bluuebunny/crossref_metadata_2025 huggingface dataset:
Damage-aware structural control based on reinforcement learning
Authors:   Polish Academy of Sciences (PAS), Jedlińska Aleksandra ,   Polish Academy of Sciences (PAS), Pisarski Dominik ,   Polish Academy of Sciences (PAS), Mikułowski Grzegorz ,   Polish Academy of Sciences (PAS), Błachowski Bartłomiej ,   Polish Academy of Sciences (PAS), Jankowski Łukasz
Published: June 2024

This contribution presents a semi-active control technique intended for mitigation of structural vibrations. The control law is derived in a repeated trial-and-error interaction between the control agent and a simulated environment. The experience-based training approach is used which is the defining feature of the machine learning techniques of reinforcement learning (RL). In particular, a specific modification of the Deep Q Learning (DQN) approach is applied. The involved artificial neural network not only approximates the expected reward of the control (which defines the control action and quantifies its performance), but additionally keeps track of structural damages. This requires a specific architecture, which allows the network to be damage-aware, and a specific training procedure, where the memory pool preserved for the RL stage of experience replay is populated with not only the observations, control actions, and rewards, but also with the momentary status of structural damage. Such an approach explicitly promotes the damage-awareness of the control agent. The proposed technique is tested and verified in a numerical example of a shear-type building model subjected to a seismic excitation. A tuned mass damper (TMD) with a controllable level of viscous damping is used to implement the semi-active actuation, and the optimally tuned classical TMD provides the reference response.
SN-FSAT system for single sensor ultrasonic guided wave beam steering and damage detection
Authors:   Goethe University of Frankfurt am Main, Márquez Reyes Octavio A. ,   Goethe University of Frankfurt am Main, Moll Jochen ,   University of Bologna, Mohammadgholiha Masoud ,   University of Bologna, De Marchi Luca
Published: June 2024

In wireless communications as well as in structural health monitoring (SHM) beam steering presents interesting advantages. In the former it plays an important role for data directivity, which is also a perk for energy efficiency and data security; for the latter, directional inspection through guided waves leads also to energy efficiency, arbitrary dedicated space scanning, multi-function operation through multiple beams and even to application of a base-line free inspection concept. Normally, to enable this simultaneous beam control and data communication in arbitrary spatial directions (2D) over a structure, a large array of transducers is required, arranged to a similarly complex hardware and software support system. In this work, we present a system based on a dedicated sensor node (SN) and frequency steerable acoustic transducer (FSAT) combination. The SN-FSAT system is charged of the adequate power and signal management for acquisition, transmission, processing and data evaluation, required to perform spatial scanning and damage detection, as well as direction focused wireless communication tasks. These were tested over an isotropic aluminum plate-like structure by means of a double symmetric beam, via differential actuation signals, or in a high directive beam in a single direction by means of a quadrature driving signals. The complete guided wave-based setup consists of three SN-FSAT pairs. As damage a pseudo fault of a small added mass with damping tacky tape was used. The results show that damage located in different positions around the FSATs can be detected without a baseline reference and multiple directional communication paths between the SN-FSAT network can be established.
Estimation of the effective range of PZT sensors for damage detection based on full wavefield measurements
Authors:   Air Force Institute of Technology, Dziendzikowski Michal ,   Air Force Institute of Technology, Kowalczyk Kamil ,   Air Force Institute of Technology, Niedbala Patryk ,   Air Force Institute of Technology, Dragan Krzysztof ,   AGH University of Science and Technology, Dworakowski Ziemowit
Published: June 2024

Optimal location of sensors for enhanced damage detection capability is one of the key factors which needs to be considered when designing SHM system based on PZT sensors. However the sensitivity of a sensor to damage is not only dependent on its relative location with respect to elastic waves source and damage, but can also depend on structure geometry as well as particular signal features which are used for structure assessment.

In the presentation, results of structure evaluation based on guided waves excited by PZT transducers and full wavefield measurements obtained by laser scanning vibrometer will be presented. The measurements were acquired for pristine state of test structure and with introduced damage, therefore it is possible to calculate spatial distribution of various signal characteristics and estimate effective range of PZT sensors for damage detection based on a given damage index. Various parameters affecting the damage detection range will be compared, in particular: type and the extent of damage, type of signal characteristic used, parameters of the excitation signal (e.g. frequency, duration), as well time interval used for damage index calculation. The obtained results may be useful for refining algorithms for sensor placement optimization as well as damage localization techniques, e.g. based on RAPID imaging algorithm.
Optimal Sensor Placement with Minimal Predicted Posterior Variance
Authors:   Technical University of Munich (TUM), Mendler Alexander ,   Helmut-Schmidt-University / University of the Federal Armed Forces Hamburg, Marsili Francesca ,   Helmut-Schmidt-University / University of the Federal Armed Forces Hamburg, Kessler Sylvia ,   Technical University of Munich (TUM), Grosse Christian U.
Published: June 2024

