bluuebunny/crossref_metadata_2025 huggingface dataset:
Printed PZT Transducers Network for the Structural Health Monitoring of Foreign Object Damage Composite Panel
Authors:   ParisTech, Paunikar Shweta ,   Delft University of Technology (TU Delft), Galanopoulos Georgios ,   ParisTech, Rébillat Marc ,   Fraunhofer Institute for Manufacturing Technology and Advanced Materials (IFAM), Wirth Ingo ,   ParisTech, Monteiro Eric ,   ParisTech, Margerit Pierre ,   ParisTech, Mechbal Nazih
Published: June 2024

The work presented here focuses on the structural health monitoring (SHM) of a foreign object damage (FOD) composite panel equipped with an innovative printed piezoelectric transducer network. The 3D woven composite FOD panel measures approximately 800 mm x 320 mm, is curved with a cross-sectional thickness varying from approximately 2 mm to 12 mm, and a stainless-steel leading edge is bonded at one of its sides. The core idea explored here is to rely on an innovative screen-printing technology to print a full piezoelectric transducer allowing to successfully achieve SHM on such a complex composite structure. This work is being carried out within the European project MORPHO – H2020. After printing a 25 elements PZT network, a four points bending fatigue experimental campaign using the PZT network along with other sensor technologies (embedded optical fibres with FBG sensors and acoustic emission sensors) is carried out. This unique experimental campaign allows to generate data and will help to develop diagnostic and prognostic methodologies for remaining life estimation and SHM of the FOD panel. It is demonstrated here through impedance measurements that the printing process associated with the printed PZT transducers is highly repeatable thus validating its use at a larger industrial scale. Furthermore, the printed piezoelectric transducers electromechanical behaviour is characterized and they are shown to be able to detect foreign object impact and to size and localize resulting damage using Lamb waves signals collected at different locations of the network. This innovative printing technology for PZT transducers network is thus extremely promising. It is furthermore highly advantageous to use the printed transducers for SHM instead of regular ceramic ones as this technology is non-intrusive, add negligible weight, can be printed during the manufacturing process, and arrays of transducers ensure easy availability of another transducer in case of failure of one.
Ultrasonic measurements with embedded Fiber Bragg Gratings in polyurethane resins
Authors:   Université Gustave Eiffel, Derrien Nicolas ,   Université Gustave Eiffel, Lehujeur Maximilien ,   Université Gustave Eiffel, Chapeleau Xavier ,   National Radioactive Waste Management Agency (ANDRA), Yven Béatrice ,   University Paris-Saclay, Laffont Guillaume ,   University Paris-Saclay, Roussel Nicolas ,   University Paris-Saclay, Blanchet Thomas ,   University Paris-Saclay, Recoquillay Arnaud ,   Université Gustave Eiffel, Durand Olivier ,   Université Gustave Eiffel, Gugole Gautier ,   AFC-STAB Composite, Prampart Sébastian ,   AFC-STAB Composite, Naylor Robin ,   Université Gustave Eiffel, Abraham Odile
Published: June 2024

In geophysics, Distributed Acoustic Sensing (DAS) optical fibers are increasingly being exploited to probe the subsurface using seismic waves. However, the integration of the deformation undergone by the fiber over a gauge length (typically a few meters) and a limited sampling frequency prohibit the use of this technology for ultrasonic applications. On the other hand, fiber Bragg gratings (FBGs) offer a sufficiently small sensitive element (500 μm to several cm) to measure the dynamic deformation field at ultrasonic frequencies. Recently, it has been shown that FBGs bonded to a surface or embedded in a structure can be used to measure the dynamic deformation field caused by the propagation of an ultrasonic wave. However, when inserting an optical fiber into a material, the effects of the structure on the fiber mechanical and optical properties need to be taken into account. For FBGs embedded in a reacting material, such as a polymer or concrete, the polymerization stage can affect the FBG spectrum leading to a variation of the Bragg wavelength, a decrease of the grating amplitude and, in the worst case, a modification of the FBG signature (lobe broadening, introduction of harmonics, etc.) due to the material’s shrinkage inducing strain on the fiber. In order to prepare for large-scale on-site measurements with embedded FBGs, it is necessary to carry out preliminary laboratory tests to address the instrumentation issues associated with inserting optical fibers into a curing material. For this purpose, several FBGs with different characteristics (insensitive to microbending, acrylate/polymide coating, multicore, etc.) have been integrated into different polyurethane resins to compare their response to insertion, heat transfer and the measurement of dynamic deformations at ultrasonic frequencies. In this contribution, the reduce scale models produced in polyurethane resins, and the results related to fiber insertion and ultrasound measurements, are presented in detail.
Determining structural properties of a prestressed concrete bridge through the combination of static and dynamic load testing
Authors:   University of Luxembourg, Bertola Numa ,   ETH Zürich, Reuland Yves ,   Swiss Federal Institute of Technology  (EPFL), Brühwiler Eugen
Published: June 2024

