bluuebunny/crossref_metadata_2025 huggingface dataset:
Analyzing Vertical Earthquake Vibrations and Moving Vehicle Loads for Structural Health Monitoring and Vibration Suppression in Bridges
Authors:   BAM Federal Institute for Materials Research and Testing, Bahrami Eskandari Ardalan ,   The University of Science and Culture, Nikkhoo Ali ,   Sheffield University, Hajirasouliha Iman
Published: June 2024

The design of bridges often overlooks the vertical component of earthquakes or considers it of secondary importance, despite compelling evidence indicating specific structural damage caused by primary earthquake waves. Conversely, during the operational phase, the combined influence of ground motion and moving loads from vehicles can significantly impact the structural health monitoring (SHM) of bridges.
This study aims to evaluate the simultaneous effect of vertical earthquake vibrations and moving vehicle loads on simply supported bridges. The research employs a practical methodology based on the eigenfunction expansion method to analyze change of deflection due to the effect of these concurrent forces under seven different earthquake records. It is shown that within a realistic range of vehicle mass and velocity, the average of changing the maximum deflection at the mid-span of the main beam (denoted as M_n) reaches up to 163% under various scenarios.
Subsequently, the seismic parameters influencing this phenomenon are identified through a statistical analysis of set of 100 different earthquake records with unique features. A linear regression equation is presented to predict the M_n based on the earthquake specific properties.
Additionally, to control the vertical vibration of bridge systems, a novel vibration suppression system utilizing steel pipe dampers is introduced, and its reliability is examined across a broad spectrum of bridge flexural rigidity. The results indicate that the system's efficiency depends on M_n and the soil type of the bridge construction, enabling a reduction in structural sections (up to 27%) while achieving the same maximum target deflection in the initial state. This efficiency leads to a more economical design solution, emphasizing the potential benefits of the proposed system for practical application.
Detailed analysis based on vertical and longitudinal deformations estimated from InSAR displacement by data assimilation
Authors:   NEC Corporation, Yano Yukihiro ,   NEC Corporation, Kinoshita Kosuke ,   NEC Corporation, Kumura Takahiro
Published: June 2024

Application of a SAR satellite to Structural Health Monitoring (SHM) has attracted much attention due to its high efficiency. SHM using a SAR satellite doesn’t require installation of sensors and has the capability of monitoring wide area. However, the displacement obtained by a single SAR satellite is line-of-sight displacement which is not either vertical or longitudinal displacement. Thus, it is not easy to interpret line-of-sight displacement.
This paper aims to solve this problem. The objective of this paper is to estimate longitudinal and vertical displacement from line-of-sight displacement and to analyze in detail a fallen water pipe bridge. For estimation of longitudinal and vertical displacement, data assimilation is employed.
As a result of data assimilation, successfully observed line-of-sight displacement is reproduced. Negative correlations between displacements at piers and temperature are observed. A year prior to the bridge collapse, anomalous displacement pattern is observed at the centre of the span. In addition, deformation caused by damage is observed.
Cumulative second harmonic Lamb wave generation and propagation in composite laminates: an insight into the influence of material anisotropy and damping
Authors:   Tongji University, Shan Shengbo ,   The Hong Kong Polytechnic University, Zhang Yuanman ,   The Hong Kong Polytechnic University, Song Yang ,   The Hong Kong Polytechnic University, Liu Ze ,   The Hong Kong Polytechnic University, Cheng Li
Published: June 2024

Significant strides have been taken in the structural design of fibrous composites in recent decades. Early detection of material degradation and damage in such materials is crucial for various industrial applications, yet poses challenges. Second harmonic Lamb waves have demonstrated their superior sensitivity to microstructural defects, making them an effective tool for structural health monitoring (SHM). However, understanding of the second harmonic Lamb wave generation and propagation in composite laminates remains inadequate due to the complexities introduced by material anisotropy and damping, which impede the practicality of SHM applications. This study investigates the cumulative second harmonic Lamb waves in composite laminates at low frequencies, considering both material anisotropy and damping. Theoretical analyses were carried out to explore how anisotropy and damping affect the generation and propagation of second harmonic Lamb waves. A nonlinear material constitutive model was proposed. It was used to conduct numerical simulations that verified the properties of second harmonic Lamb wave generation and propagation at different wave propagation angles, with a focus on the cumulative effect. Experiments were carried out to further validate the properties of the second harmonic Lamb waves in a carbon fiber reinforced panel. The findings indicate that the Lamb waves propagating at 0° showcase exceptional cumulative effect, rendering them suitable for SHM applications. Additionally, material damping induces a cumulative second harmonic Lamb wave with an initial increase followed by a decrease, creating a predictable “sweet” zone of larger amplitude that is easy to measure for SHM applications. The elucidation of the generation and propagation characteristics of the second harmonic Lamb waves provides guidance for structural damage detection and monitoring applications.
Comparison of anomalous displacement detectability of bridges between X and C band InSAR data: Case study on a collapse of water pipe bridge
Authors:   NEC Corporation, Kinoshita Kosuke ,   NEC Corporation, Yano Yukihiro ,   NEC Corporation, Kumura Takahiro
Published: June 2024

