Structural Integrity Monitoring Research Articles

Deep learning-based indirect bridge damage identification system

With the growing number of well-aged bridges and the urgency in developing reliable, (pseudo-) real-time monitoring of structural safety and integrity, there is a worldwide and widespread campaign toward transforming structural health monitoring practice. Among these attempts, the application of data-driven approaches in developing damage identification techniques has received particular attention in recent years. Given the growing volume of structural health monitoring data, the power of data-driven approaches has been further exploited. These efforts have been predominantly focused on building and training algorithms using direct measurements from bridges. Although recent years have seen transformative technologies in producing cheap and wireless sensors, network-wide bridge instrumentation is logistically difficult and expensive. This has led to a new group of structural health monitoring systems entitled indirect or drive-by approaches. In drive-by systems, measurements from an instrumented vehicle are used to extract structural damage signatures. In other words, in these systems, the instrumented vehicle acts as both actuator and receiver while passing over a bridge. The main challenge in deploying drive-by approaches for damage identification purposes is that the signals collected on drive-by vehicles also embody signatures from the vehicle, road/rail profile and are easily contaminated by environmental and operational conditions. Furthermore, the majority of current drive-by damage identification systems rely on prior knowledge of vehicle or bridge dynamic characteristics which has led to limited application of the concept in practice so far. To address these challenges, this study employs a powerful class of deep learning algorithm to develop a damage identification system using measurements on an instrumented travelling train. The proposed algorithm is capable of automatically extracting damage signatures from train-borne measurements only. To demonstrate the algorithm’s capability, the method is applied to measurements collected on a model instrumented train travelling on a simply supported model steel bridge. For this purpose, a deep convolutional neural network is built, optimised, trained and tested to detect damage using acceleration signals collected on the instrumented train only. The hyperparameters of the algorithm are optimised using the Bayesian optimisation technique. The accuracy of the algorithm is experimentally tested for four positive damage scenarios (combination of two different locations and intensity) and three different travelling speeds. This is the first demonstration of the data-driven drive-by damage identification system under scaled operational environment conditions. The performance of the proposed method is discussed under different travelling speeds and different damage states. The result shows that the proposed method can accurately and automatically detect and classify damage under varying speed, rail irregularities and operational noise using train-borne measurements only and offers a great promise in transforming the future of bridge damage identification system.

Detection, Location and Quantification of Structural Faults in a Two-Story Building Using the Artificial Immunological System

Large buildings and skyscrapers are vulnerable to environmental, temporal, and anthropological stresses, generating wear and tear that can lead to this social and economic impediment's collapse. The technological improvements of the fourth industrial revolution have resulted in changes in the connection between physical space and man, known as the cyber physic model, which necessitates monitoring systems to protect the structural branch and so correct this structural vulnerability. Thus, the structural health monitoring system is the exact measure of the evolution required by the cyber physic model in construction and the protection of the monumental buildings, ensuring not only their economic development but also the safety of society. Therefore, this research work presents the innovative proposal of the cyber-physical structural health monitoring system aimed at buildings and skyscrapers, based on and differentiated by intelligent computing techniques, using the negative selection algorithm to perform the analysis and monitoring of structural integrity, overcoming the existing traditional work. The cyber-physical structural health monitoring system will be applied to experimental data obtained from the shear building model that represents these imposing skyscrapers. An artificial immune system will be developed and used in the decision-making process based on the acquisition and processing of the obtained signals to perform the identification, localization, and quantification of possible structural damage. Observing the results, this work proved to be efficient, robust, and economically feasible, having high performance and overcoming the shortcomings of traditional techniques. It represents the perfect measure of cyber physics in the monitoring of large buildings and skyscrapers.

Multi-Variant Modal Analysis Approach for Large Industrial Machine

Power generation technologies are essential for modern economies. Modal Analysis (MA) is advanced but well-established method for monitoring of structural integrity of critical assets, including power ones. Apart from classical MA, the Operational Modal Analysis approach is widely used in the study of dynamic properties of technical objects. The principal reasons are its advantages over the classical approach, such as the lack of necessity to apply the excitation force to the object and isolate it from other excitation sources. However, for industrial facilities, the operational excitation rarely takes the form of white noise. Especially in the case of rotating machines, the presence of rotational speed harmonics in the response signals causes problems with the correct identification of the modal model. The article presents a hybrid approach where combination of results of two Operational Modal Analyses and Experimental Modal Analysis is performed to improve the models’ quality. The proposed approach was tested on data obtained from a 215 MW turbogenerator operating in one of Polish power plants. With the proposed approach it was possible to diagnose the machine’s excessive vibration level correctly.