Examining structural safety requires hypotheses on several properties of the bridge structure, such as material properties, boundary conditions, and self-weight. The traditional approach relies on conservative assumptions for each structural property, following the conventional new-design approach. Nonetheless, this approach leads to conservative evaluations of the bridge capacity and may lead to the erroneous conclusion that the structure is deficient. Over-conservatism in structural safety assessments may have large environmental and economic impacts on global infrastructure management. A more advanced approach is to conduct multiple tests and monitoring activities on the structural system to provide more accurate values of these bridge properties. This paper presents a methodology to determine several parameters, including the structural stiffness, the boundary conditions, and the self-weight of concrete bridges based on static and dynamic load testing and robust data-interpretation techniques. The methodology is used on a prestressed concrete bridge in Switzerland. This bridge from 1958 has a single span of 35 meters. Prior to monitoring, conservative evaluations (using the conventional approach) led to the conclusion that the bridge has structural deficiencies. After monitoring, the bridge demonstrates significant reserve capacity, mostly due to the reduction of the self-weight safety factor. This study shows the potential of monitoring techniques for more sustainable and economic infrastructure management.
Development of a Bayesian Framework for Kinematic Data Fusion
Authors:   University of Trento (UNITN), Lotti Alessandro ,   University of Trento (UNITN), Zorzi Stefano ,   University of Trento (UNITN), Tonelli Daniel ,   University of Strathclyde, Tubaldi Enrico ,   University of Trento (UNITN), Zonta Daniele
Published: June 2024

Structural health monitoring (SHM) is widely used for assessing the condition of bridges at risk. Traditional SHM techniques rely on point-wise information provided by individual sensors placed at strategic locations. However, a more comprehensive assessment of the bridge state can be achieved through data fusion, integrating information from different sensors.

This article presents a Bayesian framework data fusion method that combines information from various measurements to improve the knowledge of the structural deformation state. The proposed framework identifies key deformation parameters by exploiting a simplified model that describes the system deformation state and uses an extensive set of data, including prisms, extensometers, tiltmeters, and beyond. Moreover, this approach provides a continuous knowledge of the deformation state, and reduces the uncertainties associated with individual sensor measurements. The framework developed is initially applied to a simulated case study of a simply supported beam, and then to the Colle Isarco viaduct, a highway bridge equipped with an extensive monitoring system.
Predictive Probability of Localization Curves (P-POL) for Structures with Changing Environmental Conditions
Authors:   Technical University of Munich (TUM), Mendler Alexander ,   Université Gustave Eiffel, Döhler Michael
Published: June 2024

One of the fundamental challenges in structural health monitoring (SHM) is the lack of data from the damaged state, which is required to verify the automated damage detection algorithms. In this paper, a recently developed approach is presented that allows one to assess the detectability of damages before they occur. The approach is based on so-called probability of detection curves that can be evaluated based on data and a model from the undamaged structure, in a “predictive” way. The method is based on four fundamental assumptions: the damage-sensitive features can be approximated through a normal distribution, the variance in the measurement remains constant for different damage scenarios, an analytical model of the examined structure is available for the sensitivity computation, and the relationship between measurements and structural changes can be linearized for small structural changes. In previous publications, this approach has already been applied to natural frequency and mode shape monitoring as well as ultrasonic testing. In this paper, it is applied to strain and inclination measurements from numerical case studies for the first time, demonstrating the universality of the method. Moreover, it is analyzed how changes in the environmental and operational variables (EOVs) affect the predicted POD. The results demonstrate that the predicted POD are valid even in the presence of environmental changes, but they depend on the user-defined hyperparameters of the algorithms that remove the environmental effects from the measurements. Therefore, the hyperparameter selection is critically discussed and an optimal monitoring strategy is outlined.
Simulation-based Optimal Sensor Placement in Tunnel Structures considering Uncertain in-situ Conditions and Multiple Loading Scenarios
Authors:   Ruhr University Bochum, Gottardi Nicola ,   Karlsruhe Institute of Technology (KIT), Freitag Steffen ,   Ruhr University Bochum, Meschke Günther
Published: June 2024

The expansion of the underground infrastructure and the necessity to maintain the full functionality of the existing tunnels highlights the role of structural health monitoring in keeping track of the behaviour of the structure over time.
In the study, the focus is on segmental tunnel lining for deep and long tunnels. The aim of this research is to investigate the best position for sensor placement based on the potential loading scenarios that might insist on the tunnel structure. Since for the evaluation of the lining safety we are interested in the maximum stresses reached in the structure, which might lead to durability critical cracking or crushing of concrete, a method is presented to suggest optimal positions for strain gauges. Due to uncertain geological conditions, multiple loading configurations acting on the tunnel structure are simulated using a finite element model. The results are employed to better identify the sensor locations, on the basis of where damage is prone to occur and where maximum information about the stress state in the structure can be inferred.
The results obtained by the framework are discussed in light of a practical engineering perspective and the advantages as well as the limitations of the proposed approach are expounded.
Shape sensing with iFEM on a type IV pressure vessel based on experimental measurements
Authors:   Politecnico di Milano, Bardiani Jacopo ,   Politecnico di Milano, Giglio Marco ,   Politecnico di Milano, Manes Andrea ,   Politecnico di Milano, Sbarufatti Claudio
Published: June 2024