This paper presents a feasibility of detecting anomalous displacements on bridges where collapses have occurred using C-band satellite synthetic aperture radar (SAR). Recently, the number of aging bridges due to damage and corrosion has been increased in many countries. Although there are various maintenance and management systems, the authors have focused on satellite SAR, one of the remote sensing technologies, to establish a wide-area multiple bridge monitoring technology. In a previous study, the authors analyzed high-resolution X-band interferometric SAR (InSAR) data acquired by the Italian satellite COSMO-SkyMed during the two years before the accident for the MUSOTA water pipe bridge in Japan, which collapsed on October 2021. As a result, it was shown that there was an anomalous displacement about one year before the accident, which seemed to be a sign of the collapse. On the other hand, the utilization of Sentinel-1 (C-band) data, which has lower resolution than X-band data, but is freely available, covers a wide area, and has dense time-series SAR images, would be beneficial for the practical application of this technology. In this study, InSAR data acquired by Sentinel-1 for the MUSOTA water pipe bridge from 2016 to 2022 are analyzed. Then, we compare the line-of-sight (LOS) displacement anomaly before the collapse with the results analyzed by the X-band satellite. As a result of statistical analysis of the displacement between the collapsed span and the adjacent spans with similar structural type assumed to be intact, it is shown that Sentinel-1 also captured differences which seemed to be the signs of the collapse about one year before the accident. This result supports the analysis of the X-band satellite data and shows potential for utilization in bridge anomaly detection systems, even for low-resolution C-band data.
SHM System Architecture based on Ultrasonic Guided Waves for Aircraft Application Scenarios
Authors:   Deutsches Zentrum für Luft- und Raumfahrt (DLR), German Aerospace Center, Schmidt Daniel ,   Deutsches Zentrum für Luft- und Raumfahrt (DLR), German Aerospace Center, Moix-Bonet Maria ,   Deutsches Zentrum für Luft- und Raumfahrt (DLR), German Aerospace Center, Petersen Jan ,   Deutsches Zentrum für Luft- und Raumfahrt (DLR), German Aerospace Center, Wierach Peter
Published: June 2024

Structural health monitoring (SHM) based on ultrasonic guided waves is a novel technology using permanently attached actuator and sensor networks, data acquisition and data evaluation systems to enable in-service inspection of aircraft structures.
A modular and decentralized SHM system is presented that can be adapted to various aircraft application scenarios. The central element of the SHM system is a robust sensor array based on piezocomposite technology that can be efficiently integrated into the manufacturing process of CFRP structures (via co-bonding). The sensor array consists of integrated piezoceramic transducers, temperature sensors, electrical cables and integrated electronic components. The decentralized system architecture of the individual electronic components reduces the installation and cabling effort during the assembly of aircraft structures as well as the system weight. Integration and installation aspects as well as various system architectures are discussed using an Airbus A350 door surround structure. Furthermore, an outlook of damage detection under representative flight loads is given.
Determination of dispersion curves of higher order guided ultrasonic waves based on experimental data by a 2D-DFT
Authors:   Helmut-Schmidt-University / University of the Federal Armed Forces Hamburg, Barth Tilmann ,   Helmut-Schmidt-University / University of the Federal Armed Forces Hamburg, Rauter Natalie
Published: June 2024

Guided ultrasonic wave based structural health monitoring has been subject to research over the last decades. In order to analyze the wave propagation data correctly, knowledge about the fundamental wave propagation properties is indispensable. Due to the multimodal and dispersive nature of guided ultrasonic waves these properties are characterized by dispersion diagrams.

In previous work by the authors [1] the dispersion behavior of the guided ultrasonic waves is extracted by an adapted non-uniform 2D-DFT algorithm based on [2]. With this algorithm the dispersion curves can be computed from experimental data in an accurate and highly automated fashion. So far the application is limited to the fundamental wave modes over large frequency ranges. In this work the approach is extended to higher-order wave modes, which are of special interest in particular for the

nonlinear wave propagation.

To analyze the robustness of the presented data acquisition algorithm the procedure is applied first to isotropic waveguides and second to more complex material systems like fiber metal laminates. The results indicate that the presented approach enables one to derive the dispersion curves over large frequency ranges from experimental data not only for the fundamental A0 and S0 wave modes but also for higher-order modes.

References

[1] Barth T, Rauter N and Lammering R. Experimental determination of Lamb wave dispersion diagrams using 2d fourier transform and laser vibrometry. Preprint at https://wwwresearchsquarecom/article/rs-1321459/v1 2022; 10.21203/rs.3.rs-1321459/v1.

