bluuebunny/crossref_metadata_2025 huggingface dataset:
Model Updating and Assessment using Axial Strains for a Statically Determinate Truss Bridge
Authors:   Indian Institute of Technology Madras (IITM), Lenka Kaibalya ,   Indian Institute of Technology Madras (IITM), Umakanthan Saravanan
Published: June 2024

The safety of aging structures over the course of their service life is given by structural reliability. Additionally, there is a lack of techniques available for processing raw structural health monitoring (SHM) data in a way that makes it possible to validate and/or update structural reliability. Along with the diagnosis of structural anomaly using SHM techniques, the prognosis of structural condition is also an important aspect. Field-measured strain responses can be used as an indication of structural safety and provide information regarding the load-carrying capacity of the structure. Reliability assessment can be conducted only if a more accurate digital analogue is obtained for the actual bridge and when the structural responses can be predicted accurately. This paper validates a digital model of a real-life railway truss bridge. Pamban bridge is a century-old railway bridge having one track and four internally statically determinate truss leaves. Electrical strain gauges are instrumented to measure the axial strain responses in 84 out of 92 members of the bridge. Strain responses of 1072 train movements that traversed the bridge from 1st January 2021 to 14th July 2021 are recorded, and root mean square error (RMSE) between these field-measured strain responses and analytically computed strain responses from the digital model are estimated. The stationary trend of the time history of RMSE values depicts constant structural reliability. A technique is also proposed to forecast structural strain responses, which is validated with field-measured strain responses.
Phase-based Full-field Displacement Measurement with Phase Nonlinearity Weighting Process
Authors:   Chonnam National University, Kong Yeseul ,   Chonnam National University, Lee Seung Hwan ,   Chonnam National University, Park Gyuhae
Published: June 2024

The camera, as a non-contact sensor, has evolved into a robust tool for measuring the full-field vibration of complex structures. It offers distinct advantages over traditional contact sensors, including flexible positioning, simultaneous multi-point tracking, and high spatial resolution. Vibration imaging techniques have been developed to identify structural operational conditions through feature extraction methods. Phase-based vibration imaging techniques can visualize full-field operational shapes or vibrational features in a less-supervised manner. While phase-based optical flow with Gabor filters provides accurate displacement estimation, managing a large amount of image data can pose challenges in extracting full-field displacement efficiently.
In this paper, we propose a compressive sensing approach for full-field phase-based optical flow and vibration imaging. Compressive sensing serves as an efficient sampling method that requires significantly fewer frames than traditional approaches, selecting frames at less than one third of the original video. Features are extracted from the displacement of the compressed video frames. Subsequently, the vibration features are transformed into images. The proposed methodology is validated through experiments conducted on an air compressor. Comparative results between vibration imaging from video with compressive sensing and that from the original video demonstrate that vibrational features can be extracted using only a few frames of the original video. Consequently, this approach can reduce processing time and data volume while preserving the performance of vibration feature extraction.
Robust instrumentation of composite pressure vessels manufactured via multi-filament winding for distributed fiber optic sensing
Authors:   RWTH Aachen University, Bareiro Oscar ,   Fraunhofer Institute for Production Technology (IPT), Girmen Caroline ,   FibrisTerre GmbH, Nöther Nils ,   RWTH Aachen University, Gries Thomas
Published: June 2024

The development of hydrogen storage and transport technologies with safety, efficiency and economy is one of the main scientific and technological challenges to stablish the infrastructure for a hydrogen economy. The storage must not only be lightweight and compact but also safe, cost-effective, long-range and rapidly refuellable. Type IV composite pressure vessels have been recognized as a cost-efficient solution that can address these problems for on-board storage in the hydrogen-powered mobility and hydrogen transport in tube trailers. The demanding working environment presents a challenge to the failure analysis and predictive maintenance of composite pressure vessels; the knowledge of the precise condition of the pressure vessel is therefore mandatory to guarantee safety usage. The continuous strain monitoring of composite pressure vessels via distributed fiber optic sensors (DFOS) can enable the implementation of a structural health monitoring system. The robust instrumentation of composite pressure vessels with DFOS is here presented; the pressure vessel manufacture is based on the multi-filament winding. Small pressure vessel prototypes are designed and manufactured using carbon fibers as reinforcement; DFOS are integrated into the cylindrical section composite laminate during winding. A connector housing is placed on the dome section of the pressure vessel to protect sensor exit/entry points. The condition of the integrated DFOS is assessed throughout manufacture (winding and consolidation) via optical time domain reflectometry (OTDR). Afterwards, hydraulic burst tests are performed with step-wise pressure increments and the strain evolution and failure are monitored via distributed strain sensing. The integrated DFOS are capable of monitoring the strain evolution over the length of the vessel for the circumferential direction.
Distributed fiber optic strain sensing for structural health monitoring of 70 MPa hydrogen vessels
Authors:   BAM Federal Institute for Materials Research and Testing, Wosniok Aleksander ,   BAM Federal Institute for Materials Research and Testing, Schukar Marcus ,   Plastic Omnium New Energies, Woody Paul ,   BAM Federal Institute for Materials Research and Testing, Breithaupt Mathias ,   BAM Federal Institute for Materials Research and Testing, Wang Bin ,   BAM Federal Institute for Materials Research and Testing, Kriegsmann Andreas
Published: June 2024