The service life of a ship depends on its structural integrity, which can be undermined through time via different loading conditions. The incorporation of a Structural Health Monitoring (SHM) system could enhance the safety of the vessel because information about its health condition could potentially lead to the avoidance of catastrophic scenarios by the detection of structural damages and their progression, especially when they may be unobservable via physical inspection.
Within this context, a model-based SHM method called inverse Finite Element Method (iFEM) has been recently developed to assess the displacement and stress profile of vessels. iFEM offers several advantages, including the independence from material properties and loading conditions, the lack of training procedures, and the limited computational demand, which makes it suitable for real-time applications, enabling to reconstruct the complete displacement, and thus, the strain field starting from discrete strain measures.
In this work, we explore the application of the iFEM in challenging scenarios, regarding repeated stress fluctuations (e.g. fatigue) or other type of mechanisms like corrosion, buckling or even externally imposed damages from collision impacts or combat type damages (explosions, ballistic, etc.), depending on the specific case of naval vessels. An anomaly index is used to highlight the health state of the structure by comparing the strain predicted by the iFEM at some test locations with the strain measured by a test sensor in the same position. The two values will match only for the healthy structure, while their departure from each other will indicate anomalous conditions. Though the formulation of the diagnostic problem is general for an arbitrary component geometry and damage type, the proposed study is accompanied by some numerical examples applied to a realistic ship case.
A Hybrid Approach for Damage Detection and Localization on a Thin Metallic Plate
Authors:   Sapienza University of Rome, Venturi Andrea ,   National Research Council (CNR), Dessi Daniele
Published: June 2024

This paper illustrates a hybrid method for identifying structural damage that includes concepts from Physics-Based approaches within a Data-Driven framework. The lessons learned from the use of damage identification methods based on the analysis of modal curvature allows us to develop effective feature engineering in the development of Machine Learning (ML) algorithms. Moreover, the training of the algorithms exploits data provided from a population of damage cases generated by Finite Element (FE) analysis.

The above data-analysis pipeline is applied to the identification of damage in a rectangular metallic plate for which measured modal data are available from a dedicated experimental campaign in CNR-INM laboratory using accelerometers. The damage is modelled as a stiffness reduction over a small area being also representative of material thinning due to corrosion. To have meaningful samples of the training database, the modal convergence of the FE model to the real structure is guaranteed by a structural optimization process. Experimentally identified noise, representative of real-life applications, is then added to the FE results before algorithm training.

Damage existence and position are determined by a Novelty Detection approach and a Regression Neural Network, respectively. First, damage is identified on new test cases generated by the same FE procedure used for training. Secondly, the trained algorithms are applied to the experimental dataset. Sensitivity analysis on several parameters (number of samples for training, damage severity and noise levels) is numerically carried out to understand the applicability limits of the present methodology.
Structural Health Monitoring of Aerospace Structures using Guided Electromagnetic Waves in a Dielectric Waveguide at Ka-Band
Authors:   Goethe University of Frankfurt am Main, Rao Manuel E. ,   Goethe University of Frankfurt am Main, Memmolo Vittorio ,   Goethe University of Frankfurt am Main, Moll Jochen ,   Goethe University of Frankfurt am Main, Krozer Viktor
Published: June 2024

Dielectric waveguide (DW) antenna systems are widely used in millimeter-wave radar applications. A large and innovative field is structural health monitoring (SHM) of aerospace structures. The proposed research aims at detecting structural changes and potential defects at Ka-band on metallic structures that represents the device under test (DUT). To determine the severity of damage compared to the pristine structural state, dimensionless damage indicators (DI) are calculated after the root-mean-square deviation (RMSD) and root-mean-square correlation coefficient (RMSCC) methods.

The geometric design of the DW is crucial for a barrier-free propagation of guided electromagnetic waves (GEW) of a specific frequency bandwidth inside the dielectric material. In the presented approach, a rectangular DW is designed through numerical modelling which contains a polypropylene matrix, aluminum walls and two K-connectors, each with an inserted pin. The DW is attached to the surface of a nearly plane DUT connected externally to a vector network analyzer (VNA) that measures the elements of the scattering matrix (S-parameters). The reflection and transmission behavior, reflected in the S-parameters, provide information about the interaction of the GEW with a damage. It was found that damage sizes of 2mm can be detected reliably with the proposed methodology. The full paper will present and discuss numerical simulations with CST Microwave Studio with a comparison to experimental measurements.
Multi-Objective Model Updating of Tower Structures Featuring Closely Spaced Modes
Authors:   Leibniz University Hannover, Ragnitz Jasper ,   Leibniz University Hannover, Hofmeister Benedikt ,   Leibniz University Hannover, Jonscher Clemens ,   Technische Universität (TU) Darmstadt, Hübler Clemens ,   Leibniz University Hannover, Rolfes Raimund
Published: June 2024

