and maintenance.

Damage identification in sandwich structures using Convolutional Neural Networks

The increasing complexity of structures and materials, coupled with ever more stringent demands for safety and cost reduction in maintenance operations, has driven the need to develop advanced techniques for structural integrity monitoring, known as Structural Health Monitoring (SHM). In this context, this study investigates the use of image processing techniques of mode shapes in a composite sandwich panel, employing various Convolutional Neural Network (CNN) models, with the purpose of identifying damage. The main objective is to classify the type of damage, whether it is in the core, at the interface, or in the laminate, followed by the precise location of the flaw and determination of damage dimensions in terms of length and width. To achieve these goals, the finite element method was used to create a representative database, and subsequently, an efficient data management system and model implementation were established to optimize computational costs. The results obtained show that, in the classification task, a high accuracy in damage type identification (99%) was achieved, even when images had high levels of noise. Regarding the regression task for damage localization, satisfactory results were obtained, while the dimensioning of damage length proved acceptable, albeit with some limitations, and inclination identification presented challenges. This study represents a significant advancement in the field of SHM and explores both the advantages and limitations of using Convolutional Neural Networks for this purpose. The results obtained provide valuable insights for the practical application of these techniques in the detection and assessment of damage in sandwich structures, thus contributing to enhancing the safety and efficiency of maintaining such structures.

A review on cementitious composite incorporating carbon nanomaterials: The self-sensing functionality as an implication for geological CO2 storage monitoring

Ensuring the long-term security and safety of geological CO2 storage relies on the durability of wellbores and cement. Addition of carbon-based nanomaterials (CNMs) have enhanced traditional cement, improving cement properties, and preventing gas migration. Despite these advancements, their potential for self-monitoring in GCS remains understudied. Self-sensing cementitious composites (SSC) are gaining attention for their ability to detect risks and damages, like cracks and corrosion, by monitoring environmental changes. This review first scrutinizes the self-sensing mechanism of SSC, including piezoresistivity, electrical resistivity and conductivity. The commonly used CNMs in SSC, such as carbon nanotubes (CNTs), carbon nanofibers (CNFs), carbon black (CB), and graphene nanoplatelets (GNPs), along with the hybrid nanocomposites, detailing recent advancements. Based on their effects, such as enabling the measurement of electrical response to strain, real-time monitoring of structural integrity, and improvement in cement performance, the implications are made for GCS cases, for instance, structural health monitoring and detecting leakage. Technical challenges, such as balancing electrical properties and mechanical strength, scalability, and environmental sensitivity, are addressed, along with insights on employing machine learning algorithms to optimize SSC usage. Finally, future directions for integrating SSC into GCS initiatives are proposed.

Visual Strain Sensors Based on Fabry-Perot Structures for Structural Integrity Monitoring.

Strain sensors that can rapidly and efficiently detect strain distribution and magnitude are crucial for structural health monitoring and human-computer interactions. However, traditional electrical and optical strain sensors make access to structural health information challenging because data conversion is required, and they have intricate, delicate designs. Drawing inspiration from the moisture-responsive coloration of beetle wing sheaths, we propose using Ecoflex as a flexible substrate. This substrate is coated with a Fabry-Perot (F-P) optical structure, comprising a "reflective layer/stretchable interference cavity/reflective layer", creating a dynamic color-changing visual strain sensor. Upon the application of external stress, the flexible interference chamber of the sensor stretches and contracts, prompting a blue-shift in the structural reflection curve and displaying varying colors that correlate with the applied strain. The innovative flexible sensor can be attached to complex-shaped components, enabling the visual detection of structural integrity. This biomimetic visual strain sensor holds significant promise for real-time structural health monitoring applications.

Structural Health Monitoring of Solid Rocket Motors: From Destructive Testing to Perspectives of Photonic-Based Sensing