Shape Sensing of Thin Shell Structure Based on Inverse Finite Element Method

Shape sensing as a crucial component of structural health monitoring plays a vital role in real-time actuation and control of smart structures, and monitoring of structural integrity. As a model-based method, the inverse finite element method (iFEM) has been proved to be a valuable shape sensing tool that is suitable for complex structures. In this paper, we propose a novel approach for the shape sensing of thin shell structures with iFEM. Considering the structural form and stress characteristics of thin-walled structure, the error function consists of membrane and bending section strains only which is consistent with the Kirchhoff–Love shell theory. For numerical implementation, a new four-node quadrilateral inverse-shell element, iDKQ4, is developed by utilizing the kinematics of the classical shell theory. This new element includes hierarchical drilling rotation degrees-of-freedom (DOF) which enhance applicability to complex structures. Firstly, the reconstruction performance is examined numerically using a cantilever plate model. Following the validation cases, the applicability of the iDKQ4 element to more complex structures is demonstrated by the analysis of a thin wallpanel. Finally, the deformation of a typical aerospace thin-wall structure (the composite tank) is reconstructed with sparse strain data with the help of iDKQ4 element.

Online structural integrity monitoring of high-performance composite rocket motor casing

Solid rocket motor (SRM) is one of the significant sub-systems for aerospace launch vehicles. The requirements of high range and high payload capabilities in aerospace systems demand the usage of high specific strength and high specific stiffness materials for rocket motor casings to minimize the inert weights. The present state-of-the-art technology is to realize stages of missile systems/launch vehicles with Composite Rocket Motor Casing (CRMC) to have a high-performance factor. CRMC is being designed with a marginal Factor of Safety (FOS) and mostly for a one-time use during its service life. CRMC is pressure tested to verify leak containment and its ability to sustain the Maximum Expected Operating Pressure (MEOP). Acoustic Emission Testing (AET), an online and global Structural Health Monitoring (SHM) technique along with strain and dilation measurement is used for observing the dynamic behavior of CRMC during the pressure test. Conventional Non-destructive Testing (NDT) such as Ultrasonic (UT) and Radiographic testing (RT) are employed to identify the type of defects. This unique capability of monitoring the dynamic behavior of active defects during real-time is of great significance for the acceptance of CRMC. The evolution of such an online system requires, first, identification of an appropriate process and next its adoption to meet the challenges posed by composite materials. There are various steps involved in the online SHM of CRMC. In this paper, various test practices, requirements, test parameters along with the details of strain and dilation measurements are given. This online inspection methodology and strain-dilation measurement during the Proof Pressure Test (PPT), along with a series of other inspection methods form the basis of CRMC acceptance for further use.

In situ cure monitoring and In-service impact localization of FRPs using Pre-implanted nanocomposite sensors

From manufacturing onset through service, cure progress and structural integrity of fibre-reinforced polymer (FRP) composites are monitored continuously using a single type of pre-implanted nanocomposite sensors. The sensors precisely respond to broadband dynamic strains up to half a megahertz, yet without imposing intrusion to host composites and downgrading the original structural integrity. In conjunction with differential scanning calorimetry and a Sesták–Berggren autocatalytic kinetic model, cure behaviors of FRPs during fabrication are evaluated accurately, in terms of the matrix polymerization degree that is correlated with subtle changes in propagation characteristics of guided ultrasonic waves captured by the pre-implanted sensors. Use of the sensors is subsequently extended to structural integrity monitoring of FRPs that are in service. Experimental validation demonstrates that a transient impact to composites can be localized and imaged with the pre-implanted sensors. This study illustrates an in situ life cycle monitoring approach for FRPs using a single type of permanently implanted sensors with neglectable intrusion to composites.