In this contribution, we present a multi-objective model updating approach for the purpose of damage localisation and quantification on a girder mast structure based on closely spaced modes. In structural health monitoring (SHM), modal quantities, such as eigenfrequencies and mode shapes, serve as monitoring parameters. Specifically, model updating strategies adjust the local structural stiffness of a simulated structure to reduce the difference between the simulation results and the experimentally identified modal quantities. The resulting structural stiffness represents the damaged state of the structure, thus providing valuable information about the occurred damage.
Symmetrical tower structures are challenging for model updating approaches due to their closely spaced bending modes, which are prone to uncertain alignments between the identified mode shapes and the model mode shapes. As a result, the naive comparison between the mode shapes of the real structure and the model often yields poor results. In order to overcome this problem, we adapt the concept of the subspace of order 2 modal assurance criterion (S2MAC), i.e., finding the best fit to a mode shape vector in a subspace spanned by two bending modes, hence enabling a meaningful comparison of the changes in the dominant mode shapes. The proposed method is validated using an experimental 9 m tall girder mast structure featuring reversible damage mechanisms. Mode shapes and eigenfrequencies, identified using the Bayesian operational modal analysis (BayOMA) method, are employed for multi-objective model updating, allowing for the estimation of several Pareto-optimal solutions. The structure is parameterised with two damage parameters, accounting for the location and severity, which limits the optimisation to only one damaged beam at any given time. The damage localisation is performed for data sets recorded over a time span of several months, including three different damage positions, resulting in a successful validation of the proposed
Examining Quantiles in Structural Health Monitoring
Authors:   Helmut-Schmidt-University / University of the Federal Armed Forces Hamburg, Vogel Frederike
Published: June 2024

A central task within structural health monitoring is to determine the condition of a given structure, such as bridges, by gathering and assessing data from integrated sensory systems. This data can be viewed as functional and subjected to corresponding statistical methodologies. Prompt detection of potential structural damage is crucial due to its potential impact on a structure's service life.
One approach to address this challenge has been to use methods like (functional) principal component analysis ((F)PCA). FPCA identifies the primary directions of signal variability and monitors the evolution of corresponding scores over time. However, this conventional monitoring concept falls short in accounting for skewness or shifting effects, as it merely represents curves as deviations from the mean. A comprehensive analysis of the entire signals' distributions becomes imperative to tackle the multifaceted nature of structural damages and environmental influences adequately. In this innovative approach, we expand upon FPCA by incorporating a quantile perspective, where we model the significant variations in each sensor profile at various quantile levels. This empowers us to consider scores at different quantiles as vital monitoring metrics without imposing distributional assumptions on scores or error terms. Furthermore, our model takes into account confounding effects, particularly the temperature. We validate our method through simulation studies and real-data scenarios using strain and inclination data from long-term monitoring projects of German bridges. Our results demonstrate the effectiveness of our strategy as both a visual inspection tool and a reasonably accurate damage identification technique.
Development of a condition monitoring of heat exchangers for corrosion progress and coolant condition
Authors:   Fraunhofer Institute for Ceramic Technologies and Systems IKTS, Tschöke Kilian ,   Fraunhofer Institute for Ceramic Technologies and Systems IKTS, Kühmstedt Mario ,   Fraunhofer Institute for Ceramic Technologies and Systems IKTS, Schuster Christian ,   Fraunhofer Institute for Ceramic Technologies and Systems IKTS, Schröder Mathias ,   Fraunhofer Institute for Ceramic Technologies and Systems IKTS, Schubert Lars ,   MAHLE Industrial Thermal Systems GmbH & Co. KG, Kerler Boris
Published: June 2024

The transport sector, and thus also rail transport, needs to be completely decarbonised by 2050 at the latest. Considering the long service life of rail vehicles, this goal means enormous innovation and growth potential for the vehicle and supplier industry of railway technology in the coming years.

This contribution presents the first results of an R&D project to reduce the total cost of ownership of passenger and freight rail vehicles. The reduced life cycle costs can be achieved over the operating life, among other things, with condition monitoring approaches for critical components of cooling systems to enable diagnosis-based maintenance. Based on guided elastic waves, an integrated system is being developed to analyse the depth and position of corrosion damage to heat exchangers in rail vehicles. This method is to be supplemented by a refractive index sensor integrated in the coolant circuit which can be used to derive influences of the coolant condition on corrosion.