We report on the development and testing of 70 MPa hydrogen pressure vessels with integrated fiber optic sensing fibers for the automotive use. The paper deals with the condition monitoring of such composite pressure vessels using the optical backscatter reflectometry (OBR) which was applied for a distributed fiber optic strain sensing along fully integrated polyimide-coated single-mode glass optical fiber (SM-GOF). The sensing fibers were embedded into the vessel structure by wrapping them over the inside polymer liner during the manufacturing process of the outside carbon fiber reinforced polymer (CFRP). Detecting local strain events by the integrated fiber optic sensors might be an opportunity for monitoring the material degradation. Regarding the investigation of the ageing behavior, both ambient hydraulic cycling tests and slow burst tests were conducted on 70 MPa pressure vessels with embedded fiber optic sensors. The achieved results contribute to the knowledge about material fatigue to guarantee the safe usage during the entire lifetime of the composite hydrogen storage vessels.
The presented work stands in context of the European Horizon 2020 research project SH2APED focused on the development of an innovative hydrogen storage system composed of an assembly of nine 70 MPa tubular pressure vessels used as an automotive battery pack.
Ground penetrating radar for moisture assessment in railway tracks: An experimental investigation
Authors:   ETH Zürich, Arcieri Giacomo ,   ETH Zürich, Rigoni Thomas ,   ETH Zürich, Hoelzl Cyprien ,   Schweizerische Bundesbahnen SBB, Haener David ,   ETH Zürich, Chatzi Eleni
Published: June 2024

The health monitoring of railway networks offers a means to ensuring high-quality service, avoiding safety risks, and optimally planning maintenance actions to minimize life-cycle costs. Monitoring of the substructure is particularly linked to the tracking of degradation, which often stems from water infiltration, causing moisture accumulation in the underlying ballast layers. Such deterioration incurs substantial expenses in the order of several million per annum, while reducing the useful life of the track infrastructure. In optimizing management, it is important to improve detection schemes via affordable and non-invasive procedures. Such a solution is found in use of Ground Penetrating Radar (GPR) technology, which employs non-invasive radar pulses to map the subsurface, and possibly detect water infiltration. Train-based GPR systems thus offer tremendous potential for the development of preventive and automated monitoring of railway network infrastructure. Nevertheless, despite previous efforts in this direction, the technology remains relatively under-explored and lacks comprehensive studies to establish its suitability for this task. Moreover, there is no consensus on a standardized procedure for automatic inference of reliable indicators of railway health from GPR observations. In this work, we report on an extensive experimental analysis conducted on a controlled railway track, built by the Swiss Federal Railways, for this campaign. The humidity condition of the railway track was artificially altered to reach different levels of water content and GPR measurements were gathered under the varying conditions, with ground truth assessed through lab tests on collected samples. GPR data with complete ground truth labels deliver a rare benchmark, which can enhance understanding of this technology. Our findings show that GPR systems can effectively detect moisture infiltration in railway tracks, although several challenges are to be addressed for the development of accurate, automated procedures.
Guided-wave Dynamic Probability Model-based Crack Quantification Method for Metal Structures
Authors:   Nanjing University of Aeronautics and Astronautics  (NUAA), Ouyang Li ,   Nanjing University of Aeronautics and Astronautics  (NUAA), Fang Fang ,   Nanjing University of Aeronautics and Astronautics  (NUAA), Qiu Lei ,   Nanjing University of Aeronautics and Astronautics  (NUAA), Yuan Shenfang
Published: June 2024

In the process of applying guided-wave-based aircraft online structural health monitoring, the characteristics of guided wave signals used to represent structural status exhibit random uncertainty due to the interference of loads on guided wave propagation. The trend of signal feature changes caused by damage is often obscured, making the quantification of cracks under loads one of the challenges in online structural health monitoring for aircraft. To address this challenge, this paper proposes a crack quantification method using an adaptive Gaussian mixture model (GMM) and optimized probability migration distance measurement. This method effectively mitigates the uncertainty caused by loads by constructing an adaptive Gaussian mixture model to track the probability distribution changes of guided wave signal features under different structural health conditions. Subsequently, an optimized probability migration distance measurement method is employed to characterize the structural state, further enhancing the correlation between features and the extent of structural damage. Finally, an optimized probability migration distance relationship with crack length is established to estimate the crack length of new structural specimens. The effectiveness of this method is validated using load-induced crack propagation experiments on metal structures.
Towards reliability assessment of ultra-wideband microwave leakage monitoring in detecting stringer debondings in composite airframes
Authors:   University of Naples Federico II, Memmolo Vittorio ,   Goethe University of Frankfurt am Main, Moll Jochen ,   Deutsches Zentrum für Luft- und Raumfahrt (DLR), German Aerospace Center, Moix-Bonet Maria ,   Villari B.V., Lugovtsova Yevgeniya ,   Deutsches Zentrum für Luft- und Raumfahrt (DLR), German Aerospace Center, Schmidt Daniel ,   Goethe University of Frankfurt am Main, Krozer Viktor ,   Bochum University of Applied Sciences (BO), Mueller Inka
Published: June 2024