Understanding the sensitivity of thin-film graphene/polymer nanocomposite strain sensors to ultrasonic waves: Analytical and experimental analysis

Thin-film graphene/polymer nanocomposite sensors have been shown to be exceptionally sensitive to ultrasonic waves, making them promising next-generation candidates for structural integrity monitoring. However, the ultrasonic sensing mechanism of these sensors has never been scrutinized, restricting the deployment of these sensors to real-life applications. Herein, we carry out the first-ever study on the ultrasonic sensing mechanism of thin-film graphene/polymer nanocomposite sensors, through complementary physical experiments and analytical modelling. At first, sensors were precisely fabricated from nanofillers of different sizes and different matrix materials, and their electrical conductivities and ultrasonic sensitivities were measured. Analytical models that are based on the effective medium theory and the various contact modes between graphene nanofillers, entailing interphase regions and the quantum tunneling effect, were then established and fitted to the experimental results to reveal a series of microscopic characteristics of the sensors fabricated. Through a systematic analysis, it was found that the sizes of nanofillers and the properties of matrices significantly influence the microscopic morphologies and strain-induced dynamics of the sensors, in turn dictating their electrical conductivities and ultrasonic sensitivities. This insightful study will serve as the foundation for realizing applications of high-sensitivity thin-film graphene/polymer nanocomposite sensors in real-life ultrasound-based structural integrity monitoring scenarios.

A review of probabilistic structural integrity assessment in the nuclear sector and possible future directions

AbstractThere always exists uncertainty and variability in structural integrity assessments arising from lack of knowledge, modeling approximations, or differences between as‐manufactured components and as‐operated components. This may ultimately impact on reactor lives. Such uncertainties and variabilities can be understood and quantified using probabilistic techniques together with reliability‐based acceptance criteria, facilitating the quantification and management of risk. This review article provides a systematic appraisal of the latest worldwide literature and also unpublished reports from EDF UK nuclear plants, giving an overview of the existing knowledge of probabilistic/reliability structural integrity methodologies and tools from a multifaceted stance, including failure modes, problem types, material types, employed codes, correlations, and probability distributions. Structural reliability analysis at different levels is discussed, and pertinent issues on convergence testing, required number of trails, sensitivity analysis, verification of individual analyses, extremely small probabilities, reactor‐wide failure probabilities, and digital twin structural integrity monitoring are elaborated.

Novel Hybrid Polymer Composites with Graphene and MXene Nano-Reinforcements: Computational Analysis.

This paper presents a computational analysis on the mechanical and damage behavior of novel hybrid polymer composites with graphene and MXene nano-reinforcements targeted for flexible electronics and advanced high-strength structural applications with additional functions, such as real-time monitoring of structural integrity. Geometrical models of three-dimensional representative volume elements of various configurations were generated, and a computational model based on the micromechanical finite element method was developed and solved using an explicit dynamic solver. The influence of the geometrical orientation, aspect ratio, and volume fractions of the inclusions, as well as the interface properties between the nano-reinforcements and the matrix on the mechanical behavior, was determined. The results of the presented research give initial insights about the mechanical and damage behavior of the proposed composites and provide insight for future design iterations of similar multifunctional materials.

An implantable, compatible and networkable nanocomposite piezoresistive sensor for in situ acquisition of dynamic responses of CFRPs

When sensors are embedded in composites for structural integrity monitoring (SIM), the sensors per se, unfortunately, degrade the original integrity of host composites. Envisaging such deficiency and facilitated by advances in nanotechnology, we develop a new type of ultrathin, piezoresistive sensors using spraying coating and nanocomposites formulated with graphene nanoplatelets/polyvinylpyrrolidone. The sensors are deposited on dielectric membranes made of partially pre-cured B-stage epoxy films, electrified using carbon nanotube film (CNT-film)-made wires, and implanted in carbon fibre-reinforced polymer composites (CFRPs), to form a sensor network. Only ~45 μm thick (including wires), the implanted sensors exhibit high compatibility and nonintrusive attributes with CFRPs, enabling composites to perceive broadband signals in situ, ranging from static strain (with a high gauge factor of 34.5) to structure-guided ultrasonic waves up to 450 kHz – the first time that piezoresistive sensors implanted in CFRPs respond to dynamic strains in such a broad frequency band. CFRPs with implanted sensor networks are endowed with capacity of in situ SIM, yet not compromising their original integrity. With remarkably reduced intrusion to composites – as proven in tensile and bending tests, the developed sensors outperform prevailing sensors for SIM of composites such as lead zirconate titanate-based sensors.