First, an acoustic measuring technique was put into operation. Suitable piezoelectric transducers were selected and test measurements, including the introduction of artificial damages, along different paths on a representative heat exchanger were carried out and evaluated. Measurement signals can be received over different distances, and a monitoring of larger areas of the heat exchanger seems possible. For the development of the monitoring of the coolant, plasmonically active sensor substrates with a gold nanostructure were produced and installed in a measuring flow cell. In laboratory tests, concentrations of water-coolant mixtures could be distinguished with the refractive index sensor. In the future, the refractive index sensing will be carried out by means of bypass flow measurement. Since the coolant is pumped in a circuit, the sensor system can be attached to one of the inlet/outlet lines.
The effect of low temperatures on monitoring structural health using acoustic-ultrasonic piezoelectric transducers on composite structures.
Authors:   Deutsches Zentrum für Luft- und Raumfahrt (DLR), German Aerospace Center, Galiana Shankar ,   Deutsches Zentrum für Luft- und Raumfahrt (DLR), German Aerospace Center, Moix-Bonet Maria ,   Deutsches Zentrum für Luft- und Raumfahrt (DLR), German Aerospace Center, Schmidt Daniel ,   Deutsches Zentrum für Luft- und Raumfahrt (DLR), German Aerospace Center, Wierach Peter
Published: June 2024

Composite materials have proven to be one of the ideal choices when it comes to structural applications requiring high performance working at extreme low temperatures, such as hydrogen tanks or space structures. One of the main challenges of using them is that they are susceptible to impact and fatigue, which may lead to different damages. Implementing structural health monitoring (SHM) systems is one strategy to enhancing their reliability, with guided waves (GW) being one of the most promising techniques. For the effective implementation of GW-based SHM in these applications, an in-depth comprehension of its behavior up to cryogenic temperatures is required for accurate monitoring. In this regard, the objective of the present work is to preliminary investigate the effect of low temperatures on wave propagation and transducer integration. By progressively removing a composite panel from a liquid nitrogen (LN2) bath with an integrated GW-SHM network based on co-bonded DuraActTM transducers, the GW and temperature are continuously monitored from LN2 CT to room temperature. The results show that the behavior of the GW by decreasing the temperature does not follow a monotonic pattern, having at least local maxima in terms of amplitude, and nonlinear behavior for the time-of-flight. Electromechanical impedance and C-Scans ensured the quality of the bonding of the co-bonded DuraActTM transducers.
Influence of Grease Contamination on Acoustic Emission Monitoring of Low-speed Roller Bearings
Authors:   Delft University of Technology (TU Delft), Scheeren Bart ,   Allseas Engineering, Thakoerdajal Nitish Perveshsingh ,   Delft University of Technology (TU Delft), Pahlavan Lotfollah
Published: June 2024

Large-scale low-speed rolling element bearings form crucial connections in offshore installations such as heavy-lifting cranes, single point mooring systems, and wind turbines. Due to the stochastic nature of wind and waves, the applied loads on these bearings are hardly predictable. Furthermore, the remote nature of the offshore environment requires a high standard of operational safety and reliability, reinforcing the need for advanced inspection and monitoring methods. Acoustic emission (AE) is a continuous monitoring technique for condition assessment of low-speed bearings, as it may detect the stress waves associated with developing degradation within the rolling elements. This paper presents an investigation on the influence of grease contamination on acoustic emission (AE) monitoring of low-speed bearings. It discusses several experiments that have been conducted in various regimes of particle contaminated lubrication, and proposes a strategy for condition monitoring. The presented experiments have been conducted with differing bearing geometries in multiple testing environments. The contamination particles have been both naturally developed and artificially introduced. The results total of 9 experimental cases are discussed and compared. All experiments follow the same data-acquisition principles, utilising arrays of three AE transducers sensitive in a range between 40-580 kHz. Data is recorded while operating the bearing at low speed under load. Abrasive wear of the rolling elements and crushing of the particles are concluded to be the main sources of AE due to particle contamination. Processing consists of waveform cross-correlation clustering and feature analysis. The results suggest that hard abrasive particles make significant contribution to the AE signals, which is associated with abrasive wear of the rollers and raceways. Comparisons with the contribution of softer and finer particles are presented as well.
Smartphone-Based Vibration Monitoring Studies of a Pedestrian Link Bridge
Authors:   University College Dublin (UCD), Ozer Ekin
Published: June 2024

Vibration-based structural health monitoring has gained a new pace with the rise of emerging consumer-grade sensor technologies. Accelerometers embedded in smartphones are among those which provide ubiquitous data potential with minimal burden to administrators. This research presents the preliminary findings of modal identification results obtained from smartphone sensors positioned on a single-span pedestrian link bridge. While a good number of modal identification studies are available in the literature, most of them correspond to short-term tests which are unlikely to capture seasonal features impacting identification results. In this study, the authors summarise a large vibration dataset from the testbed bridge with repetitive measurements taken over a month. The identification results presented herein are early representations of the daily and monthly temporal behaviour of modal frequencies observed throughout the tests.
Large scale monitoring of a highway bridge with remote sensing and distributed fiber optic techniques during load tests
Authors:   Graz University of Technology, Lienhart Werner ,   Graz University of Technology, Moser Thomas ,   Graz University of Technology, Strasser Lisa
Published: June 2024