[2] Alleyne DN. The nondestructive testing of plates using ultrasonic Lamb waves. PhD Thesis, Imperial College of Science, Technology and Medicine, London, 1991.
Effect of moisture on the sensing capabilities of piezoelectric actuator/sensor pairs on flax fibre reinforced composite laminates
Authors:   University of Patras, Tsitsas Panagiotis ,   NCSR , Demokritos, Rekatsinas Christoforos ,   University of Portsmouth, Barouni Antigoni ,   University of Patras, Chrysochoidis Nikolaos
Published: June 2024

The sensitivity of the piezoelectric actuators’ response to the moisture uptake has been investigated on flax fibre reinforced composite laminates. Non-woven flax fabric was impregnated with epoxy and composite laminates were fabricated using hand lay-up and cured in a compression moulding machine. The laminates were initially impacted with a drop-weight impact machine at a low energy level, with the aim to generate an artificial surface crack that will allow the moisture to enter the fibre-matrix interface more effectively. A set of piezoelectric actuators and sensors were adhered on the surfaces of the composite panels in a specific rectangular arrangement to allow for wave capturing at various directions. The wave characteristics of the samples were extracted in pristine stage and then the samples were impacted and immersed in distilled water until saturation. At regular intervals after the water immersion, the panels’ weight was measured using a laboratory scale and the wave propagation characteristics of the materials ware measured in order to investigate the gradual effect of moisture on the capabilities of the actuators and sensors. The propagation of stress waves in the dry and wet samples was investigated and the effect of moisture absorption was examined experimentally using piezoelectric wafers until the saturation was reached. Finally, a continuum damage mechanics layerwise time domain spectral finite element model, both for the composite laminate and the rich resin layers, was also developed to simulate the materials’ performance under impact and evaluate the wave propagation within the material at the dry state.
Crowdsensing-based automatic bridge health condition assessment using drive-by measurements and deep learning
Authors:   Aalto University, Li Zhenkun ,   Aalto University, Lan Yifu ,   Aalto University, Lin Weiwei
Published: June 2024

In recent decades, assessing the structural health conditions of aging bridges has emerged as a significant concern. A recent drive-by measurement method has attracted substantial attention, in which only several sensors are installed on crowdsensing vehicles rather than bridges, providing a more economical and convenient solution. This paper proposes an automatic bridge condition assessment framework incorporating drive-by measurements and deep learning techniques. The methodology involves collecting and segmenting accelerations from a vehicle passing a healthy bridge into short-time overlapped frames. Over multiple vehicular passes, all frames are then transformed into frequency-domain responses, forming the input for training an unsupervised deep learning model. The model is then trained to reconstruct the input using these frequency-domain responses. In assessing the bridge with an unknown health state, the trained model is employed to reconstruct the passing vehicle’s new short-time frames, and the response construction error automatically determines the bridge’s health condition. Experimental validation utilizing a laboratory bridge and scaled truck demonstrated that the trained model could consistently identify a healthy bridge during passages, with larger reconstruction errors indicating that the bridge was damaged. The innovative framework showcased promise for efficient and reliable bridge health condition assessment.
3D Vibration Analysis using Phase-Based Displacement Estimation and Stereo Cameras
Authors:   Chonnam National University, Lee Seunghwan ,   Chonnam National University, Kong Yeseul ,   Chonnam National University, Park Gyuhae
Published: June 2024

Non-contact sensors are increasingly integral to the field of Structural Health Monitoring (SHM), with camera being widely utilized due to their high spatial resolution and cost-effectiveness in measuring structural displacements. Phase-based optical flow (POF) techniques have proven valuable for accurate and computationally efficient 2D vibration measurements. However, these techniques are typically confined to single-camera systems and encounter challenges in measuring vibrations in out-of-plane directions.
Previous research on 3D vibration measurement has predominantly centered on pixel intensity-based techniques using stereo camera systems. Although these methods demonstrate high performance in calculating global strains and stresses, they are less suitable for accurate vibration measurement. To address these limitations, this study proposes a novel 3D vibration measurement approach that integrates POF within the context of stereo camera systems.
2D vibrations are extracted by applying Complex Gabor filters to stereo images, and subsequently, 3D vibration measurements are estimated through camera calibration and triangulation, using computed camera internal and external parameters. To validate the method, modal analysis experiments are conducted on an aluminum plate. The 3D vibration measurement results are compared with those obtained through 3D Digital Image Correlation (DIC) and contact sensors and the accuracy is demonstrated.
Intelligent automatic operational modal analysis
Authors:   Politecnico di Torino, Rosso Marco Martino ,   University of L'Aquila, Aloisio Angelo ,   Politecnico di Torino, Marano Giuseppe Carlo ,   Sapienza University of Rome, Quaranta Giuseppe
Published: June 2024