Online monitoring of structural materials integrity in process industry for I4.0: A focus on material loss through erosion and corrosion sensing

This paper presents a critical review of the main sensing techniques considered for online monitoring of the integrity of alloys steel-made equipment used in process industry. This includes pipelines, tanks, electrical transformers and other metallic structures used in construction of buildings and bridges. Radio frequency (RF), Magnetic flux leak (MFL), ultrasonic-acoustic, fiber brag grating (FBG), thin layer strips, and photoacoustic sensors remain the main sensing techniques used or suggested for this purpose. However, despite lot of research works to improve their sensitivity and accuracy, further efforts are required to have them readily operational in the field especially for covering large volumes. This can be facilitated by the significant development carried out in various areas related to information technology (IT) and instrumentation, such as optical fiber sensors and self-powered wireless sensors under I4.0 paradigm. While optical fiber sensors, particularly FBG sensors, have the advantage to be scalable to cover wide areas with a few number of wires, they encounter the challenge of being not highly sensitive, especially for detecting degradations e.g. corrosion and erosion occurring at the opposite side of the steel structure. In addition, for such types of degradations, the fiber needs to be permanently and tightly connected to the structure, which is difficult to sustain. In addition, their proportional cost to the target sensitivity and resolution remains expensive. Wireless sensors equipped with ultrasonic sensors remain another tangible potential alternative for detecting internal corrosion and erosion of the structures if some pending issues relevant to power supply, using for instance energy harvesting, are sorted out. Overall, the existing technologies still suffer from a lack of sensitivity, accuracy, universality and/or cost effectiveness which justifies that more development efforts need to be done.

Investigation of low frequency fibre Bragg grating accelerometer based on thermoplastic cantilever beam

Vibration measurement technique is very important in structural integrity monitoring. Various fibre Bragg grating (FBG) based accelerometers have been developed for vibration measurement. However, most of the researchers focused on high frequency monitoring and only few reported works are based on low frequency measurement. Therefore, this paper presented a low frequency FBG accelerometer based on Polyphenylene Ether (PPE) thermoplastic cantilever beam. The proposed FBG accelerometer was attached to a shaker and vibration signals were given with variations in frequency and acceleration. As a result, the FBG accelerometer has a sensitivity of 110 pm/g and natural frequency of 9 Hz. The proposed accelerometer capable to detect low frequency of 2 Hz at 0.04 g which is suitable for utilisation in seismic monitoring of earthquake.

Output-only Based Identification of Modal Parameters of Linear and Nonlinear Structures by Wavelet Transform

In recent years, the methods of integrity monitoring of civil structures and mechanical ones, such as damage detection, fatigue analysis and risk-based management structures, have been receiving considerable interest from various fields of science and engineering. This work uses a mathematical tool for calculation and for a relatively new signal processing, in which uses a time-frequency analysis that provides more detailed information about non-stationary signals. We are talking about the Wavelet Transform. First, the Continuous Wavelet Transform is used to identify the modal parameters such as natural frequencies and damping rates, of a linear structure with four degrees of freedom. After that, this method is tested on a non-linear structure that represents a 20 floor building, proposed in the literature. A theoretical explanation of the methodology and an example of a simple application are also presented in order to understand the efficiency of the technique in the identification of modal parameters of structures.

Mapeamento sistêmico da aplicabilidade da análise de confiabilidade em sistemas de monitoramento estrutural

The structural integrity monitoring method (SHM - Structural Health Monitoring) was widely used in processes of detection, location and quantification of damage in mechanical systems. This study area aims, mainly, to analyze the structural integrity of mechanical systems, covering not only the damage identification process, but also correction and control actions. In parallel, a reliability analysis has seen an increase in research not only in structures, but also in composite materials to assist in the probabilities of structural failure according to their failure modes. This work presents a systematic review of the literature regarding the development and applicability of the reliability analysis in the monitoring of component failures corrects. To carry out the systematic mapping, databases of the Portal of Journals of the Coordination for the Improvement of Higher Education Personnel (CAPES), SCOPUS and Web of Science were used. It is noteworthy that a study of the execution of the mapping process enabled the optimization of the study carried out, as well as made it possible to identify the main GAP’s regarding the subject addressed. The results showed that there are studies on the practical application of this tool in everyday structures, but there is a great lack of studies focused on active monitoring systems.