Many bridges in western countries are at the end of their lifetime and hence Structural Health Monitoring is vital to ensure their safe operation. Load test are one way to assess the utilization grade and to decide if actions have to be taken. We report on a comprehensive monitoring campaign of load tests of a large highway bridge on the Brenner highway. The Brenner highway is the main alpine truck crossing passages from Germany to Italy. Several different remote sensing techniques like static and dynamic laser scanning, robotic total station measurements and interferometric radar were used to determine displacements of the bridge deck and girders. Strain changes and bending were observed with distributed fiber optic sensing. Furthermore, the movements of the bridge bearings were determined with IoT tilt sensors. Data was acquired during static and dynamic load tests. We demonstrate that the utilization grade of the bridge can be assessed by an integrated analysis of the different sensor data in combination with a numerical model of the structure. As a consequence of the investigations it was decided by the highway operator to close one of the three lanes of the bridge.
Implementation and validation of robot-enabled embedded sensors for structural health monitoring
Authors:   BAM Federal Institute for Materials Research and Testing, Johann Sergej ,   Technische Universität Hamburg-Harburg (TUHH), Stührenberg Jan ,   Technische Universität Hamburg-Harburg (TUHH), Tandon Aditya ,   Technische Universität Hamburg-Harburg (TUHH), Dragos Kosmas ,   BAM Federal Institute for Materials Research and Testing, Bartholmai Matthias ,   BAM Federal Institute for Materials Research and Testing, Strangfeld Christoph ,   Technische Universität Hamburg-Harburg (TUHH), Smarsly Kay
Published: June 2024

In the past decades, structural health monitoring (SHM) has matured into a viable supplement to regular inspections, allowing to execute repair and maintenance work at early stages of structural damage, thus resulting in relatively low rehabilitation costs. With the advent of wireless technologies and advancements in information and communication technologies, civil infrastructure has been increasingly instrumented with wireless sensor nodes to record, analyze, and communicate structural data relevant to SHM. However, in harsh environments, wireless sensor nodes may be damaged and fail to operate. A promising alternative to wireless sensor nodes is to embed sensors directly into concrete. In this paper, a sensor system for embedment into concrete is proposed, able to self-assess the structural data recorded from the concrete. Moreover, the data and information provided by the sensors are collected by robotic systems via RFID data transfer in an attempt to reduce the amount of human interaction and to overcome the limitations with respect to communication range and power supply. In laboratory experiments, the sensor systems are embedded into a concrete structural element, validating the capabilities of the sensors to record and to analyze the structural data. Furthermore, as will be shown in the paper, power will be supplied on-demand by legged robots that are deployed to collect the data and information, which is provided by the sensors for persistent storage. In summary, this study corroborates that the embedded sensors represent a crucial step towards intelligent, self-assessing concrete.
Non-Contact Laser Doppler Vibrometer for Characterization of Guided Waves in Timber Structures with Varying Moisture Content and Grain Orientations
Authors:   American University of Beirut, Yassine Reem ,   American University of Beirut, Mustapha Samir ,   University of Bristol, Ali Fakih Mohammad ,   Polish Academy of Sciences (PAS), Malinowski Pawel H.
Published: June 2024

Timber structures have been widely used in critical engineering applications throughout history. The need for Non-Destructive Testing of timbers has arisen, and ultrasonic-guided waves (GWs) have proved their efficacy in studying structural integrity. The effect of the grain direction in orthotropic timber structures on the propagation characteristics of GWs is not well understood, especially in the presence of moisture. This study aims to comprehend the effects of timber-grain orientations and the moisture content (MC) on dominant GW modes, specifically, on the anti-symmetric A0 mode.
The study is based on samples extracted from a Western White Pine utility pole, in three different orientations including longitudinal, tangential, and radial. The MC is introduced using a desiccator with a saturated salt solution. The studied MC conditions included dry, ambient MC, and high MCs varying from 10% to 27%. The GWs were excited using piezoelectric actuators and sensed with a Scanning Laser Doppler Vibrometer with Free-Free boundary condition. Phase velocities were obtained using the frequency-wavenumber plots of the measured line scans and were compared to the analytical curves for mode analysis.
The results showed that the experimental A0-mode phase velocities match well with those generated analytically. The difference was negligible for longitudinally oriented specimens with a percentage difference ranging from 0.28% to 5.34%, as the MC varied. The phase velocity decreased by about 7% as the MC increased from 0% to 27%. A noticeable difference in the phase velocity was measured with different orientations; at dry conditions, the A0 propagated at 958 m/s in the longitudinal specimens, while it decreased in the tangential specimens to about 760 m/s, and had the lowest phase velocity in the radial direction reaching 433 m/s.
The results show a high sensitivity of the propagating GWs to the direction of the fibers and the MC present in the timber.
Seven Years SHM of Offshore Wind Turbine Foundations: Review, Experiences and Outlook
Authors:   Wölfel Engineering GmbH + Co. KG, Tsiapoki Stavroula ,   Wölfel Engineering GmbH + Co. KG, Colomer segura Carles
Published: June 2024

Wölfel Engineering is a leading expert on the design and provision of SHM systems for offshore wind turbines, as well as the assessment of their structural integrity based on the implementation of sophisticated data evaluation algorithms. This contribution aims to provide an insight into the SHM results of offshore wind turbine foundations in numerous wind farms over the last seven years, focusing on presenting an overview of the evolution of SHM concepts, monitoring goals and features monitored for structural assessment – alternatively referred to as key performance indicators (KPIs).