In the realm of space exploration, solid rocket motors (SRMs) play a pivotal role due to their reliability and high thrust-to-weight ratio. Serving as boosters in space launch vehicles and employed in military systems, and other critical & emerging applications, SRMs’ structural integrity monitoring, is of paramount importance. Traditional maintenance approaches often prove inefficient, leading to either unnecessary interventions or unexpected failures. Condition-based maintenance (CBM) emerges as a transformative strategy, incorporating advanced sensing technologies and predictive analytics. By continuously monitoring crucial parameters such as temperature, pressure, and strain, CBM enables real-time analysis, ensuring timely intervention upon detecting anomalies, thereby optimizing SRM lifecycle management. This paper critically evaluates conventional SRM health diagnosis methods and explores emerging sensing technologies. Photonic sensors and fiber-optic sensors, in particular, demonstrate exceptional promise. Their enhanced sensitivity and broad measurement range allow precise monitoring of temperature, strain, pressure, and vibration, capturing subtle changes indicative of degradation or potential failures. These sensors enable comprehensive, non-intrusive monitoring of multiple SRM locations simultaneously. Integrated with data analytics, these sensors empower predictive analysis, facilitating SRM behavior prediction and optimal maintenance planning. Ultimately, CBM, bolstered by advanced photonic sensors, promises enhanced operational availability, reduced costs, improved safety, and efficient resource allocation in SRM applications.

Damage assessment on an asymmetric sandwich composite made of natural materials using strain gauges

Sandwich composites have been a growing subject of interest to various industries, thus it has become essential to monitor these structures. There is a need to implement damage identification and characterization strategies. This work aims to study the reliability of the implementation of strain gauge sensors, the mechanical properties of an asymmetric sandwich composite made by a core of cork agglomerate, two fibre-reinforced epoxy skins and a natural stone on top. Initially, destructive bending tests were conducted to establish the maximum load for the specimens. This served as a crucial reference point for subsequently employing strain gauges to investigate the mechanical properties of the composite. Finally, the destructive bending tests were performed using strain gauges in order to assess the performance of strain gauges to detect damage within the composite. The study provides comprehensive insights into various mechanical properties, including bending strength, tensile, compressive, and flexural moduli, as well as the determination of the neutral line location within the material. Notably, the results from the destructive bending tests employing strain gauges demonstrated their effectiveness in detecting catastrophic events within the composite. This research contributes to the advancement of reliable structural integrity monitoring and characterization techniques for sandwich composites.

Fiducial marker and blob detection-based motion compensation algorithm for Thermoelastic Stress Analysis measurements

Non-contact techniques for measuring stress and strain on structures offer advantages including preservation of structural integrity, remote and continuous monitoring, full-field measurement capability, non-destructive testing, and adaptability to various materials. Among them, thermoelastic stress analysis is one of the most established non-contact techniques to measures stress distributions in structures by analysing temperature-induced deformations. This research proposes a methodology for the compensation of the edge effect, a undesirable phenomenon resulting from rigid displacements in the analysed structure when using thermoelasticity approach. These displacements give rise to virtual stress distributions along the sample’s edges. For this purpose, a fiducial marker was used to track rigid displacements in thermal images, that were then processed using a blob detection algorithm, for compensating for edge effects under different loading conditions. The algorithm was capable of measuring the displacement of a sample at different frequencies (5-25Hz) and distances from the target (300-600 mm), with the lowest uncertainty of 0.2 mm. The impact of compensation was found The impact of the compensation was found to be significant, especially at frequencies lower than 10 Hz, where the displacement was the highest.

Deep learning enhanced metamodel design based on reduced mode shapes for delamination identification in composite structures

Structures requiring robust maintenance and accessibility pose challenges for structural integrity monitoring. This monitoring, crucial in various engineering applications, enables corrective actions to ensure machinery and equipment safety. Laminated composite materials, prevalent in aeronautical structures, often exhibit imperceptible delaminations or cracks, demanding the use of artificial intelligence for damage characterization. This study employs a numerical-experimental methodology, integrating artificial neural networks, to identify delaminations. Delamination, a structural damage form, was intentionally induced in epoxy-fiberglass composite structures. Modal tests captured characteristics in damaged and undamaged structures, revealing artificial intelligence effectiveness in pinpointing damage location and size. Damage significantly altered beam mode shapes, generating complex modes. Induced delaminations led to a measurable 15% reduction in natural frequencies, and damping ratios increased by approximately 10% in damaged structures. Artificial neural networks achieved an impressive 92% accuracy in predicting delamination location and size. Quantitative analysis of mode shape alterations validated the proposed methodology’s efficacy. This study provides valuable quantitative insights into damage detection in laminated composite structures, highlighting AI's potential for enhanced structural health monitoring. The numerical–experimental approach, supported by quantitative results, emphasizes the importance of advanced techniques for precise and efficient damage characterization in critical engineering applications.