Ultra-wideband guided electromagnetic waves have been proposed recently for non-destructive testing and structural health monitoring applications. Among the multitude of possible microwave waveguides, recent works explored the concept of microwave leakage monitoring to detect stringer debondings in hollow carbon composite structures. The principle is to monitor the wave propagation characteristics within the cavity formed by the stringer and the airframe skin using transmission measurements. Changes in the transmission path, e.g. occurring from microwave leakage in the debonded region, can be assessed through a damage indicator approach.
To proof the reliability of this concept, an experimental campaign was carried out according to the building block approach typically used in aeronautics. First, a small-scale specimen with a debonded stringer was investigated in the laboratory to proof the methodology. Then, a large-scale fuselage segment was used for technology demonstration enabling stringer debonding with increasing severity through static and fatigue loading. The paper shows that the microwave characteristics in the waveguide (stringer tunnel) is affected by the debonding conditions according to its severity and depth. In addition, extending the same approach on a number of test cases, the probability of detection was carried out showing the minimum defect size which can be identify reliably. This allows designing a reliable structural health monitoring system based on guided electromagnetic waves trapped in the tunnel.
Physically-informed change-point kernels for variable levels of physical knowledge inclusion in Gaussian processes
Authors:   Sheffield University, Pitchforth Daniel James ,   Sheffield University, Jones Matthew R. ,   Sheffield University, Cross Elizabeth J.
Published: June 2024

The relative balance between physics and data within any physics-informed machine learner is an important modelling consideration to ensure that the benefits of both physics and data-based approaches are maximised. An over reliance on physical knowledge can be detrimental, particularly when the physics-based component of a model may not accurately represent the true modelled system. An under utilisation of physical knowledge potentially wastes a valuable resource, along with benefits in model interpretability and reduced demand for expensive data collection. Although adjusting the relative levels of physics and data reliance within a model is possible through the adaptation of the model structure, in practice, this can be challenging, with the relative balance produced by new model structures not always clear before they are implemented. This paper presents a means of being able to tune the balance of physics and data reliance within a model through the development of physically-informed change-point kernels for Gaussian processes. These combine more structured physical kernels, capable of enforcing physically derived behaviours, with flexible, general purpose kernels, and provide means to dynamically change the relative levels of reliance on physics and data within a model.
Acquiring a Machine Learning Data Set for Structural Health Monitoring of Hydrogen Pressure Vessels at Operating Conditions using Guided Ultrasonic Waves
Authors:   Saarland University, El Moutaouakil Houssam ,   Saarland University, Fuchs Christian ,   Fraunhofer Institute for Ceramic Technologies and Systems IKTS, Savli Enes ,   BAM Federal Institute for Materials Research and Testing, Heimann Jan ,   BAM Federal Institute for Materials Research and Testing, Prager Jens ,   Goethe University of Frankfurt am Main, Moll Jochen ,   Fraunhofer Institute for Ceramic Technologies and Systems IKTS, Tschöke Kilian ,   Goethe University of Frankfurt am Main, Márquez Reyes Octavio A. ,   Goethe University of Frankfurt am Main, Schackmann Oliver ,   Goethe University of Frankfurt am Main, Memmolo Vittorio ,   Saarland University, Schneider Tizian
Published: June 2024

Hydrogen is an energy source of increasing importance. As hydrogen is very reactive to air and
needs to be stored under high pressure, it is crucial to provide safe transportation and storage.
Therefore, structural health monitoring, based on guided ultrasonic waves and machine
learning methods, is used for Composite Overwrapped Pressure Vessels (COPVs) containing
hydrogen.
To acquire data that allows robust detection of COPV defects, there are two main process
parameters to consider. These are the pressurization of the vessel and the temperature
conditions at the vessel. This paper will focus on the derivation of a design of experiment (DoE)
from the needs of various validation scenarios (e.g. concerning pressure, temperature or
excitation frequency). Practical limitations must be considered as well.
We designed experiments with multiple reversible damages at different positions. A network
of 25 transducers, structured as five rings with five sensors in one line, is installed on a vessel.
Guided ultrasonic waves are used via the pitch-catch procedure, which means that the
transducers act pairwise as transmitter and receiver in order to measure all transmitterreceiver combinations. This leads to 600 signal paths, recorded by a Verasonics Vantage 64 LF
data acquisition system. Finally, the influences of temperature and pressure within the
acquired data set are going to be visualized.
Two-dimensional stiffness identification by inversion of local wavenumbers based on artificial neural network
Authors:   Nanjing Tech University, Yang Lehui ,   Nanjing Tech University, Zhao Jinling ,   Nanjing Tech University, Chang Le ,   Nanjing Tech University, Zhao Jianping
Published: June 2024