This work presents a novel methodology for the automatic output-only modal identification of linear structures under ambient vibrations named Intelligent Automatic Operational Modal Analysis (i-AOMA). The proposed methodology leverages the Covariance-based Stochastic Subspace (SSI-cov) algorithm for output-only modal parameter identification and encompasses two key phases. In the first phase, the SSI-cov algorithm is executed using quasi-randomly samples of the control parameters. The corresponding stabilization diagrams are next processed via Kernel Density Estimation in order to prepare a training database for the i-AOMA's intelligent core. This is a machine learning technique (namely a Random Forest algorithm) that predicts the optimal combinations of the control parameters involved in the SSI-cov algorithm. The second phase of i-AOMA rests on the iterative generation of quasi-random control parameter samples. If a sample is classified as feasible by the intelligent core of i-AOMA, then the SSI-cov algorithm is applied, while a new sample is considered otherwise. Such iterative procedure stops once a statistical convergence criterion is fulfilled. Hence, ultimate modal estimates are carried out from the stabilization diagrams, and relevant statistics are computed to quantify the effects of the uncertainties attributable to the variability of the control parameters. The entire Python source code for i-AOMA is made freely available through public web repositories. Some applications of the proposed methodology are finally illustrated.
Advanced Parallel Laser Line-Camera System for Automatic and Precise Crack Width Measurement
Authors:   The University of Hong Kong (HKU), Li Chaobin ,   The University of Hong Kong (HKU), Su R.K.L
Published: June 2024

Accurate quantification of cracks is crucial in evaluating building conditions. However, current non-contact measurement technologies encounter challenges in measuring crack widths, particularly in hard-to-reach areas. This paper presents a novel portable parallel laser line-camera system that is specifically designed for automatic and precise measurement of crack widths from different angles and over long distances. The system is composed of a digital camera, parallel line laser diodes, and rigid positioning arms, forming a triangulation-based configuration. A newly developed image processing algorithm is at the core of this innovation, allowing for the extraction of accurate pixel scale information from laser strips, thus overcoming the challenge of pixel distortion in non-perpendicular photography. The U-Net algorithm is utilized to efficiently identify crack regions, followed by the Canny algorithm for meticulous extraction of crack features. By utilizing the pixel scale data obtained from the laser strips, the actual width of cracks can be accurately determined. The system's efficacy and precision have been validated through laboratory and field tests. This automatic, precise, and portable solution significantly advances the field of on-site crack measurement, offering substantial potential for practical applications in building condition assessments.
An Exploration of Leveraging CVM Sensors to Monitor Structures Across Various Industries - Part 1: Mining, Infrastructure and Marine
Authors:   TESTIA GmbH, Haridas Aswin ,   Anodyne Electronics Manufacturing Corporation (AEM), Roach Dennis ,   Anodyne Electronics Manufacturing Corporation (AEM), Lynch-Staunton Trevor ,   TESTIA GmbH, Speckmann Holger
Published: June 2024

Over the past decade, there has been a growing interest in exploring new tools and methodologies to improve safety and concurrently reduce maintenance and repair costs. Nondestructive Testing (NDT) methods have been instrumental in inspecting structural conditions, but the increasing demand for continuous Structural Health Monitoring (SHM) has led to the adoption of technologies such as Comparative Vacuum Monitoring (CVM). As an FAA-certified, nondestructive, failsafe SHM system, CVM utilizes pneumatic sensor technology to efficiently monitor the onset and propagation of structural cracks in real-time, facilitating condition-based maintenance. When coupled with remote interrogation, CVM can overcome a myriad of inspection impediments stemming from accessibility limitations, complex geometries, the location of hidden damage, and the isolated location of the structure. Although validation and certification programs have established the viability of these sensors for monitoring diverse structures such as bridges, buildings, aircraft, ships, wind turbines, railroad cars and mining equipment, a compilation of capabilities addressing specific industry pain-points remains unrealized. This study focuses on the capabilities of CVM, evaluating their applications in addressing industrial challenges. The technology's versatility is assessed through the cross-industry innovation strategy of Testia, examining its efficacy in diverse sectors. The paper emphasizes the practical implementation of CVM for automated damage detection, aiming to drive optimized and timely maintenance decisions. Furthermore, this paper explores the implementation of a distributed CVM sensor network and its impact on solving industrial challenges. These findings empower end-users to evaluate economic and technical benefits, fostering opportunities for widespread technology implementation. By enabling quick and remote sensor data gathering, CVM systems can revolutionize maintenance practices and enable proactive, predictive and optimized monitoring schemes. The application of SHM technologies in sustaining critical structures has a potential to significantly reduce maintenance costs, minimize repairs through early damage detection, address uncertainties in structural aging and prevent premature failures.
On the fusion between interferometric satellite data and in situ measurements for the monitoring of built heritage
Authors:   Politecnico di Torino, Coccimiglio Stefania ,   Politecnico di Torino, Miraglia Gaetano ,   Politecnico di Torino, Ceravolo Rosario
Published: June 2024