Implementation of an enhanced fiber optic sensing network for structural integrity monitoring at the Brenner Base Tunnel

The reliable assessment of deformation characteristics along tunnel linings is essential to understand ongoing structural processes during construction and operation. Conventional monitoring techniques may involve limitations, either in the spatial or the temporal resolution and do not deliver the overall deformation behavior along the entire lining. Distributed fiber optic sensing (DFOS) has significantly evolved in recent years to monitor large-scale civil infrastructure, with scientific sensing designs being realized within various research projects. The technology can be advantageous for in-situ tunnel monitoring since the distributed strain and temperature sensing feature delivers a more complete picture of the linings’ structural deformation behavior.This paper introduces the design and realization of an enhanced distributed fiber optic sensing network inside concrete tunnel lining segments, currently being implemented at the Brenner Base Tunnel. The construction is one of the largest civil infrastructure projects world-wide and will be globally the longest underground railway connection with a total length of about 64 km once completed. The designed DFOS system, consisting of more than 35 km sensing cable overall, provides distributed strain and temperature information along numerous tunnel cross-sections, spread over more than 30 km tunnel drive and two different construction lots. After installation, measurements are being continuously performed, autonomously evaluated, and transferred to the ACI online visualization dashboard in real time. It can be shown that fiber optic sensors have considerably developed from research into innovative practice and are capable to extend or even to replace conventional, geotechnical sensors.

Scientometric Analysis of the Dynamics of Artificial Intelligence Applications in NonDestructive Testing and Structural Integrity Monitoring

This study aims to present a scientometric analysis of Artificial Intelligence (AI) applications in NonDestructive Testing and Evaluation (NDT&E) and Structural Integrity Monitoring. The data is collected from the research papers contained in the Web of Science Core Collection, covering studies on AI applications in various NDT methods employed across different industry sectors. The collected 654 research articles-dataset published between April 1989 and January 2023 was processed using CiteSpace software, which classified the dataset into twelve clusters. Moreover, a subset of the data pertaining to the relevant research in the nuclear industry was considered for analysis. Various metrics were used to analyse the consistency of the clusters and publications' influence and quality. The results, which quantify the dynamics and quality of research in AI for NDT&E, are given in visualised mappings, and tabulated data and the content of the major clusters was discussed to reveal the used AI and NDT&E methods and the sectors or industries where they have been used.

Non-Destructive Testing Methods for In Situ Crack Measurements and Morphology Analysis with a Focus on a Novel Approach to the Use of the Acoustic Emission Method

This article presents a concise review of modern non-destructive testing (NDT) methods that allow the detection, tracking, and measurement of cracks in reinforced concrete structures. Over the past decades, the range of solutions available on the market has increased. This provides excellent opportunities when choosing and designing systems for diagnosing and continuously monitoring structures. Cracking affects the mechanical properties, durability, and serviceability of a structure or its elements. Therefore, there is a need to develop methods that would allow the determination of the moment of a destructive process’s formation, i.e., a crack’s appearance. At the same time, it is crucial to be able to track the development of cracks for the entire structure, not just selected locations. This work also presents the concept of combining selected NDT methods and creating a system for the continuous monitoring of structural integrity and predicting changes in the durability of existing and future buildings.

A medium-high frequency FBG accelerometer based on a V-shaped flexible hinge

Acquiring medium- and high-frequency acceleration signals is vital for broad infrastructure health monitoring, such as bridges, railways, and tunnels. This article introduces an innovative fiber Bragg grating (FBG) accelerometer featuring a V-shaped flexure hinge structure with enhanced sensitivity. Theoretical calculation and simulation analysis software were employed to evaluate the proposed structure's parameters. Experimental results show a sensitivity of approximately 67.9 pm/G under a working bandwidth of 400–900 Hz and a resonant frequency of 1250 Hz. The influence of temperature at 8.4 pm/°C and transverse interference validate 3.5%. These findings align with simulations, affirming the sensor's performance. This innovation holds promise for enhancing structural integrity monitoring in large-scale structures by detecting medium- and high-frequency vibration measurements.

Structural Health Monitoring of Chemical Storage Tanks with Application of PZT Sensors.