Impact and fatigue damages in carbon fiber reinforced polymers (CFRP) are unavoidable in severe operating conditions, which will lead to the stiffness properties degrading and redistributing in space. The local wavenumbers of guided waves at a specific point depend on the stiffness at that spatial location. Therefore, the two-dimensional stiffness properties can be determined by inversion of local wavenumbers. In this paper, the relationship between the local wavenumbers and the stiffness coefficients was established by the artificial neural network (ANN) method. For each spatial point, based on the semi-analytical finite element (SAFE) method, the wavenumbers (inputs of ANN) with different frequencies can be obtained at given stiffness coefficients (outputs of ANN). Furthermore, the accuracy of the ANN method was validated by the finite element model. Results show that the predicted two-dimensional stiffness coefficients of the damaged region are close to the true ones, thus demonstrating the accuracy of the two-dimensional stiffness identification method.
Reliability Assessment of Damage Localization Techniques for Guided Wave-based Structural Health Monitoring Systems in Composite Stiffened Structures
Authors:   Bochum University of Applied Sciences (BO), Bayoumi Ahmed ,   Fraunhofer Institute for Ceramic Technologies and Systems IKTS, Savli Enes ,   Bochum University of Applied Sciences (BO), Mueller Inka ,   Fraunhofer Institute for Ceramic Technologies and Systems IKTS, Tschöke Kilian ,   University of Naples Federico II, Memmolo Vittorio ,   Goethe University of Frankfurt am Main, Moll Jochen
Published: June 2024

In Guided Wave-based Structural Health Monitoring (GWSHM) systems, the reliability assessment is of utmost importance. The primary focus is the smallest damage that can be detected with 90 percent probability and 95 percent confidence. However, the probability of correct damage localization is equally crucial as the probability of detection. In this view, defining the smallest damage that can be localized accurately in a statistically meaningful way is quite challenging.

Several localization techniques have been developed in the literature to localize damage resorting to a GW sensor network, like SAFT and RAPID. This article investigates the reliability of such SHM systems with these localization techniques using an experimental data set from the Open Guided Waves (OGW) platform and compares it with predictions obtained via a simulation model using the EFIT method. A carbon fibre-reinforced plate-like structure reinforced by an omega stringer and a GWSHM system employing twelve piezoceramic transducers bonded over the surface to excite and receive guided waves are used. Pseudo damage of thirteen different sizes are attached to three
different locations using a damping tacky tape. Moreover, the EFIT modelling tool is used to simulate the experiment with an extensive number of damage scenarios at the defined three damage locations with a high degree of consistency with the experimental data, facilitating a more robust analysis. The Outcome concerning the influence and correlation between damage size on localization accuracy, considering various localization techniques, were explored and discussed. In addition, the reliability assessment allowed to establish the performance of the localization approaches in a rigorous way.
Infrared Thermography Inspection of Stay Cables
Authors:   SITES, Manzini Nicolas ,   SITES, Carreaud Pierre ,   DELFOSSE CONSEILS, Delfosse Gérard
Published: June 2024

The injection of petroleum waxes into bridge stay cables is a proven method for protection against corrosion, primarily due to their sealing properties, adhesion to strands, and thermal stability. However, with a liquefaction point below 70°C, exceptional ambient conditions can lead to partial liquefaction of the injected waxes, especially for cables with a dark sheath, resulting in leaks and extrusions. Sections of stay cables may lose their corrosion protection without visible signs from the outside.
This study explores the use of infrared thermography for identifying and mapping areas lacking wax. It involves acquiring high-resolution infrared thermal images of the stay cables and studying temperature variations. Since wax has a higher thermal inertia than air, a thermal gradient is expected between areas with wax and those without after the daily heating cycle.
This paper presents a campaign conducted on a cable-stayed bridge in France. During this campaign, temporary instrumentation was deployed to identify the optimal intervention window. Multiple sessions of acquisitions using a high-precision thermal camera were then performed. The acquired images were subsequently rectified and assembled to create uniform and comprehensive panoramas of the stay cables. The results obtained for several characteristic cables are presented and discussed in this paper.
Guided wave based SHM for loosening detection in bolted lap joints with the application of Neural Networks
Authors:   Indian Institute of Space Science and Technology, K. b. Goutham ,   Indian Institute of Space Science and Technology, Mohan hajare Mayuresh ,   Indian Institute of Space Science and Technology, C. r. Bijudas
Published: June 2024