The performance of each monitoring system critically depends on the sensor layout and typical optimization criteria are based on the accessibility to the structure on site, experience with similar structures, and an optimal signal-to-noise ratio. More advanced criteria aim to optimize the feature extraction process, and this paper aims to develop a sensor placement strategy for optimal damage diagnosis. The approach is based on Bayesian updating and the optimization goal is to find a sensor layout that is the most sensitive to small changes in the examined system parameters. It is shown that such layouts manifest themselves through a minimum variance in the posterior distribution. Using Kalman filters, the posterior variance can be “predicted” based on the measurement error and numerical models, so no data from and no simulations in the damaged state are required. By employing a polynomial chaos expansion (PCE)-based Kalman filter, the method becomes applicable for structures with many measurement channels, multiple examined system parameters, and for a large number of sensor combinations. Gaussian distributions are assumed for both system inputs and outputs. For proof of concept, the sensor placement is optimized on a numerical cantilever beam, demonstrating that the method can efficiently find the optimal sensor layout. Moreover, arbitrary measurement quantities (inclinations, vibrations, etc.) or damage-sensitive features (statistical values, modal parameters, etc.) can be used as observations, and multiple system parameters can be considered at the same time. Among other factors, an optimal sensor layout leads to large measurement responses, a small measurement error, and more advanced statistical quantities that will be elaborated on in detail.
System identification based on the cross Gramian
Authors:   Hochschule für Technik, Wirtschaft und Kultur Leipzig (HTWK), Moeller Max ,   Hochschule für Technik, Wirtschaft und Kultur Leipzig (HTWK), Rohrer Maximilian ,   Hochschule für Technik, Wirtschaft und Kultur Leipzig (HTWK), Lenzen Armin
Published: June 2024

In order to make mechanical structures more reliable, research is being carried out into methods of detecting and localizing damage to structures, known as Structural Health Monitoring (SHM). One method for continuously monitoring a structure is called State Projection Estimation Error (SP2E) and was developed at the University of Applied Sciences Leipzig. This method is based on state space systems, which are generated by output-only system identification. It is state-of-the-art to create a stabilization diagram to choose the best model order. Since the monitoring process is continuous, a state space model is created every 10 minutes, and therefore it is not possible to look at a stabilization diagram. Thus, the system identification process has to be automated. A novel approach developed in the SPP 100+ research program takes place via the theory of the cross Gramian. The cross Gramian is defined by the Hankel operator, which maps the past input to the future output. This Gramian indicates the interaction between the states and the input/output of a system and can be seen as the energy capacity of a system. By comparing the power of the measurement with the identified energy capacity, the model order selection can be automated.

There are limits to the existence of the cross Gramian for multiple-input, multiple-output (MIMO) systems. The system has to be square, stable and symmetric. The covariance driven system identification never provides a symmetric system. A way to create a symmetric system out of the asymmetric measurement data is presented by the use of a symmetrizer, such that the cross Gramian can always be computed. With this symmetrizer a method to estimate the system matrix A by a stable Lyapunov equation, which is classically computed by a numerically unstable pseudo inverse, is presented.
Physics Informed Gaussian Process for Bolt Tension Estimation
Authors:   University of Edinburgh, Rashid Dashty Samal ,   University of Edinburgh, Giorgio-Serchi Francesco ,   Shibaura Institute of Technology, Hosoya Naoki ,   University of Edinburgh, Garcia Cava David
Published: June 2024

Bolted joints are fundamental components in many engineering applications. Therefore, the need for monitoring their tension over their life span is an essential for securing their integrity. Modelling of the dynamics of a bolt has shown success through Euler-Bernoulli beam theory where a relationship of boundary conditions and tension allows determining the changes in the modal parameters. However, the widespread adoption of this approach has faces challenges, as obtaining high-fidelity data for bolts under all tension phases is often unfeasible in practice, particularly for those in low tension. Nevertheless, merging data and prior physics knowledge can provide practical constraints for bolt tension estimation in areas lacking observational data. This study establishes its foundation by developing a stochastic model for bolt tension estimation through the integration of bolt data-driven and physics-based predictions using Gaussian Process Regression (GPR). The core concept of this approach involves predicting data observations through a stochastic simulation using a physics-based model, particularly in scenarios where observational data is absent. The proposed methodology is validated with experimental data to critically evaluate its performance.
SensorDisc - a novel approach to in-situ assessment of bolt load and condition.
Authors:   fischerwerke GmbH & Co. KG, Rill Daniel ,   fischerwerke GmbH & Co. KG, Fortenbacher Matthias
Published: June 2024

While Structural Health Monitoring has become widespread and well-accepted for ensuring safety and longevity of buildings and facilities, the condition of fasteners in said structures is oftentimes not considered for holistic condition assessment.