Over the past decades, earthquakes, and other catastrophic events has highlighted the increasingly growing vulnerability of infrastructures, buildings, and architectural heritage structures. In addition to catastrophic events, often related to climate change, another fundamental aspect is represented by the lack of maintenance and monitoring of the asset. In the field of preservation of buildings and infrastructures, Structural Health Monitoring techniques can effectively contribute to the assessment of the health state of structures.

In the case of structural monitoring using data-driven approaches, since the information depends on the type of data used, a great resource for knowing as much as possible about the structure is represented by the combination of multiple data. Thanks to the integration of different types of data, it is possible to know the conditions of the structure in more detail as different aspects can be combined.

In this context, together with the data obtained from in situ monitoring systems, data obtained remotely, i.e. satellite data, can be used. There are different types of satellite data that can be used to study different aspects, e.g., multispectral data, hyperspectral data, and interferometric data, and they can make different contribution to civil engineering. In this paper, the exploitability of open-source interferometric data is investigated and they are studied for the purpose of their integration with data obtained from a monitoring system installed on a monumental building, in order to monitor and observe structural health.
Case Study: Lightweight and Emergency Monitoring of a Post-disaster Bridge
Authors:   Southeast University, Sun Luyao ,   Southeast University, Xiong Wen ,   Southeast University, Zhu Yanjie
Published: June 2024

In the realm of civil engineering, the health and integrity of infrastructural facilities are of paramount importance. Particularly in the aftermath of disasters, prompt and accurate structural assessments are crucial to ascertain the safety and functionality of structures. To this end, there is a pressing need for the design of such systems that can be rapidly deployed and provide accurate and reliable data in emergency situations. This paper presents a novel lightweight emergency monitoring system (LEMS) specifically tailored for a continuous rigid bridge in Anhui Province that had previously caught a fire, aimed at facilitating prompt post-disaster structural evaluations. We primarily aim to outline the design principles for such lightweight emergency monitoring system intended for post-disaster evaluations and to report the monitoring conditions and results gathered from years of data collection. Through the analysis of collected data, several significant insights have been derived, underscoring the effectiveness of the proposed LEMS in accurately representing the post-disaster structural behavior of the bridge, which provides invaluable insights for further inspections and evaluations. It contributes tangibly to the field of civil engineering and infrastructure maintenance by introducing a novel approach in the design and implementation of emergency monitoring systems specifically for post-disaster scenarios.
Temperature Compensation using Constraint based Dynamic Time Warping in Guided Waves
Authors:   Lulea University of technology, Kumar Narendra ,   Lulea University of technology, Gupta Payal ,   Lulea University of technology, Carlson Johan E.
Published: June 2024

Introduction:

Guided waves being highly sensitive to temperature variations, temperature compensation algorithms, such as Optimal Baseline Selection, Baseline Signal Stretch and Scale Transform demonstrate effective performance under limited conditions. Dynamic Time Warping (DTW) has shown excellent compensation performance, however it comes with a substantial computational burden of O(N*N), where N represents the number of samples in each signal.

Methodology:

DTW works by construction of cost matrix that maps every point in one time series to all the points in the other time series, that results in complexity O(N*N).This problem can be solved by narrowing the search window using global constraints. The two most common constraints in the literature are the Sakoe-Chiba band and the Itakura Parallelogram. This paper uses Sakoe-Chiba band as a global constraint, the Sakoe-Chiba band is defined through a window size parameter which determines the largest temporal shift allowed from the diagonal.

Temperature compensation performance of DTW Sakoe-Chiba is tested using the available OGW dataset #2 provided by Jochen Moll et al. The OGW dataset has been generated using 12 number of piezoelectric transducers bounded to CFRP (Carbon Fiber Reinforced Plastic) plate and varying the temperature from 20-60 degree with an increment of 0.5 degree. The signal is recorded for 1300 microseconds, that results in 13000 samples/time stamps(N). Initial results show that the Sakoe-Chiba constraint based DTW performs well and at a significantly lower cost, as indicated below.

Result:

The largest temporal shift (r) is estimated using Local Peak Coherence (LPC),for signal at 20 and 60 degree r is 135 samples. This results in complexity O(N*2r) which is very less than conventional DTW compensation technique O(N*N).