Bolted and riveted connections are a mainstay in many aerospace, mechanical, and civil engineering applications. Early detection of bolt loosening is essential for ensuring structural integrity and preventing catastrophic failures. Guided wave-based structural health monitoring (SHM) systems are well-suited for damage detection in plate-like structures. The present study aims at an experimental investigation of Lamb wave propagation in an aluminum lap joint with two bolts using piezoelectric transducers and compares the effectiveness of different damage indices based on wave energy transmission between plates in looseness detection. Torque values ranging from 5 Nm to 12 Nm at 1 Nm intervals are applied, along with a hand-tightened condition as well as free plates without bolts. Torquing conditions are evaluated for both bolted joints individually as well as simultaneously. The experiment is currently in progress, and the preliminary results are being examined. Further to this, the experimental findings will be compared with the finite element (FE) modelling results. Relatively few studies have explored the use of deep learning algorithms to enhance the performance of guided wave-based SHM systems for bolt loosening detection. The central goal of this study is to develop a neural network architecture for a Lamb wave propagation-based SHM system for bolted connections. This network will be trained using damage indices from the FE modelling results and later validated with the experimental data.
Impact of the Metal Volume Fraction on the Propagation of Guided Ultrasonic Waves in Fiber Metal Laminates
Authors:   University of Bremen, Patel Umesh ,   Faserinstitut Bremen (FIBRE), Polle Christoph ,   Faserinstitut Bremen (FIBRE), Koerdt Michael ,   Faserinstitut Bremen (FIBRE), Koerdt Maximilian ,   Faserinstitut Bremen (FIBRE), Herrmann Axel S. ,   University of Bremen, Lüssem Björn
Published: June 2024

This study investigates the impact of metal volume fraction (MVF) on the propagation of guided ultrasonic waves (GUW) in fiber metal laminates (FML). GUW interactions with various metal and prepreg layers are influenced by changes in the FML composition. A crucial parameter for FML characterization is the metal volume fraction (MVF), representing the volume of metal to prepreg. This research assumes that variations in MVF affect GUW propagation in FMLs.
The study analyzes the influence of MVF on GUW propagation in FML, specifically comparing GUW propagation in GLARE plates with nearly identical geometry but different MVF and layer structures. Initially, simulations were conducted to predict expected changes in dispersion properties. To validate these predictions, pitch-catch measurements were performed on the panels, measuring three transducer paths for each panel to enhance measurement significance. An intensive study of wave modes was undertaken to investigate the influence of MVF on mode velocities.
The simulation results were validated through experiments, highlighting the significant impact of MVF on GUW propagation in FML. Interestingly, in FMLs with varying MVFs and layer structures, the structural conditions of the layers (such as fiber orientation) demonstrated a non-negligible influence on GUW propagation, surpassing the influence of MVF alone.
A Laboratory test aimed at demostrating the potential of an Interferometric Radar to estimate the Seismic Risk of damaged buildings
Authors:   Centre Tecnològic de Telecomunicacions de Catalunya, Luzi Guido ,   Centre Tecnològic de Telecomunicacions de Catalunya, Palamà Riccardo ,   Universitat Politècnica de Catalunya - BarcelonaTech, Gonzalez-Drigo Ramón ,   Universitat Politècnica de Catalunya - BarcelonaTech, Pujades Luis G. ,   Universitat Politècnica de Catalunya - BarcelonaTech, Caselles Oriol ,   Universitat Politècnica de Catalunya - BarcelonaTech, Vargas-Alzate Yeudy F.
Published: June 2024

In the last decade, the use of interferometric Real Aperture Radar (RAR) to perform the survey of static and dynamical performance of civil structures, and estimate their vibration state and modal behaviour, for bridge monitoring, consolidated. In the case of urban buildings, due to the small amplitude of vibration of these structures, and the higher complexity of the radar survey, a few case studies have been published. Nonetheless, recent research [1] focused on the potential of measuring the frequency of vibration of a building after a seismic event to estimate the occurred damage using this non-invasive and remote sensing technique. The study here described reports the results of an experiment carried out to investigate the potential of this application through a laboratory setup. A framed metal structure, composed of bars fasten by bolts, representing a scaled reproduction of a civil building, is monitored using a portable K band (24 GHz) radar interferometer. The instrument is a prototype developed at CTTC which provides improved range resolution and displacement accuracy, with respect to the state of the art of commercial instruments. In the experiment, the structure is monitored by the radar step by step while its stiffness is decreased by loosening, in different points, the bolts constraining the structure. This procedure aim simulating an increasing damage. Simultaneously to the radar acquisitions, data from a setup of accelerometers mounted on the framed structure are also acquired. Despite the high environmental noise (radar clutter) caused by multiple reflections generated by the neighbour walls and furniture elements internal to the laboratory, the spectral analysis of the displacement samples retrieved from radar acquisitions and from the accelerometers confirm the expected behaviour.