This is not representative of the role fixings (in particular, bolt connections) play, as they experience high loads, fatigue, and corrosion, with the potential for causing costly consequences in case of failure.

In practice, the condition assessment of bolts is usually carried out by periodic re-tightening and manual documentation of the torque and apparent condition. This procedure is not only labor-intensive but also fails to provide deeper insights into the condition of the bolted connection itself. Furthermore, in many cases, the inspection intervals are not tailored to the specific needs of projects and structures but are based on general assumptions, which necessarily represent a compromise between costly over-caution and accepting blind spots.

One possible reason for not making more widespread use of monitoring technology in this case is the high cost and complexity of conventional bolt monitoring, where individual bolts are manually fitted with load sensors, and data processing is performed by highly skilled professionals.

A solution for this dilemma is presented in the form of the fischer SensorDisc, which offers a fully integrated, robust, and convenient option for bolt load assessment. One of the key features that distinguish the SensorDisc is its dedicated design for use in industrial and construction environments. Not only can it be used in a variety of bolt connections, either as original equipment or as a retrofit, but it also does not require special tools for readout, as this can be accomplished with a common smartphone.

For maximized ease of use, data and device management are all offered through a cloud-based service that can be accessed via an app, browser, or API.
Intelligent carbon-based TRC as self-strain sensor
Authors:   Technion- Israel Institute of Technology, Yosef Lidor ,   Technion- Israel Institute of Technology, Goldfeld Yiska
Published: June 2024

Abstract:

This study investigates the monitoring capabilities of a carbon yarn embedded in a cementitious matrix as an intelligent strain sensor by means of a gauge factor (GF). In the proposed configuration, the yarn simultaneously functions as the structural system and as the sensory platform, yielding efficient and intelligent concrete elements. The concept of the smart self-sensory system is based on monitoring changes in the electrical properties of the carbon yarn, namely: resistance, inductance, or impedance, and correlating them to the structural health. These capabilities were investigated in carbon yarns that were inserted within textile-reinforced concrete (TRC) structural elements, in which the correlation yielded important information in the macro-structural level. The integrative measurements were used to estimate the structural state associated with cracking and straining along the element. Yet, to fully understand the structural-electrical mechanism and to further enhance the development of the smart carbon-based sensor, it is essential to explore the electrical-micro-structural mechanism at the component level. Therefore, the study offers to explore the correlation between straining and cracking at the component level by means of GF. Separately investigating strained or cracked zones at the yarn’s level will provide vital information on the monitoring capabilities of the carbon yarn.

To answer these goals, the study will perform an experimental investigation of carbon-based TRC elements subjected to a designated pull-out test based on a uniaxial tensile setup. The proposed setup aims to correlate between changes in the electrical properties characterizing the carbon yarn and the micro-structural mechanism associated with the degradation of the structural health associated with the propagation of the crack.

The outcomes of this study aim to enhance the understanding of the structural-electrical correlation, which paves the way for the development of intelligent strain sensors for TRC structures.
A spin on active learning analysis for health monitoring.
Authors:   Sheffield University, Clarkson Daniel ,   University of Cambridge, Bull Lawrence A. ,   Sheffield University, Dardeno Tina A. ,   Sheffield University, Wickramarachchi Chandula T. ,   Sheffield University, Cross Elizabeth J. ,   Sheffield University, Rogers Timothy J. ,   Sheffield University, Worden Keith ,   Sheffield University, Dervilis Nikolaos ,   Sheffield University, Hughes Aidan J.
Published: June 2024

Decisions on the operation and maintenance of high-valued structures are critical, collecting and analysing data that can inform these decisions motivates the development of structural health monitoring (SHM) and tool monitoring (TM) systems. Descriptive health-state information labels for measured data points are crucial for successful monitoring systems. However, the labels can be expensive to collect and are often unavailable. Due to this problem, fully-supervised machine learning is limited in it’s application to monitoring systems which gives rise to unsupervised and semi-supervised approaches, one of these approaches is active learning. Active learning aims to highlight which unlabelled data points have the highest value of information and would be most informative to the model if they were labelled. For risk-based active learning in SHM, the model is used to advise some maintenance decision process where querying a data point means an expert inspecting the structure to determine its health-state. The current paper applies an active learning regression technology to a machining tool health case study. The algorithm queries data points that are most important in helping determine the health-state of the tool. Querying the most informative datapoints can drastically reduce the costs of a monitoring system. The regression analysis results are used to estimate tool wear and inform a risk-based maintenance decision process. This addresses the problem of when to replace a tool based on its health status, which has applications for many monitoring system problems.
Application of Modal Strain Energy Analysis to Localize an Added Mass on an Aluminium Plate.
Authors:   CEA Cadarache, Langlet Théo ,   Aix-Marseille University (AMU), Rakotonarivo Sandrine ,   CEA Cadarache, MICHEL Frédéric ,   Aix-Marseille University (AMU), Mattei Pierre-Olivier ,   Aix-Marseille University (AMU), Côte Renaud
Published: June 2024

The modal strain energy method is a vibration-based damage detection method that allows the use of passive measurements. The method is based on the measurement and analysis of the modal response of the structure under investigation.