In the final paper, the analysis will be replicated across a range of temperatures, and the performance of damage detection will be thoroughly discussed.
Evaluation of a permanent SHM system performance in detecting damage scenarios considering runnability thresholds
Authors:   Politecnico di Milano, Bernardini Lorenzo ,   Politecnico di Milano, Radicioni Luca ,   Politecnico di Milano, Bono Francesco Morgan ,   Politecnico di Milano, Benedetti Lorenzo ,   Politecnico di Milano, Somaschini Claudio ,   Politecnico di Milano, Cazzulani Gabriele ,   Politecnico di Milano, Belloli Marco
Published: June 2024

Due to an increasing number of bridges approaching the end of their design lives, there is a growing interest by the infrastructure managers on monitoring systems able to provide information on the service capability of structures. Focusing, in particular, on railway bridges, serviceability is related to both structural serviceability and runnability, where the first is related to the structural capability of the bridge, while the second is related to the constraints on rail geometry.
In this work the authors present an investigation of the capability of a permanent monitoring system to detect the presence of damages that would lead to an exceedance of the structural threshold values imposed by runnability norms. The target structure is a double-span Warren truss railway bridge, located in Italy, designed in 1946 and currently instrumented with a permanent monitoring system. The latter performs dynamic, quasi-static and static measurements, with sensors ranging from velocimeters to end displacement transducers and clinometers. Thanks to a FE model of the actual structure, a set of damage scenarios can be simulated, defining the damage entities that lead to the exceedance of thresholds imposed by norms in terms of runability assessment. As a result, the installed sensors mesh was found to distinguish such critical structural conditions as well as lower entity damage scenarios. The paper discusses the capability of different sensor configurations and monitoring strategies (“dynamic”, “static”, etc) to detect such damage scenarios, in terms both of “early warning” capability and of minimum time required to detect the anomaly.
The same approach can be extended to the analysis with respect to structural serviceability limits.
Incremental minimum distortion embedding for online structural damage classification.
Authors:   Technical University of Catalonia (UPC), Leon Medina Jersson Xavier ,   Technical University of Catalonia (UPC), Parés Núria ,   Technical University of Catalonia (UPC), Pozo Francesc
Published: June 2024

The damage detection problem in structures diminishes the maintenance cost and extends the life of the structures. Online structural damage classification is a relevant topic in structural health monitoring since monitoring the structure using sensors can help decision-makers can rely on empirical evidence for making informed choices regarding the maintenance, repair, or replacement of structural components. Sensors generate high-dimensional data. To address this challenge, employing dimensionality reduction methods becomes essential. These methods help obtain a low-dimensional representation of the data. Traditional dimensionality reduction methods are trained on a fixed data set, and incorporating new data may require retraining the entire model. This study employed the Minimum Distortion Embedding (MDE) method incrementally, ensuring the maintenance of a consistent embedding as new data becomes available. This incremental embedding took advantage of an anchor constraint to pin the existing embedding in place, then embed the new points. An online structural classification methodology was developed using the incremental MDE and a stream based hoeffding tree classifier. Stream data processing differs from batch processing, as it involves the real-time consumption of each incoming data. The developed methodology was tested with data obtained by accelerometers in a laboratory scaled jacket-type wind turbine foundation. The structural damage classification problems had 5 different classes including the undamaged class and 4 different damage classes. The damage corresponded to a 5mm crack in four different structural elements of the wind turbine foundation. The results indicate the good behavior of the damage classification methodology supported by the high values of the classification metrics obtained.
Bayesian Parameter Estimation of Vibrating Systems via Physics-Based Biases from Neural Networks
Authors:   ETH Zürich, Haywood-Alexander Marcus ,   ETH Zürich, Kamariotis Antonios ,   ETH Zürich, Arcieri Giacomo ,   ETH Zürich, Chatzi Eleni
Published: June 2024

Often, Structural Health Monitoring (SHM) campaigns draw damage-identifiers from vibration-based monitoring data. One of the downstream tasks of vibration-based SHM is that of system identification, i.e., inference of a model which is able to describe the system. For vibration-based monitoring, such models often rely on partial-differential equation formulations, where the identification task may be tied to inference of the parameters of such models. The probabilistic nature of SHM incentivises the extraction of stochastic estimates of these parameters, allowing for propagation of uncertainties associated to measurement noise or model imprecision. Common approaches to generating such stochastic estimates require an assumption on the shape and independence of the posterior distributions. To circumvent these assumptions, the Hamiltonian Monte-Carlo (HMC) sampling approach simulates the posterior distribution, allowing for freedom in the posterior. However, this requires intensive computational effort, especially when the governing equations have increased complexities, such as nonlinearities. Physics-informed neural networks (PINNs) utilise physics-based objective functions in the form of known PDEs, exploiting the automatic differentiation of NN architectures. This implies that additional terms in the PDE, such as nonlinearities, or boundary conditions can be accounted for. When the domain of observations encapsulates the collocation domain of the PINN, they can be exploited for parameter identification, by optimising over these parameters in addition to the network weights. However, classic objective functions in the form of mean-squared-norms underperform on noisy data, as a consequence of their deterministic nature. In this work, PINNs are combined with a HMC sampler to estimate the posterior distribution of the parameters of dynamic systems with noisy measurements. Multiple types of dynamic systems are simulated, varying the system complexity in terms of type and strength, and with different levels of noise. The results are assessed against the known values, and the codependent posterior distributions of each system are discussed.
Enhancing Predictive Maintenance in Contour Cutting Machines: A Similarity-Based Approach for Band Blade Remaining Useful Life Estimation and Early Fault Detection
Authors:   University of Siegen, Sharma Shubham ,   University of Siegen, Richter Wolfgang ,   Albrecht Bäumer GmbH & Co. KG, Bertelmann Philipp ,   University of Siegen, Kraemer Peter
Published: June 2024