Over the years, design approaches have become more accurate, alongside with the continuous increase of the size of the structures. Examples from offshore wind farms are used in order to show the effect of these evolutions to KPIs, such as the natural frequencies and the fatigue life consumption.

The challenges met in industrial SHM applications for offshore foundations are discussed. A novel approach to SHM, the agile SHM concept, is Wölfel’s answer to the future requirements on monitoring accuracy and reliability. The proposed concept enables the efficient exchange of information between the results of the SHM system and the design process, so that the design model is at all times an accurate representation of the real structures.
Convolutional autoencoder-based framework for damage localization under variable temperature
Authors:   Politecnico di Milano, Junges Rafael ,   Politecnico di Milano, Lomazzi Luca ,   Politecnico di Milano, Cadini Francesco ,   Politecnico di Milano, Giglio Marco
Published: June 2024

Confounding factors such as variable temperature have an impact on Lamb wave behaviour, affecting the accuracy of damage localization methods based on such waves. In this study, an innovative approach to Lamb wave prediction based on convolutional autoencoders (CAEs) is proposed and applied to an experimental dataset consisting of Lamb wave acquisitions on a Carbon Fiber Reinforced Polymer (CFRP) plate under varying temperatures.

Leveraging an experimental dataset of Lamb wave signals acquired from a CFRP plate at different temperatures, this research focuses on utilizing CAEs to enhance the accuracy and reliability of damage localization. This algorithm extracts critical features from Lamb wave data, effectively recognizing subtle wave properties variations, thus significantly improving the precision of damage localization. Two different architectures of CAEs were evaluated. One which uses the temperature value as a direct input into the latent space of the autoencoder, and another that does not process the temperature value. This analysis was performed to demonstrate the actual impact of the temperature information on prediction accuracy and, furthermore, the accuracy of the CAEs at predicting Lamb wave signals for temperatures outside of their training dataset.

The results obtained demonstrate that the inclusion of the temperature information into the autoencoder architecture not only increased its accuracy for temperatures within its training dataset but also increased its robustness with regards to temperature variations, displaying better performance at predicting Lamb wave signals for temperatures outside of its training dataset. The algorithm proposed here presents a way forward for increasing the robustness and reliability of damage localization methods based on Lamb waves.
Exploring transfer learning for improving ultrasonic guided wave-based damage localization
Authors:   Politecnico di Milano, Lomazzi Luca ,   Politecnico di Milano, Pinello Lucio ,   Leibniz University Hannover, Abbassi Abderrahim ,   Politecnico di Milano, Giglio Marco ,   Politecnico di Milano, Cadini Francesco
Published: June 2024

Designing maintenance strategies to reduce the failure risk of plated structures is paramount for increasing the safety level of aerospace, civil and mechanical systems. Although traditional time-scheduled maintenance policies are reliable, they come with costly operations and avoidable downtimes. Recently, more complex condition-based strategies have been studied in the literature. This class of maintenance actions rely on structural health monitoring (SHM) frameworks: a sensor network is installed on the structure diagnostic data are processed to monitor the health state of the structure.

The high dimensionality of data and the limitations of model-based SHM algorithms have led researchers to investigate data-driven solutions for improving the reliability of condition-based strategies. So far, supervised machine learning strategies have mainly been considered. However, since the cost of generating labeled datasets usually turns out to be prohibitive, two alternative solutions have gained attention: unsupervised methods and transfer learning (TL). While the former approach has been proved to provide satisfactory damage detection performance, it requires external knowledge sources to also localize and quantify damage. Instead, transfer learning could be used for performing all the damage diagnosis tasks, without the need for coupling the data-driven method with complex algorithms to restore the information lost by using smaller datasets for training. TL allows adapting pre-trained ML tools to new situations, new tasks and new environments. Moreover, TL can be leveraged when few labeled data are available, or to adapt efficient tools that have already been trained on a slightly different task.