The aim of this work is to investigate the application of this method to detect and localise a local change in a structure using acoustic measurements in a fluid around the structure.

Firstly, the modal strain energy is presented. The technique is based on the analysis of the modal shapes of undamaged and damaged samples obtained by operational modal analysis. The application of modal strain energy to off-contact acoustic measurements is then discussed.

The method is used to detect and locate a local change (an additional mass) in an aluminium plate subjected to a mechanical force. The study begins with numerical simulation using finite element models, followed by experiments on the aluminium plate.
Demonstrating the feasibility of monitoring bone fracture healing using electromechanical impedance spectroscopy – a comparison between three different inputs in Ward’s hierarchical clustering method
Authors:   Goethe University of Frankfurt am Main, Slanina Teresa ,   Goethe University of Frankfurt am Main, Beck Sebastian ,   Goethe University of Frankfurt am Main, Mälzer Moritz ,   Goethe University of Frankfurt am Main, Moll Jochen ,   Goethe University of Frankfurt am Main, Barker John H. ,   SurgiTAIX AG, Wieschebrock Dario ,   SurgiTAIX AG, WEBER Holger
Published: June 2024

Current methods for monitoring the progression of bone fracture healing require large, complex, and expensive imaging technology and, if used repeatedly, can expose patients to harmful ionizing radiation. Recent development of miniaturized, implantable, inexpensive microelectronic sensing technology now makes it possible to continuously monitor fracture healing non-invasively at the patient's bedside.
This proof-of-concept study demonstrates a novel, smart, implantable medical device based on electromechanical impedance spectroscopy. The setup consists mainly of a piezoceramic disk (PIC255 from PI Ceramics), an impedance chip (AD5933 from Analog Devices) and an inductive battery recharging system. The system operates autonomously for 24 hours of measurements. Every 20 minutes, the health state of the bone is measured. To test the setup, 20 tibia bones of sheep limbs obtained from a butcher's shop are used. By drilling five holes into the bone one after another, different defects mimicking bone fractures of different sizes are created. This simulates an inverse bone healing process. The collected data is transmitted via Bluetooth LE to a portable device that displays the impedance spectra. After the results have been reviewed, the data is transferred to a computer for statistical evaluation, such as the Root Mean Square Deviation (RMSD). Preliminary results have shown an increasing trend in RMSD with increasing number of holes in the bone. Based on these observations, an in-vivo study is planned.
Convolutional Autoencoders for Signal Reconstruction and their Application to Damage Signature Extraction
Authors:   Technical University of Madrid (UPM), Azuara Guillermo ,   Technical University of Madrid (UPM), Barrera Eduardo ,   Technical University of Madrid (UPM), Ruiz Mariano ,   Technical University of Madrid (UPM), Barba Pedro Luis ,   Technical University of Madrid (UPM), Piñas Alejandro
Published: June 2024

The utilization of Machine Learning (ML) techniques for Structural Health Monitoring (SHM) has increased in the last years. Using ML techniques for signal reconstruction has been explored in the literature of this field. In this work, a database of Ultrasonic Guided Waves (UGW), propagated through a thermoplastic composite plate, and generated and acquired with piezoelectric transducers (PZT), has been preprocessed, organized, and analyzed to reconstruct signals by using Convolutional Autoencoders (cAE) neural networks. This special type of autoencoders (AE) is based on the utilization of convolutional stages, which are formed by deep-learning layers that are especially useful for their utilization on discretized time-series signals, due to their ability to convolve different sections of the signal, thus extracting different features which are automatically learned by the network. The cAE, which have a bottleneck section in their middle part (between the encoder and the decoder sections), have the ability to reduce the dimensionality of the input data, thus allowing the extraction of a minimum number of relevant features of the signals. Summarizing, this paper presents a neural network designed as a cAE which is able to reconstruct data from a reduced number of features, reaching a correlation value between the real and the artificial data higher than 98%.
Utilising InSAR for monitoring infrastructure at national scales
Authors:   SatSense Ltd., Magnall Nathan ,   SatSense Ltd., Ingleby Tom ,   SatSense Ltd., Douglas Sarah
Published: June 2024