Anticipating the Remaining Useful Life (RUL) of cutting tools is crucial for optimising operational efficiency, ensuring safety, and minimising maintenance costs. This paper addresses the unresolved challenge of unexpected band blade failures in contour cutting machines within the foam and plastic industry, where such failures disrupt production, lead to material wastage, and pose operational hazards. Band blades circulate continuously at very high speeds during operation, and no classic sensor technology can be applied to them. Any information required to estimate the condition of the band blades must be acquired externally. Using similarity-based prediction models, a strategy for estimating band blades' effective RUL is proposed. Historical failure data is systematically collected from a contour-cutting machine operating under various load conditions using multiple contact and non-contact sensors mounted on the machine. Features are extracted and processed from the mounted sensors in both time and frequency domains using classical condition monitoring techniques. The classification of damaged and undamaged states of the blade is performed using bagged decision trees, which consider predefined health levels of the band blade during test runs. Degradation trends are generated for a test set of blade failures, and a similarity model is trained on this data to determine the remaining life. Different features are selected based on their prognostic capabilities to optimise the proposed algorithm's performance. The developed model is tested on new data, demonstrating efficacy in predicting band blades' health status and RUL with good accuracy. This research contributes to cutting tool condition monitoring and predictive maintenance by offering a novel and reliable approach for early fault detection and estimation of the RUL of band blades in contour cutting machines, mitigating disruptions, reducing material wastage, and enhancing overall operational efficiency. The procedures are validated in situ with a latest generation's real contour-cutting machine.
Bayesian model-based damage detection in engineering systems using Invertible Neural Networks
Authors:   TNO, Martínez Andrés ,   TNO, Lathourakis Christos
Published: June 2024

This study explores the application of BayesFlow, a state-of-the-art deep learning technique for approximate Bayesian inference, in the context of Structural Health Monitoring (SHM), and specifically, model-based damage detection. BayesFlow diverges from other likelihood-free methods by not relying on predefined summary statistics, but learning optimal ones from the simulations via invertible neural networks. It is also capable of performing almost instantaneous inference for an arbitrary number of datasets, which is ideal for real-time applications such as bridge monitoring systems. The effectiveness of the proposed approach is demonstrated with a small scale bridge use case with synthetic measurements for different testing scenarios. Across all sub-cases, BayesFlow is able to determine both the damage location and severity with a high accuracy, while the credible intervals are close to those obtained under an exact Bayesian inference approach with the true likelihood.
Exploring Human-Crowd Interaction in Structural Monitoring: Insights from Two Decades of Events at the G. Meazza stadium
Authors:   Politecnico di Milano, Lucà Francescantonio ,   SACERTIS Ingegneria S.r.l., Turrisi Simone ,   Politecnico di Milano, Zappa Emanuele ,   Politecnico di Milano, Cigada Alfredo
Published: June 2024

In recent times, a growing body of research has emerged to evaluate how structures respond to dynamic forces generated by human activities. Notably, stadiums and sports arenas require significant attention due to the presence of spectators during events such as football matches and concerts. The way the crowd behaves during such events causes significant vibration levels which can be critical for both human comfort and structural integrity. Generally, the interaction between humans and structures is a significant concern in structural health monitoring. While a structure behavior may be considered deterministic (albeit challenging to predict), the forces exerted by crowds are inherently variable and uncontrollable. A critical aspect is related to the fact that studying the crowd behavior is a complex task, because modelling it not only implies engineering knowledge but also taking into account other complex factors, e.g., the social and psychological aspects of human behavior. In this context, this paper uniquely contributes by presenting and analyzing real-world vibration data gathered by the monitoring system of the G. Meazza stadium in Milan. This extensive dataset, built over the past 15 years, represents a one-of-a-kind contribution in the literature on stadiums and sport arenas, offering valuable insights into possible factors that influenced the evolution of crowd behavior during large-scale events.
Transfer learning-based drive-by monitoring of railway tracks
Authors:   University College Dublin (UCD), Ghiasi Ramin ,   French National Railway Corporation (SNCF), Lestoille Nicolas ,   French National Railway Corporation (SNCF), Diaine Cassandre ,   University College Dublin (UCD), Malekjafarian Abdollah
Published: June 2024