Chemical pressure storage tanks are containers designed to store fluids at high pressures, i.e., their internal pressure is higher than the atmospheric pressure. They can come in various shapes and sizes, and may be fabricated from a variety of materials. As aggressive chemical agents stored under elevated pressures can cause significant damage to both people and the environment, it is essential to develop systems for the early damage detection and the monitoring of structural integrity of such vessels. The development of early damage detection and condition monitoring systems could also help to reduce the maintenance costs associated with periodic inspections of the structure and unforeseen operational breaks due to unmonitored damage development. It could also reduce the related environmental burden. In this paper, we consider a hybrid material composed of glass-fiber-reinforced polymers (GFRPs) and a polyethylene (PE) layer that is suitable for pressurized chemical storage tank manufacturing. GFRPs are used for the outer layer of the tank structure and provides the dominant part of the construction stiffness, while the PE layer is used for protection against the stored chemical medium. The considered damage scenarios include simulated cracks and an erosion of the inner PE layer, as these can be early signs of structural damage leading to the leakage of hazardous liquids, which could compromise safety and, possibly, harm the environment. For damage detection, PZT sensors were selected due to their widely recognized applicability for the purpose of structural health monitoring. For sensor installation, it was assumed that only the outer GFRP layer was available as otherwise sensors could be affected by the stored chemical agent. The main focus of this paper is to verify whether elastic waves excited by PZT sensors, which are installed on the outer GFRP layer, can penetrate the GFRP and PE interface and can be used to detect damage occurring in the inner PE layer. The efficiency of different signal characteristics used for structure evaluation is compared for various frequencies and durations of the excitation signal as well as feasibility of PZT sensor application for passive acquisition of acoustic emission signals is verified.

Multi-objective sensor placement optimization in SHM systems with Kriging-based mode shape interpolation

Sensor location optimization plays a key role in the application and development of structural integrity monitoring methodologies, especially in large mechanical structures. Given the existence of an effective damage detection and identification procedure, the question of how many and where to place the acquisition points (sensors) so that the monitoring system operates at peak efficiency arises. In this study, an innovative methodology is proposed in order to maximize the quality of modal information and minimize the number of sensors in the SHM system. To maximize the quality of modal information, it considered the reconstruction of mode shapes using Kriging interpolation. The study was carried out on plate-type composite material structures for initial validation and later applied and validated on a main rotor blade of the AS-350 helicopter. The initial modal information (modal deformation) was obtained through the finite element method, and the multi-objective Lichtenberg algorithm was used in the complex optimization process. The proposed method presented in this work allows for the best possible distribution of a minimum and sufficient number of acquisition points in a structure in order to obtain more modal information for a better modal reconstruction from kriging interpolation of these minimum points. Numerical examples and test results show that the proposed method is robust and effective for distributing a reduced number of sensors in a structure and at the same time guaranteeing the quality of the information obtained, even in noisy situations. Numerical results considering both the simple and complex geometric cases show that the proposed combined FS-kriging method is effective in distributing a finite number of sensors on the structures and at the same time guaranteeing the quality of the modal information obtained. The results also indicate that the layout of sensors obtained by multi-objective optimization does not become trivial and symmetrical when a set of modes is considered in the objective function formulation. The proposed strategy is an advantage in modal testing as it is only necessary to acquire signals at a limited number of points, saving time and operating costs in vibration-based processes.

Compression after multiple impact strength of composite laminates prediction method based on machine learning approach

The intelligent structural health monitoring system that can evaluate the structural safety online is the future development trend, in which the strength online prediction is the key step. This study developed a machine learning (ML) method to predict the compression-after-impact (CAI) strength of carbon/glass hybrid laminates subjected to multiple impacts at different impact positions online, which can help to find and replace damaged materials quickly to prevent irreversible disasters caused by accidental impact. Firstly, a finite element model verified by experiments was established to obtain the data of training ML model. Secondly, the eXtreme Gradient Boosting (XGBoost) model was utilized to predict the CAI strength of the composites subjected to multiple impacts at different distances between impact positions (DBIP). In addition, the feature importance of impact parameters based on the SHapley Additive exPlanations (SHAP) method was also studied. The results showed that the prediction accuracy and efficiency of ML-based method were better than that of FEM. Impact energy was the most significant factor affecting CAI strength, and DBIP cannot be ignored. The proposed method has great potential in online structural integrity monitoring systems of high-performance composite structures.