Interferometric SAR (InSAR) is now a widely used tool for the monitoring of infrastructure, in particular mines, dams, and oil and gas facilities. The technology is also widely used for the monitoring of construction activities such as tunnelling. With the growth of globally available and continuously updated SAR datasets, the monitoring of entire regional and national infrastructure networks is now feasible. Here we show how continuously updated, network-wide, InSAR datasets can be used to proactively manage large infrastructure networks such as roads, railways, and water pipelines.
In the UK continuously updated InSAR data (derived from Sentinel-1 SAR data) is being effectively used across the entire railway network to proactively monitor potential embankment failures, large-scale landslides, and potential hazards outside of the boundary fence. A challenge of using InSAR data in this way is the huge volume of data that is available, as such custom risk metrics and machine learning algorithms are applied to highlight some of the areas at heightened risk.
InSAR is also being applied to tackle the issue of water pipe leakage caused by natural ground movements, such as soil shrink-swell. In this case, regularly updated InSAR data has been used in the UK and Australia to detect areas at elevated risk of pipeline failures based on ground movement patterns, and by combining historical movement data and known failure events it is possible to refine such models. With expanding global SAR constellations proactive, nationwide, monitoring of critical assets is now possible, with the challenge being how we derive insight from these very large datasets.
Vibration-Based Railway Suspension Monitoring Under Varying Operating Conditions via Robust Statistical Time Series Type Methods
Authors:   University of Patras, Vlachospyros Georgios ,   University of Patras, Fassois Spilios D. ,   University of Patras, Sakellariou John S.
Published: June 2024

The on-board condition monitoring of railway suspension components is important for ensuring passenger comfort, maintaining proper operation, enhancing safety, and reducing operational costs. In this context statistical time series type random-vibration-based methods constitute a potentially efficient and cost-effective framework.

Yet a significant challenge emerges from the competing requirements for high sensitivity to suspension component degradation and robustness to varying Operating Conditions (OCs) and uncertainty. Varying Operating Conditions and uncertainty are related to factors such as varying payload, travelling speed, and rail profile. The problem is further aggravated by the complexity of the vehicle dynamics, including the wheelbase filtering effect.

Despite the significant progress made so far and the development of various methods, the challenges related to varying OCs and uncertainty largely remain as a technical barrier. The aim of the present study is the introduction of a data-based methodology aspiring to overcome them. The methodology is based on Multiple-Input Single-Output Transmittance Function (MISO-TF) time series models of a special form allowing for eliminating the effects of varying rail profile while properly treating the wheelbase filtering effect. Monitoring is based on a MISO-TF feature space, using either a Random Coefficient Gaussian Mixture or a Multiple Model based scheme. The former, although more elaborate, allows for the introduction of a complete probabilistic framework. An interesting advantage of both versions is that operation can be based on a very limited number of sensors and only a partial data-driven model of the dynamics.

The methodology is comprehensively assessed via hundreds of SIMPACK based Monte Carlo runs under variable payload and rail profiles. The achievable performance is shown to be very good for both versions and for 10%, 15%, and 20% of condition degradation in either primary or secondary suspension components.
A Framework for Condition Survey and Structural Health Monitoring of Masonry Arch Bridges
Authors:   Universitat Politècnica de Catalunya - BarcelonaTech, Gönen Semih ,   Universitat Politècnica de Catalunya - BarcelonaTech, Garcia-Ramonda Larisa ,   Universitat Politècnica de Catalunya - BarcelonaTech, Cabané Cañas Albert ,   Universitat Politècnica de Catalunya - BarcelonaTech, Drougkas Anastasios ,   Universitat Politècnica de Catalunya - BarcelonaTech, Roca Pere ,   Universitat Politècnica de Catalunya - BarcelonaTech, Chacón Rolando ,   Universitat Politècnica de Catalunya - BarcelonaTech, Pelà Luca
Published: June 2024

Masonry arch bridges are essential elements of the road and railway networks of Europe, as they pertain to cultural heritage and play an important role in transportation. The integrity and safety of these centuries-old structures must be ensured. Therefore, there is great interest in understanding the behavior of masonry arch bridges and monitoring their health. Efficient methods for assessing the condition of structures, including inspections and monitoring, constitute a fundamental element of structural health monitoring (SHM) applications. Given the multitude of surveying techniques available, it becomes crucial to establish cost-effective sensor placement strategies capable of detecting structural failures. These strategies can involve identifying particularly sensitive areas, optimizing the number of sensors used, as well as refining the number and duration of monitoring setups, among other considerations. As part of the PONT3 project, which aims to develop a comprehensive and cost-effective approach to anticipate failure propagation in ageing bridges, this research focusses on developing interoperability-driven inspection and monitoring strategies to provide data that will allow anticipating the evolution of possible failure scenarios. In this regard, an extensive summary of the key characteristics of inspection and monitoring technologies, and typical system configurations in existing literature for masonry arch bridges will be provided. Then, the measurable physical parameters of interest for a specific failure propagation scenario will be identified using numerical simulations and applicable monitoring techniques for each of the physical parameters will be determined. Monitoring requirements in terms of measurement locations, accuracy, range, and temporal resolution will also be identified. The recommendations for interoperability-driven data collection to exchange and employ acquired information for the purpose of structural diagnosis will conclude the framework. The study will finally discuss a future outlook on the development of integrated SHM-oriented digital twins.
Advancing Hydrogen Sensing for Sustainable Aviation: A Metal Hydride Coated TFBG Optical Fibre Hydrogen Sensor
Authors:   Delft University of Technology (TU Delft), Dissanayake Kasun P. ,   Delft University of Technology (TU Delft), Dewi H. Sandra ,   Delft University of Technology (TU Delft), Schreuders Herman ,   Delft University of Technology (TU Delft), Bannenberg Lars J. ,   Delft University of Technology (TU Delft), Groves Roger M.
Published: June 2024