[1] Gonzalez-Drigo, R. et al., Assessment of Post-Earthquake Damaged Building with Interferometric Real Aperture Radar. Remote Sens. 2019, 11, 2830. https://doi.org/10.3390/rs11232830
Research on Long-span Bridge Point Cloud Morphology Identification
Authors:   Southeast University, Zhu Yanjie ,   Southeast University, Li Muxuan ,   Southeast University, Xiong Wen
Published: June 2024

With the rapid development of 3D laser scanning technology and the trend of accurate and convenient acquisition of 3D point cloud data, the 3D point cloud data that was challenging to acquire in the past has also become one of the main ways to represent the 3D form of bridges. Among several bridge types, suspension bridges have high research value due to their widespread use in long-span bridges. The main cable, as the main load-bearing member of the suspension bridge, exhibits characteristics such as a large span, heavy components, and numerous influencing factors in long-span bridges. Thus, controlling the formation of the main cables is a problem frequently encountered in bridge engineering. Using 3D point cloud scanning data, this paper constructs an overall 3D point cloud model of the bridge and proposes an automatic extraction algorithm for bridge components based on this model. Subsequently, the paper illustrates the integration of the algorithm analysis and control of user interaction platform building, aiming to build up long span bridge digital base, therefore realizing variation recognition and digital tracking of long bridge space form.
Risk and Value Informed Structural Health Monitoring System Design for the Miter Gates
Authors:   University of California, San Diego (UCSD), Chadha Mayank ,   University of Michigan-Dearborn, Hu Zhen ,   Los Alamos National Laboratory, Farrar Charles R. ,   University of California, San Diego (UCSD), Todd Michael D.
Published: June 2024

The ultimate goal of a Structural Health Monitoring and Prognostics system (SHM/DP) is to interrogate the data gathered in situ to infer the state of the structure (diagnostics), evaluate the system's evolution over time (prognos-tics), and leverage the diagnostic and prognostic results to facilitate decision-making. As a compelling business proposition, SHM/DP is expected to deliv-er economic returns through data-informed lifecycle management decisions, surpassing the estimated costs encompassing the design, initial deployment operations, and maintenance of the SHM/DP system. Therefore, it is prudent to approach a system design problem with a cost-benefit analysis perspective, aiming to achieve a design that maximizes the Value of Information (VoI) ac-quired by an information-acquisition system, evaluated in a pre-posterior sense. In this paper, our focus is on a sensor optimization framework that aims to maximize the VoI over the lifecycle of a miter gate structure. We em-ploy the proposed Bayesian sensor optimization framework to assess the ef-fectiveness of various Value of Information (VoI) metrics in quantifying the overall benefits of an SHM/DP system. Additionally, we explore the impact of the decision maker's risk profile and the adopted decision policies on the system design.
PIEZORESISTIVE SENSORS BASED ON PVDF-HFP REINFORCED WITH GNPs FOR HEALTH MONITORING APPLICATIONS
Authors:   University of Rey Juan Carlos (URJC), Díaz Víctor ,   University of Rey Juan Carlos (URJC), FERNÁNDEZ Xoan Xosé ,   University of Rey Juan Carlos (URJC), Sánchez Mario ,   University of Rey Juan Carlos (URJC), Ureña fernández Alejandro
Published: June 2024

Wearable technology has become increasingly popular in recent years. Wearable sensors are systems made of flexible substrates which good adaptability to human motion monitoring. In parituclar, they allow doctors to obtain real-time data from patients by detecting small changes in baseline values over time, such as the movement of various joints or muscles, heart activity, breathing, and even sweat.
In this regard, polymers can offer good flexibility, stretchability, and long-term reliability, which, together with the possibility of modifying their electrical response with conductive nanofillers, makes them promising for the fabrication of this kind of sensors, as their electrical conductivity changes with applied strain.
In this work, piezoresistive sensors based on poly(vinylidene fluoride-hexafluoropropylene) (PVDF-HFP) reinforced with graphene nanoplatelets via solvent casting are presented. Here, both the electrical and electromechanical responses are evaluated as a function of different processing conditions, more specifically, sonication time and evaporation temperature. Then, several successful proof-of-concept tests have been carried out from human motion monitoting to pressure sensing applications, with very high sensitivities, being very promising for this kind of applications.
Blockchain-enabled Data Mining for Pre-Condition Monitoring in Railway Infrastructure
Authors:   University College Dublin (UCD), Gordan Meisam ,   University College Dublin (UCD), Ghiasi Ramin ,   University of Porto, Mosleh Araliya ,   School of Engineering, Polytechnic of Porto (ISEP), Ribeiro Diogo ,   University College Dublin (UCD), Malekjafarian Abdollah
Published: June 2024

Early detection of abnormalities in railway systems is crucial for risk mitigation and cost-effective maintenance. Damaged wheels in freight trains cause a significant threat to transport infrastructure integrity, particularly when wheel flats occur. Monitoring wheel conditions relies on sensing mechanisms, requiring robust data integrity checks. This paper proposes a data-driven approach utilising blockchain for pre-condition monitoring to enhance data reliability. To achieve this goal, a data mining procedure is developed to establish a local blockchain. The blockchain continuously expands with new blocks, with each block linked to the previous one via a hash function. Only validated data are recorded in order to enhance the reliability of train measurements.
Guided Wave Behavior in Type IV Composite Overwrapped Pressure Vessels Under Fatigue Loading
Authors:   BAM Federal Institute for Materials Research and Testing, Hashemi Seyedreza ,   BAM Federal Institute for Materials Research and Testing, Heimann Jan ,   BAM Federal Institute for Materials Research and Testing, Charmi Amir ,   BAM Federal Institute for Materials Research and Testing, Prager Jens
Published: June 2024