In this work, TL and convolutional neural networks (CNNs) were leveraged for performing damage localization and quantification in metal and composite plated structures. That is, an in-house CNN-based framework for localizing and quantifying structural damage was considered, and the fine-tuning TL technique was employed to make the framework flexible enough to work in different domains.
Bayesian Data Fusion for Enhanced Monitoring of Bridge Displacements Using Satellite InSAR and Topographic Techniques
Authors:   University of Trento (UNITN), Tonelli Daniel ,   University of Trento (UNITN), Zini Mattia ,   University of Trento (UNITN), Simeoni Lucia ,   University of Trento (UNITN), Zorzi Stefano ,   EO59 LLC, Rocca Alfredo ,   EO59 LLC, Perissin Daniele ,   Autostrada del Brennero, Costa Carlo ,   Autostrada del Brennero, Quattrociocchi David ,   University of Trento (UNITN), Zonta Daniele
Published: June 2024

This paper investigates the effectiveness of satellite Interferometric Synthetic Aperture Radar (InSAR) technology to remotely monitor the structural health of road bridges and detect possible unexpected behaviours. The study is based on an Italian multi-span prestressed concrete bridge and a dataset of eight years of X-band SAR images collected by the COSMO-SkyMed satellite constellation. The method implemented for this study is as follows. First, SAR images are analysed using the Multi-Temporal InSAR technique to extract displacement time series of reflective targets on the bridge decks. Then, the extracted displacements are interpreted to validate their consistency with the bridge's expected responses to environmental loads, such as air temperature and river water flow variations, and detect possible unexpected behaviours. Eventually, the correlation with the air temperature is investigated to identify different bridge spans based only on monitoring data. The findings from this study advance the knowledge on using satellite InSAR-based monitoring for early detection of anomalous bridge behaviours on a large scale, providing valuable insights for the maintenance and safety of critical infrastructure.
Physics-informed surrogate modeling for a damaged rotating shaft
Authors:   Politecnico di Milano, Panagiotopoulou Vasiliki ,   ETH Zürich, Vlachas Konstantinos ,   ETH Zürich, Chatzi Eleni ,   Politecnico di Milano, Giglio Marco ,   Politecnico di Milano, Sbarufatti Claudio
Published: June 2024

In the online structural health monitoring framework, surrogate modeling aims to approximate the predictions of the underlying high-fidelity model to facilitate fast simulations. Generally, conventional machine learning techniques based on purely data-driven approaches often behave as black boxes, producing predictions that may lack physical consistency. In an effort to overcome these limitations, we proposed a neural network mapping constrained by the known physical relationships of an engineering model, which can be considered as a physics-informed inductive bias. This approach aims to enhance computational efficiency while reducing the required number of training examples. The physics-informed term guides the training process to yield predictions that exhibit greater physical consistency while maintaining high performance.
We here propose a surrogate modeling approach relying on a Physics-Informed Neural Network (PINN) scheme, which is designed to enable robust and real-time monitoring of ballistic impact damage on a rotor shaft. At first, a high fidelity finite element model is developed to simulate the response of the rotor shaft under various operational conditions. As a first reduction step, substructuring is used to decrease the dimensionality of the system's matrices, thereby reducing the computational cost. For comparison and validation, an alternative model based on the Timoshenko beam theory is utilized. Both models examine the effects of impact-induced vibration loads due to ballistic occurrences. The PINN model receives the external load as input and predicts the system's response. The system matrices extracted from both models constitute the physics-informed term. This term is incorporated into the PINN architecture in order to steer the learning process towards more physically consistent and reliable predictions regarding the state and the response of the structure. The algorithm's performance is evaluated using response data derived from both models.
Validation of an Abaqus impact wave propagation model
Authors:   Royal Netherlands National Aerospace Laboratory (NLR), Bezes Paul ,   Royal Netherlands National Aerospace Laboratory (NLR), Grooteman Frank ,   Royal Netherlands National Aerospace Laboratory (NLR), Wiegman Jan Willem ,   University of Twente, Ribeiro Marinho Natália ,   University of Twente, Loendersloot Richard
Published: June 2024

Structural Health Monitoring (SHM) is an important topic in aircraft industry to reduce the maintenance costs and increase the availability of an aircraft fleet through automating the determination of the health of the aircraft structure and systems using on-site sensors, like piezoelectric (PZT) and optic fibre Bragg grating (FBG) sensors that are often applied in SHM sensor networks.

A finite element model for wave propagation due to an impact is very desirable to enable the design of optimal sensor network configurations for SHM applications, to obtain a better understanding of wave propagation (especially in composite structures) and also to generate simulated (virtual) test data for SHM algorithm development. Therefore, an Abaqus explicit finite element (FE) model was developed for wave propagation due to impacts. To validate such a model, a series of impact tests were performed with a drop tower on an aluminium clamped square panel, at different impact energy levels and different impact locations. On the panel, eight FBG and six PZT sensors were installed. These experimental results were used to validate the FE model, in which the impacts were simulated and the computed PZT and FBG sensor responses were compared against the experimental results.

The paper will present the impact test setup, the finite element model and the validation results. The main focus of the paper will be the sensitivity analysis, identifying the model parameters that significantly affect the impact sensor responses and their optimal settings. A very good correlation between the numerical and experimental results was obtained.

In near future work, the model will be extended to a (thermoplastic) composite panel, for which experimental results on a flat composite panel already have been collected, and a stiffened composite panel representative of an actual horizontal tail plane are planned.