The aviation industry is undergoing a significant shift toward sustainability, with hydrogen emerging as a promising green fuel to reduce carbon emissions due to its high gravimetric energy density and clean combustion exhaust compared to traditional aviation fuels. However, realizing hydrogen's aviation potential requires advanced sensors to ensure safety and efficiency in challenging environments, particularly for monitoring hydrogen near high-temperature engines and cryogenic storage tanks. Fibre optic sensors offer an exceptional advantage in these demanding conditions due to their capacity to operate in harsh environments, their small size, immunity to electromagnetic interference, spark-free operation, and remote sensing capabilities. Metal hydrides are prominent sensing materials studied for hydrogen sensing, especially palladium (Pd). Nonetheless, existing literature pertaining to Pd-coated fiber optic sensors for hydrogen detection has brought to light concerns regarding response hysteresis, stability, and linearity of Pd hydrogen sensors, calling for innovative solutions. In response to these challenges, our research introduces an innovative fibre optic hydrogen sensor, introducing tantalum (Ta) based metal hydrides as the sensing material. The sensor's construction involved the deposition of a nanometer-thick Ta thin film onto the surface of a tilted fibre Bragg grating (TFBG) using a magnetron sputtering technique. The sensing mechanism relies on the modulation of optical constants of the Ta thin film in response to even minute hydrogen concentrations, culminating in spectral changes in the transmission spectrum of the TFBG. In this proof of concept work, changes in both amplitude and the centre wavelength of cladding resonances of the TFBG transmission spectrum were observed in the hydrogen concentration range from 0.01% to 4% at room temperature. These initial findings underscore the substantial potential of our approach in developing highly sensitive and robust fibre-optic hydrogen sensors tailored specifically for hydrogen-powered aviation.
Differential Evolution for Finite Element Model Updating: Algorithm and Application in Structural Analysis
Authors:   InfoGraph GmbH, Abu Abed Wassim
Published: June 2024

The availability of a numerical model that accurately reproduces the behaviour of an existing structure is paramount for diagnostic and prognostic purposes in Structural Health Monitoring. An important procedure to this end is updating the parameters of the numerical model on the basis of measured data. This process ideally involves the use of an optimisation method that adapts the parameters of the model so that the discrepancy between the numerically calculated structural response and the measured one is minimised.

In this paper a procedure for updating finite element models for the application in structural analysis is presented. The updating algorithm uses a simple and efficient adaptive population based method for global optimization over continuous spaces known as Differential Evolution. Continuous interpolation functions are utilised in order to reduce the dimension of the search space resulting in speeding up the convergence without jeopardising the reliability of finding the solution or its accuracy.

Three different examples with artificial datasets are used to verify the updating algorithm. First, a static finite element model of a 10 m single span beam consisting of beam elements with linear elastic material behaviour is considered. The second example consists of a two-dimensional single-span reinforced concrete road bridge consisting of slab elements. In both examples, the distribution of the modulus of elasticity of the elements are updated out of displacement data. The third example involves the dynamic analysis of a train crossing. A load case of an ICE1 crossing over a 32 m single-span bridge consisting of beam elements is assumed. The objective of this example is updating the distribution of the modulus of elasticity of the elements out of acceleration data.

The paper concludes with a discussion of the advantages and disadvantages of the developed updating algorithm. An overview of outstanding issues and improvements is also given.
Automated modal parameter tracking with neural network based normalization of environmental perturbations
Authors:   Université Gustave Eiffel, Priou Johann ,   Aarhus University, Greś Szymon ,   Sercel, Perrault Matthieu ,   Sercel, Guerineau Laurent ,   Sercel, Desbazeille Mathieu ,   Université Gustave Eiffel, Döhler Michael
Published: June 2024