Track quality significantly impacts passenger comfort and safety. The maintenance of track quality involves routine monitoring of track geometry and track component defects. In a drive-by monitoring framework, vibration data are collected from operational trains providing an effective and low-cost track monitoring system. This framework enables each train to inspect multiple lines without the need to install and maintain sensors on each line. In conventional data-driven frameworks that utilize machine learning or statistical models, geometrical defects diagnosis model is often constructed using supervised learning. Supervised learning requires response data and corresponding damage states (i.e., labels) for each railway line to learn the line-specific damage diagnosis model. However, labelled data are frequently unavailable for many lines in practical situations, making it challenging to construct a damage diagnosis model. Furthermore, using data directly from source line to construct a damage diagnosis model for the target line can lead to inaccurate results. To this end, in this paper, a novel drive-by monitoring framework is proposed based on Transfer Learning (TL) concept. This approach transfers the geometrical defects diagnosis model learned from one line to a new line. Firstly, condition-sensitive time domain features are extracted from the raw data. Then, a deep learning model based on the long short-term memory (LSTM) network is trained on a dataset from one line (source domain) and fine-tuned for a new dataset using a small number of samples from another line (target domain). The effectiveness of this framework is compared through the classification performance using real-world monitoring datasets collected from two different lines of the France railways network. The results demonstrate up to a 22% increase in defect detection accuracy compared to the basic method.
Consequences of thermal aging on the detection of impact damage in composite structures with PZT/FBG guided wave systems
Authors:   ONERA, Mastromatteo Loïc ,   ONERA, Gavérina Ludovic ,   ONERA, Roche Jean-Michel ,   ONERA, Lavelle Florian ,   ONERA, Irisarri François-Xavier
Published: June 2024

In the context of recent reusable launch vehicles (RLV) developments, SHM could help fast and economic revalidation of RLVs between launches, provided that the transducers keep their functionality and reliability after repeating launches being exposed to harsh flight conditions.
This paper studies the detection of barely visible impact damage in composite plates with guided waves (GW) based SHM systems. More precisely, it focuses on how this detection can be affected by the degradation of the sensors potentially induced by the exposition to flight conditions. The aging conditions in this work mainly consist of thermal cycling with high maximum temperature (150°C), expected for RLV structures and close to the operational limit of the thermoset-matrix composite. The response of the sensors to such environmental conditions depends on the sensor itself and on its bonding to the structure. Thus, in this paper two different types of sensors are tested (commonly used PZT transducers and FBG sensors) along with two different bonding methods (classic bonding with a secondary adhesive and cobonding on composite surface during the curing process).
The sensors are setup in pairs (PZT-PZT or PZT-FBG) with GW signal paths going through the impact location. After a baseline acquisition, incremental impact tests (with increasing energy) are performed and GW signals are acquired after each impact to assess the damage signature on the waveform. Then after submitting the plate to thermal cycling, GW signals are recorded again to assess its effect on the damage detection performance and on the risk of false alarms (mistaking sensor degradation for structural damage). Sensor degradations are monitored through non-destructive techniques and impedance self-diagnostic measurements for PZT. Additionally to this aging test, the responses of degraded sensors previously studied by the authors are compared with the impact signature to assess how those degraded sensors could harm the impact detection.
Economic and Ecological Benefits of SHM Techniques in Chemical Process Industry
Authors:   Evonik Operations GmbH, Hardes Corinna ,   University of Siegen, Moll Jochen ,   Evonik Operations GmbH, Treppmann Dirk ,   University of Siegen, Kraemer Peter ,   Fraunhofer Institute for Ceramic Technologies and Systems IKTS, Schubert Lars ,   Schmidtsche Schack Arvos GmbH, Weidenfeller Joerg ,   KT-Systems GmbH, Kress Klaus-Peter
Published: June 2024

High-performance machine components are involved in a wide range of industrial production processes. They play an important role in the provision of chemical products, pharmaceuticals, and energy resources. They require monitoring because they can fail during production due to corrosion and/or wear, which often means an increased risk to people and the environment. Periodic inspections with on-site test personnel often require a shutdown of the chemical plant. Ideally, an ultrasonic structural health monitoring (SHM) system could be used to provide continuous data on the structural integrity of these components while reducing or eliminating plant downtime. A plant shutdown is not only a high economic or supply risk but can also be energy intensive. In our presentation, we would like to give an overview of the technical requirements for an SHM system to be used in the chemical industry. Finally, the presentation will highlight the economic benefits of SHM as well as its contribution to energy savings and reduction of CO2 emissions per ton of chemical product produced.