Temperature Patterns in TSA for Different Frequencies and Material Properties: A FEM Approach

Thermography techniques are gaining popularity in structural integrity monitoring and analysis of mechanical systems’ behavior because they are contactless, non-intrusive, rapidly deployable, applicable to structures under harsh environments, and can be performed on-site. More so, the use of image optical techniques has grown quickly over the past several decades due to the progress in the digital camera, infrared camera, and computational power. This work focuses on thermoelastic stress analysis (TSA), and its main goal was to create a computational model based on the finite element method that simulates this technique, to evaluate and quantify how the changes in material properties, including orthotropic, affect the results of the stresses obtained with TSA. The numeric simulations were performed for two samples, compact and single lap joints. when comparing the numeric model developed with previous laboratory tests, the results showed a good representation of the stress test for both samples. The created model is applicable to various materials, including fiber-reinforced composites. This work also highlights the need to perform laboratory tests using anisotropic materials to better understand the TSA potential and improve the developed models.

A novel physics-informed framework for reconstruction of structural defects

The ultrasonic guided wave technology plays a significant role in the field of non-destructive testing as it employs acoustic waves with the advantages of high propagation efficiency and low energy consumption during the inspect process. However, the theoretical solutions to guided wave scattering problems with assumptions such as the Born approximation have led to the poor quality of the reconstructed results. Besides, the scattering signals collected from industry sectors are often noised and nonstationary. To address these issues, a novel physics-informed framework (PIF) for the quantitative reconstruction of defects by means of the integration of the data-driven method with the guided wave scattering analysis is proposed in this paper. Based on the geometrical information of defects and initial results obtained by the PIF-based analysis of defect reconstructions, a deep-learning neural network model is built to reveal the physical relationship between the defects and the noisy detection signals. This learning model is then adopted to assess and characterize the defect profiles in structures, improve the accuracy of the analytical model, and eliminate the impact of the noise pollution in the process of inspection. To demonstrate the advantages of the developed PIF for the complex defect reconstructions with the capability of denoising, several numerical examples are carried out. The results show that the PIF has greater accuracy for the reconstruction of defects in the structures than the analytical method, and provides a valuable insight into the development of artificial intelligence (AI)-assisted inspection systems with high accuracy and efficiency in the fields of structural integrity and condition monitoring.

Influence of the geometric parameters on the effective properties of piezoelectric composite sensors using real measurements and a new RVE

Smart structures have attracted the attention of many researchers due to their applications and potential, such as structural integrity monitoring, vibration and noise suppression, shape control, and damage detection in composites. The development of these structures is closely linked to advances in piezoelectric sensors and actuators. One of the most used options in industries is the Macro Fibre Composites (MFC), which is manufactured by using different components, such as Kapton, Copper Electrode, Piezoceramic Fibre and Epoxy Composite, Copper Electrode, Kapton. Two main types of MFC available are d33 and d31, and they play a fundamental role in the advancement of smart structure technology. This work carried out numerical analyses to evaluate the influence of each component on the effective coefficients of the constitutive matrix for piezoelectric composite d33, based on experimentally measured geometric parameters. The investigated parameters were thickness of the protective layers, width, and thickness of the electrodes. Finite Element Method (FEM) combined with the concepts of a proposed Representative Volume Element (RVE) and homogenization of a periodic medium were used to determine the effective coefficients of the transducer. Through the results, it was possible to identify better geometric specifications for the transducer components in order to obtain higher mechanical and electrical properties.

Hydrodynamic Performances of Aeronautical Propeller for Drones

Currently, several countries already invest on the application of drones for different scopes. These purposes are mainly civilian and military and some of these include the presence of water as search and rescue, inspection of pipelines, getting a bird’s eyes view over oil spills or structural integrity monitoring of bridges. For this reason, an interesting feature for drones is their ability to maneuver in fluids with different density as air and water. This research topic, shows strong scientific potentiality but it also represents a great challenge from scientific and technical view point: i) aeronautical propeller for drones in water must generate reverse thrust or braking force through an off-design rotation in off-design condition (different fluid’s density); ii) the propeller rotational velocity in water is generally one order of magnitude lower than the one for air application requiring the motors to be properly desing to meet such a wide operational range. In this paper, an experimental campaign aimed at the performance quantification of a three-bladed propeller for drone propulsion in water has been carried out in a towing tank. In particular, the generated forces are acquired through a load cell for different advance ratios J provided by varying both rotational regime and free stream velocity. The results show expected losses in the performance for the off-design rotation in terms of both thrust and efficiency. At low rotational speeds, higher values of efficiency are presented for small advance ratio. The maximum efficiency increase for higher RPS and it’s slightly influenced by small variation of propeller disk angle.