Automated modal parameter tracking is an essential part of vibration-based structural health monitoring of engineering structures, e.g., bridges, high-rise building, wind turbines, amongst others. In this work, we conceive a robust algorithm for accomplishing this task. First, the modal parameters are extracted with by greedy clustering the stabilization diagram, which is based on covariance-driven subspace identification and the related uncertainty quantification. After the selection of a set of relevant modal parameters to be monitored, the tracking is performed by an active search of stable modes close to the reference ones, again using statistical criteria and automated clustering. The applied strategies allow for a robust method, allowing also for relatively strong variations in the natural frequencies or the MAC without losing track of the monitored modes. Finally, when EOVs (Environmental and Operational Variables) are measured and training and testing periods are defined, a neural network is trained based on the tracked frequencies and the EOVs, with the goal of correcting the environmental influence on the estimated frequencies based on the model predictions. In doing so, a distance metric is defined for damage detection, which is robust to environmental perturbations under the measured EOVs. The whole framework is showcased on monitoring data of a full-scale bridge.
Validation of a model-based dual-band modal decomposition and expansion approach for fatigue monitoring of offshore wind turbine foundations
Authors:   Vrije Universiteit Brussel (VUB), Fallais Dominik ,   Vrije Universiteit Brussel (VUB), Sastre Jurado Carlos ,   Vrije Universiteit Brussel (VUB), Weijtjens Wout ,   Vrije Universiteit Brussel (VUB), Devriendt Christof
Published: June 2024

The fatigue limit state strongly drives the lifetime of offshore wind turbine support structures. Despite steady advancements in their design, the accumulation of fatigue damage over the lifetime of the structure remains uncertain; one way to improve certainty is to directly monitor structural loads at fatigue-critical locations, e.g., near or below the mudline. However, directly monitoring the loads at these locations is often not possible, since these locations do not allow for easy installation or retrofitting of sensors. To this end, model-based virtual sensing techniques can be employed to estimate the structural response at unmeasured locations based on vibration response data and a finite element model which can accurately describe the modal and operational deflection shapes of a specific turbine [1,2,3].
For this contribution, a modal decomposition and expansion (MDE) algorithm [4] will be used to predict strain time series at fatigue-critical locations, based on acceleration and strain data obtained from the tower of an operational OWT. Emphasis will be put on sparse data situations in which a realistic amount of sensor data is used as input. Fibre-optical strain gauges close to and below the mudline level (Fig 1) will be used for direct validation of the extrapolation capabilities. The required finite element model is built using an in-house developed and verified integrated FE model class [5,6], in combination with a unique database which contains all available geotechnical and structural data of offshore wind turbines (owimetadatabase) [7,8]. This combination allows us to generate fleet-wide, turbine-specific, finite elements models for several wind farms. The results will be presented in terms of time series (e.g., Fig. 2) and fatigue damage-related metrics. Preliminary results show that strain time series estimated with the MDE method show good agreement with the validation data.
Enhancing acoustic emission analysis for corrosion damage detection in reinforced concrete by k-nearest neighbors method
Authors:   Ege University, Işık Furkan ,   Ege University, Inderyas Omair ,   Niigata University, Suzuki Tetsuya ,   Ege University, Alver Ninel
Published: June 2024

The Acoustic Emission (AE) method is considered a significant tool for detecting damage in reinforced concrete structures caused by reinforcement corrosion. However, processing a large amount of AE data using traditional analysis methods is time-consuming. Therefore, integrating machine learning (ML) techniques into this process offers a more logical and effective solution to expedite data analysis and enhance damage detection accuracy.

This study aims to train an ML model for detecting corrosion damage in reinforced concrete structures using an AE dataset. In pursuit of this goal, it seeks to enhance source localization that contributes to identifying crack patterns through signal-based analysis methods. AE data obtained from a reinforced concrete structural element subjected to accelerated corrosion testing in laboratory conditions have undergone detailed analyses with the ML model. In this context, an ML model capable of learning signals with various wave characteristics and parameters has been successfully integrated into the source localization algorithm, significantly improving AE source localization outcomes. Furthermore, this method is critical for structural safety as it enables the early detection of reinforcement corrosion and precise localization of cracks. The integration of this machine learning model with AE data serves as a valuable tool developed for more effective monitoring of damage in structures.
Advancing Structural Health Monitoring in Civil Engineering with Greybox Modelling: A Review
Authors:   Helmut-Schmidt-University / University of the Federal Armed Forces Hamburg, Karimi Saboor ,   Helmut-Schmidt-University / University of the Federal Armed Forces Hamburg, Gündel Max
Published: June 2024

This review explores the application of Greybox Modelling, specifically Physics-Informed Machine Learning (PIML), in the context of Structural Health Monitoring (SHM) within civil engineering. SHM is vital for ensuring the safety and durability of infrastructure namely bridges. Greybox Modelling, merging physics-based principles with data-driven techniques, offers a functional approach for understanding complex structural behaviors. By leveraging existing knowledge of physical laws, this method extracts valuable insights from sensor data. The integration of Greybox Modelling takes SHM a step further. By applying machine learning algorithms with the physics that govern civil engineering structures, Greybox Modelling bridges the gap between data-driven models and physics-based models. This leads more accurate predictions and interpretable outcomes.