Filament wound composite fatigue mechanisms investigated with full field DIC strain monitoring

Abstract Fatigue of filament wound materials was investigated using Digital Image Correlation DIC monitoring every 50th cycle of a high cycle fatigue test of a split disk ring sample. The ring was cut from a filament wound glass fiber reinforced polymer pressure vessel and had a hole. The strain field redistributed over time, lowering and moving strain concentrations. The redistributive behavior was most extensive in areas that later developed local fiber failure, which soon led to catastrophic failure. Microscopy was carried out on partially fatigued material. Damage evolved as matrix cracks and matrix splitting of groups of fibers and complete debonding of single fibers. This occurred at borders of voids and matrix cracks, easing progressive fiber failure. It was concluded that fatigue in filament wound composites has an extensive matrix damage phase before final failure. Fibers could locally withstand strains close to and above the static failure strain for considerable number of cycles if little local strain field redistribution was observed. The used method was able to detect changes in the strain fields that preceded catastrophic failure. It was concluded that DIC combined with the post processing methods presented may serve as a valuable tool for structural integrity monitoring of composite pressure vessels over time.

Comparing Direct Observation of Torsion with Array-Derived Rotation in Civil Engineering Structures.

In this article, we analyze the rotation rates in a building derived from a network of translation sensors and recorded by a rotation sensor. The building is Grenoble city hall, a reinforced concrete structure with permanent accelerometric translation sensors at the top and bottom of the building. A temporary experiment was conducted, consisting in installing a BlueSeis-3A rotation sensor for more than 24 h at the top of the structure. The ambient vibrations were analyzed. The amplitudes of translation accelerations and rotation rates at the top and bottom of the building, along with their variations over time, were analyzed. The acceleration/rotation ratios were then compared with the impulse wave velocities derived from seismic interferometry by deconvolution between the top and bottom. Perspectives with regard to building imaging, time monitoring of structural integrity and understanding the contribution of rotations to the structure’s response are discussed, offering new suggestions for research projects.

Intelligent Defect Identification Based on PECT Signals and an Optimized Two-Dimensional Deep Convolutional Network

Accurate and rapid defect identification based on pulsed eddy current testing (PECT) plays an important role in the structural integrity and health monitoring (SIHM) of in-service equipment in the renewable energy system. However, in conventional data-driven defect identification methods, the signal feature extraction is time consuming and requires expert experience. To avoid the difficulty of manual feature extraction and overcome the shortcomings of the classic deep convolutional network (DCNN), such as large memory and high computational cost, an intelligent defect recognition pipeline based on the general Warblet transform (GWT) method and optimized two-dimensional (2-D) DCNN is proposed. The GWT method is used to convert the one-dimensional (1-D) PECT signal to a 2D grayscale image used as the input of 2D DCNN. A compound method is proposed to optimize the baseline VGG16, a well-known DCNN, from four aspects including reducing the input size, adding batch normalization layer (BN) after every convolutional layer(Conv) and fully connection layer (FC), simplifying the FCs, and removing unimportant filters in Convs so as to reduce memory and computational costs while improving accuracy. Through a pulsed eddy current testing (PECT) experiment considering interference factors including liftoff and noise, the following conclusion can be obtained. The time-frequency representation (TFR) obtained by the GWT method not only has excellent ability in terms of the transient component analysis but also is less affected by the reduction of image size; the proposed optimized DCNN can accurately identify defect types without manual feature extraction. And compared to the baseline VGG16, the accuracy obtained by the optimized DCNN is improved by 7%, to about 99.58%, and the memory and computational cost are reduced by 98%. Moreover, compared with other well-known DCNNs, such as GoogLeNet, Inception V3, ResNet50, and AlexNet, the optimized network has significant advantages in terms of accuracy and computational cost, too.