Innovative Ultrasonic Guided Wave Approach for Structural Health Monitoring of Type IV Composite Overwrapped Pressure Vessels

Seyedreza Hashemi, Jan Heimann, Amir Charmi, Eric Duffner, Jens Prager

Abstract:
The digitalization of quality control processes and the use of digital data infrastructures is a novel idea that can be applied for ensuring the operational safety and reliability of pressure vessels, particularly in the context of hydrogen storage at high pressure. Despite the critical role these pressure vessels play, current safety regulations lack an established concept for Structural Health Monitoring (SHM). This research addresses this gap by presenting a study on the application of ultrasonic guided waves (GWs) for SHM of Type IV Composite Overwrapped Pressure Vessels (COPVs).
The study focuses on the development of a reliable measurement system to transition from conventional periodic inspections to SHM and predictive maintenance, prolonging the remaining lifetime of the vessels. A sensor network is employed, consisting of fifteen piezoelectric wafers arranged in three rings, which are mounted on the outer surface of the COPV.
Deploying GWs, known for their long-distance propagation and ability to cover complex structures, the study explores GW behavior under different environmental and operational conditions, including periodic pressure fluctuations and temperature loadings.
Meticulous analysis of GW signals by utilizing various features and damage indices, underscores their suitability for an effective SHM under realistic working conditions. The project aims to localize defects by considering temperature, and internal pressure. Mimicking the continuous monitoring of Type IV COPVs in H2 refueling gas stations under authentic operational conditions, the COPV underwent thousands of pressure load cycles in our special test facility . The implemented methodology facilitates early damage detection, showcasing the efficacy of the designed method in effective safety assurance.
Innovative machine learning based approach for reliable and accurate measurement of guide roll alignment in continuous casting plants
Authors:   Cologne University of Applied Sciences, Rosenthal Robert ,   Cologne University of Applied Sciences, Gerz Fabian ,   Cologne University of Applied Sciences, Al-Shrouf Loui ,   Cologne University of Applied Sciences, Jelali Mohieddine
Published: June 2024

Continuous casting (strand casting) is the leading-edge technology in modern steel production. Even slight improvements of the casting process offer a range of benefits, such as improved quality, higher productivity, energy savings and a reduced environmental impact. The precise alignment of a plants guiding rolls is a crucial factor for its performance.
Strand condition monitoring systems usually incorporate modules to gauge the alignment of the guiding rolls. These modules measure the angle formed by two successive rolls by using metal rulers fitted with inclination sensors. However, the angle of the ruler only represents a valid measurement where it maintains firm contact with two successive rolls. Most of these systems rely on inductive switches that trigger a measurement at these locations. Yet this approach provides unreliable results due to the trigger’s insufficient accuracy. Furthermore, this method stores only one data point per roll, which is problematic due to significant measurement noise. Apparently, a reliable and accurate alignment measurement remains a significant challenge.
As part of an ongoing research project, this paper presents the development of a novel alignment measurement module. The module will provide continuous readings from the inclination sensors without the need for a trigger. In addition, an innovative machine learning based approach to distinguish between valid and invalid measurements is developed. This method enables the averaging of multiple measurements per roll, drastically reducing the impact of measurement noise while providing reliable alignment angles. To overcome the limited availability of real-world data, which is insufficient for model training, the measurement module is simulated to generate artificial training data. Our machine learning model achieves excellent performance on this artificially generated dataset. The paper concludes by outlining how the new approach will be applied to real-world data generated by the new module
Data-driven Parameterizable Generative Adversarial Networks for Synthetic Data Augmentation of Guided Ultrasonic Wave Sensor Signals
Authors:   University of Bremen, Bosse Stefan
Published: June 2024

Detecting and characterizing hidden damages in composite materials like Fibre-Metal Laminates (FML) remains a challenge. Guided Ultrasonic Waves (GUW) or X-ray imaging are commonly used to detect these damages, but their interpretation remains limited. Data-driven predictor models can detect damages in structures using GUW time-dependent signals, but the training data lacks variance, quality, and sufficient coverage of the hyper-parameter space. Synthetic data augmentation is often the only solution to create robust and generalized damage predictor models. Synthetic sensor data can be generated using model-based, model-assisted, or model-free methods. However, computed GUW signals show poor reality conformance due to too much constraints and simplifications. Recent developments in Generative Adversarial (Neural) Networks (GAN), commonly driven by a random generation process [1], include deterministic style vectors to generate specific signal data, determining constraints such as damage size, position, transducer positions, material and environmental properties. These new architectures aim to reduce the impact of environmental changes on data-driven damage predictor models by using parameterizable synthetic data generation. We will elaborate these new architectures and discuss the possibilities and challenges of using such GAN models for data generation of US signals in GUW-based SHM applications, as originally proposed in [2]. We will have a focus on low-quality real measuring data used for training and composite materials (e.g., FML) with hidden damages, and show some preliminary results.