This review discusses findings from literature, recent studies, that illustrate the effectiveness of Greybox Modelling, specifically PIML in various civil engineering applications. These case studies highlight how this approach can adapt to different structural designs and environmental conditions typically encountered in civil engineering. Moreover, they indicate its potential for damage detection and early warning systems, which enhance the safety and efficiency of infrastructure. The paper also addresses current challenges and outlining future directions in the field.
How to Be Certain: Using Known Relations and Trust Discount to Determine Confidence About the Degree of Uncertainty
Authors:   Leibniz University Hannover, Winnewisser Niklas R. ,   Leibniz University Hannover, Potthast Thomas ,   Leibniz University Hannover, Mett Felix ,   Leibniz University Hannover, Perin Andrea ,   Leibniz University Hannover, Broggi Matteo ,   Leibniz University Hannover, Beer Michael
Published: June 2024

Ensuring certainty in the rapidly evolving digital twin environments amidst inherent uncertainty is paramount in decision-making for critical engineering systems. This paper presents a novel approach for quantifying and tracking confidence levels within a network of sensors and structural models, specifically addressing uncertainty arising from faulty sensors. By leveraging known relations and implementing a trust discount mechanism, our methodology offers a fundamental framework for navigating in the presence of uncertainties.

A quasi-real-time case study on a cantilever beam model is simulated to demonstrate the efficacy of the proposed approach. We showcase the ability of our method to accurately assess and adapt to varying levels of uncertainty introduced by faulty sensors. Our findings highlight the importance of incorporating trust dynamics and established relationships within digital twin environments to achieve improved certainty despite imperfect data. This research contributes to the theoretical underpinnings of digital twin technology and offers practical insights for its application across diverse domains.
Crack Tip Localization in Adhesively Bonded CFRP-CFRP Joints subjected to Mode II Fatigue Loading
Authors:   Politecnico di Milano, Panerai Alessandra ,   Politecnico di Milano, Oneda Benedetta ,   Politecnico di Milano, Bernasconi Andrea ,   Politecnico di Milano, Carboni Michele
Published: June 2024

The use of resin-based composite materials in place of metals has gained prominence across various industries. Such composites offer a superior strength-to-weight ratio, addressing the increasing demand for lightweight structures to reduce emissions. As such, structural joining of composite materials is of growing importance. Traditional fasteners, however, not only compromise weight savings, but also introduce weak points within the structure. Adhesive bonding emerges as the most promising joining technology, balancing weight efficiency and performance. However, its broader adoption for primary structural joints is hindered by a lack of standardization of quality control and non-destructive inspection techniques and concerns about long-term reliability.

To address this challenge, the “CERTBOND” COST Action initiative (CA18120) was established to investigate adhesive bonding and develop a reliable certification roadmap for bonded primary structures. As part of this initiative, a Mode II fatigue loading Round Robin study was conducted on adhesively bonded CFRP-CFRP End-Notched Flexure specimens. While conducting the fatigue tests, supplementary techniques for estimating the crack tip position were employed. The selected techniques included visual testing (employing two different methods), ultrasonic phased array testing, distributed strain sensing using optical backscatter reflectometry, digital image correlation, and localization by acoustic emission. The present study describes, compares and discusses the results obtained by the cited localization techniques.
Mitigating Ill-posedness in Parameter Estimation under Sparse measurement for Linear time-varying systems employing Virtual Sensor Responses
Authors:   Indian Institute of Technology Mandi (iitmandi), O A Shereena ,   Indian Institute of Technology Mandi (iitmandi), Sen Subhamoy ,   Université Gustave Eiffel, Mevel Laurent
Published: June 2024

Monitoring linear time-varying (LTV) systems using model-based approaches typically requires dense instrumentation and high-fidelity support models, resulting in significant computational and financial costs. Bayesian filtering-based methods adopt a joint state-parameter estimation approach for LTV system monitoring wherein states/parameters are observed as well as inferred from the measurements. Eventually, sparsity in measurement while dealing with high-fidelity models can aggravate the ill-posedness in the estimation diverging the estimation to impractical or no solutions. To enhance estimation resolution and precision, an alternative approach can be supporting estimation with virtual sensor measurements sampled from future time steps. Virtual measurements are in fact measurements sampled from a future time step relative to the time at which estimation is sought. To leverage virtual measurements, the estimation needs to be time-delayed, enabling the observation of states/parameters through measurements taken at both current and subsequent time steps, thereby alleviating the ill-posedness. This method has been explored for an LTV spring-mass-dashpot system, where the numerical investigation utilizes an interacting filtering environment to estimate states and parameters in the presence of sparse measurements. The study has shown how incorporating additional information can stabilize the estimation process, leading to improved estimates for both states and parameters.