A hybrid learning strategy for structural damage detection

Over the past decades, several methods for structural health monitoring have been developed and employed in various practical applications. Some of these techniques aimed to use raw dynamic measurements to detect damage or structural changes. Desirably, structural health monitoring systems should rely on computational tools capable of evaluating the information acquired from the structure continuously, in real time. However, most damage detection techniques fail to identify novelties automatically (e.g. damage, abnormal behaviors, and among others), rendering human decisions necessary. Recent studies have shown that the use of statistical parameters extracted directly from raw time domain data, such as acceleration measurements, could provide more sensitive responses to damage with less computational effort. In addition, machine learning techniques have never been more in trend than nowadays. In this context, this article proposes an original approach based on the combination of statistical indicators—to characterize acceleration measurements in the time domain—and computational intelligence techniques to detect damage. The methodology consists in the combined use of supervised (artificial neural networks) and unsupervised (k-means clustering) learning classification methods for the construction of a hybrid classifier. The objective is to detect not only structural states already known but also dynamic behaviors that have not been identified yet, that is, novelties. The main purpose is to allow a real-time structural integrity monitoring, providing responses in an automatic and continuous way while the structure is under operation. The robustness of the proposed approach is evaluated using data obtained from numerical simulations and experimental tests performed in laboratory and in situ. Results achieved so far attest a promising performance of the hybrid classifier.

Porosity Modulated High-Performance Piezoelectric Nanogenerator Based on Organic/Inorganic Nanomaterials for Self-Powered Structural Health Monitoring.

In the modern era, structural health monitoring (SHM) is critically important and indispensable in the aerospace industry as an effective measure to enhance the safety and consistency of aircraft structures by deploying a reliable sensor network. The deployment of built-in sensor networks enables uninterrupted structural integrity monitoring of an aircraft, providing crucial information on operation condition, deformation, and potential damage to the structure. Sustainable and durable piezoelectric nanogenerators (PENGs) with good flexibility, high performance, and superior reliability are promising candidates for powering wireless sensor networks, particularly for aerospace SHM applications. This research demonstrates a self-powered wireless sensing system based on a porous polyvinylidene fluoride (PVDF)-based PENG, which is prominently anticipated for developing auto-operated sensor networks. Our reported porous PVDF film is made from a flexible piezoelectric polymer (PVDF) and inorganic zinc oxide (ZnO) nanoparticles. The fabricated porous PVDF-based PENG demonstrates ∼11 times and ∼8 times enhancement of output current and voltage, respectively, compared to a pure PVDF-based PENG. The porous PVDF-based PENG can produce a peak-to-peak short-circuit current of 22 μA, a peak-to-peak open-circuit voltage of 84.5 V, a peak output power of 0.46 mW , and a peak output power density of 41.02 μW/cm2 (P/A). By harnessing energy from minute vibrations, the fabricated porous PVDF-based PENG device (area of A = 11.33 cm2) can generate sufficient electrical energy to power up a customized wireless sensing and communication unit and transfer sensor data every ∼4 min. The PENG can generate sufficient electrical energy from an automobile car vibration, which reflects the scenario of potential real-life SHM systems. We anticipate that this high-performance porous PVDF-based PENG can act as a reliable power source for the sensor networks in aircraft, which minimizes potential safety risks.

Damage detection in an offshore platform using incomplete noisy FRF data by a novel Bayesian model updating method

Structural integrity monitoring of jacket structures is an attractive challenge faced by researchers worldwide. Because of numerous uncertainties in marine environments, using statistical methods to reduce the detrimental impacts of uncertainties on model updating and damage detection results are unavoidable. In this study, a new Bayesian model updating framework is proposed using incomplete Frequency Response Function (FRF) data. In this methodology, the incomplete measurements issue is not dealt with the model reduction or data expansion method and the number of data in the objective function is increased using FRF at different excitation frequencies. The experimental verification of a scale 2D fixed platform is implemented to reveal the validity of the proposed methodology. Several numerical damage scenarios are simulated to investigate the effect of noisy data, FE model uncertainties, incomplete measurement, and added mass in the damage detection procedure. According to the results, the introduced method is entirely successful in the model updating and damage detection of the jacket platform. The results also indicate the lower effects of uncertainties and noise levels in damage detection outcomes.