[1] Karras et al. "A style-based generator architecture for generative adversarial networks." Proceedings of the IEEE/CVF 2019
[2] Virupakshappa et al. "Using Generative Adversarial Networks to Generate Ultrasonic Signals." 2020 IEEE International Ultrasonics Symposium (IUS). IEEE, 2020
“Why Should I Trust You?”: Exploring Interpretability in Machine Learning Approaches for Indirect SHM
Authors:   Aalto University, Lan Yifu ,   Aalto University, Li Zhenkun ,   Aalto University, Lin Weiwei
Published: June 2024

Currently, machine learning (ML) methods are widely adopted in structural health monitoring (SHM), yet they are still mostly black boxes. On the other hand, given the significant responsibility associated with SHM, understanding the rationale behind it is critically important. In some cases, even experienced experts have difficulties finding evidence related to structural integrity within complex structural signals. Thus, solely relying on these black-box SHM systems carries inherent risks. Trustworthiness is the key for decision-makers when planning to act on predictions or deciding whether to deploy a new model. This understanding can also offer insights about the models, transforming untrustworthy models or predictions into reliable ones. The indirect SHM method using passing vehicles, an emerging technique in the past two decades, offers a rapid and cost-effective solution for bridge monitoring. Its signal components are affected by factors such as vehicle dynamics and road roughness, making them more complex than those in the direct method. Although ML methods have shown promising results in this domain, their results require further explanation. In this work, SHAP (SHapley Additive exPlanation) tools are utilized to interpret the result prediction of ML methods in indirect SHM. The trustworthiness of models is demonstrated through simulation databases: deciding whether a prediction should be trusted, choosing between models, and determining why a classifier should not be trusted.
A three-year project on Structural Health Monitoring of railway bridges: main results and lessons learnt
Authors:   Politecnico di Milano, Benedetti Lorenzo ,   Politecnico di Milano, Argentino Antonio ,   Politecnico di Milano, Bernardini Lorenzo ,   Politecnico di Milano, Radicioni Luca ,   Politecnico di Milano, Bono Francesco Morgan ,   Politecnico di Milano, Somaschini Claudio ,   Politecnico di Milano, Cazzulani Gabriele ,   Politecnico di Milano, Belloli Marco
Published: June 2024

Bridges and viaducts worldwide are threatened by ageing, increasing loads and climate-related extreme events. The situation calls for immediate actions to prevent catastrophic failures and extend the life of our infrastructural heritage. In this regard, thanks to significant efforts from academics, Structural Health Monitoring is paving its way towards application in the real world. Unlike the simple monitoring activity, which is not new in the field, SHM is more identifiable as a paradigm, encompassing several steps and involving many stakeholders. Also, the focus is not on a project level but on the network as a whole. Such a new perspective offers the opportunity – and obliges at the same time – to look for synergies among different bridges, with the aim of designing standardized guidelines to foster SHM scalability. Embracing this vision, in 2020, RFI and the Mechanical Engineering Department of Politecnico di Milano gave birth to a collaboration oriented to testing the application of Structural Health Monitoring on the Italian railway infrastructure. The project had a twofold objective: on the one hand, the realisation of an automated software to extract and deliver information from the monitoring systems installed on three pilot bridges; on the other hand, the draft of guidelines that could report the lessons learnt and generalise as much as possible the design and exploitation of SHM on railway bridges. Given the interest in the feasibility of an extensive adoption of such practice, the pilot bridges were selected to be representative of the whole network. This paper wraps up the three-year project, intending to share the experience gained in the field and the take-home messages for future applications of SHM, with a focus on the key aspects for a broad spectrum adoption of this paradigm.
Assessment of the dynamic parameters of a 673-meter cable-stayed bridge with wireless three-component MEMS-based sensors
Authors:   Sercel, Perrault Matthieu ,   Geocomp, Inc., Celik Ozan ,   Sercel, Guerineau Laurent
Published: June 2024

Dynamic structural monitoring of bridges under ambient vibrations is a method to assess dynamic parameters of structures with low intrusiveness, e.g. with Operational Modal Analysis (OMA) data processing. Yet, several barriers hinder its adoption for real world applications, such as a lack of adequate sensors or industrial sensor networks and more generally, a lack of integrated solutions. In this paper, we present a data collection campaign using a novel wireless sensing system for dynamic structural monitoring of civil engineering structures.
The core enabler of this SHM product named S-lynks is the use of a novel ultra-low noise seismic MEMS accelerometer, QuietSeisTM, which makes possible to envision a true industrial wireless sensor network for OMA in civil engineering structures. We developed a bespoke wireless sensing system composed of Wireless Sensor Nodes (WSN) and Gateways (WSG) and combining Low Power Wide Area Network (LPWAN) and Wireless Local Area Network (WLAN) technologies to manage the WSN wake up, synchronization and data transmission to WSG.
This product has been used on a 675-meter cable-stayed bridge to assess the dynamic parameters from ambient vibrations measurements. We successfully triggered 120 measure points on the deck and inside a tower in less than 10 hours. More than 20 modes are identified between 0 and 5 Hz, with well-defined mode shapes. Extra measurements on a few cables to identify the tension allow explaining the asymmetry observed in some mode shapes.