bluuebunny/crossref_metadata_2025 huggingface dataset:
Design of Staggered Halbach EMAT to Generate SH Guided Wave in Pipe Structures
Authors:   Indian Institute of Technology Madras (IITM), Garewal Pranaydeep ,   Indian Institute of Technology Madras (IITM), Balasubramanian Krishnan
Published: June 2024

This research investigates the application of Staggered Electromagnetic Acoustic Transducer (EMAT), mainly focusing on the beam steering capabilities of Shear Horizontal (SH) Guided Waves in pipe structures. As a specific case of EMAT, Staggered EMAT introduces a lateral movement in the magnet rows so that the wave propagation occurs at an angle. A three-dimensional model of double row Staggered Halbach EMAT is developed and a finite element analysis is carried out in an aluminium pipe with high radius to thickness ratio. To obtain better signal-to-noise ratio (SNR), the size of the magnets and the number of magnets in a row are optimized by studying multiple cases. For all the configurations, the in-plane surface displacement is studied keeping the wavelength of the transducer constant. The corresponding frequency is derived from the dispersion curve for SH0 wave mode. The results shows that there is significant increase in the amplitude of the in-plane surface displacement with the increase in magnet height and the number of magnets in a row, ultimately leading to the signal with higher amplitude and better SNR. The study then analyses Staggered Halbach EMATs where the second row of magnets is displaced by the maximum value of half of the wavelength. The interference between the waves generated from the two magnet rows resulted in the directivity change of the guided wave, thereby achieving the steering of the SH wave by the maximum value of 27°. The finding of this study shows the enhanced beam steering capabilities of Staggered EMATs, particularly in the field of pipeline monitoring and can lead to more precise and effective detection of structural defects.

Keywords: Staggered Halbach EMAT, Beam Steering, SH Guided Wave, Pipeline Inspection
Design of a Permanent Magnet Electromagnetic Acoustic Transducer (EMAT) for In-Situ Inspection of Metals at high Temperatures
Authors:   Indian Institute of Technology Madras (IITM), Mudapaka Vinay ,   Indian Institute of Technology Madras (IITM), Garewal Pranaydeep ,   Indian Institute of Technology Madras (IITM), Gantala Thulsiram ,   Indian Institute of Technology Madras (IITM), Balasubramanian Krishnan
Published: June 2024

In this proposed research, a permanent magnet longitudinal wave Electromagnetic Acoustic Transducer (EMAT) was designed for In-Situ inspection of metals at high temperatures. A permanent magnet EMAT generates Lorentz force inside metals by the interaction of magnetic fields and eddy currents. As temperature rises, various factors can influence the intensity of the Lorentz force, making it difficult to produce and detect ultrasonic waves. Thus, it is essential to optimize the sensor at ambient temperature before attempting to generate a reliable signal at elevated temperatures. The challenge highlighted has been addressed by optimizing the intensity of static field by varying the parameter like spacing between magnets that are being used. In order to minimize the effects of elevated temperatures, the sensor's stability has been enhanced by the implementation of efficient insulating measures. Furthermore, Finite Element (FE) simulations were conducted under on aluminium sample. These simulation results were then compared to experimental data to validate the efficiency of the designed sensor.

Keywords: Permanent magnet, EMAT, Lorentz force, ultrasonic waves, high temperature, static magnetic field intensity.
Monitoring of the preload in bolted connections with piezoelectric wafer active sensors
Authors:   University of Siegen, Dreisbach Anna-Lena ,   University of Siegen, Fritzen Claus-Peter ,   University of Siegen, Kraemer Peter
Published: June 2024

Piezoelectric wafer active sensors are very sensitive to changes of loads in structures, which makes them useful for the load monitoring. Unfortunately, these transducers are also sensitive to environmental effects such as a change of the ambient temperature. These effects can mask the information about the load in the recorded measurement data of the piezoelectric wafer active sensors. Therefore, the effect of the load needs to be distinguished from these masking effects of the environmental influences in order to avoid misleading information about the occurring loads of the monitoring system.
In this work, two monitoring procedures, which are applicable with piezoelectric wafer active sensors, shall be applied in order to have two different sources of information and be able to distinguish between the load information and the temperature influence. One approach is based on electro-mechanical impedance measurements and the other one is based on acousto-ultrasonics. The strategy of combining these two information shall be used to monitor the preload in bolted joints.
In an experimental setup, piezoelectric wafer active sensors have been attached to the bolt’s head and the face side of its shaft. Then, different preload stages were set. At every load stage, acousto-ultrasonics measurements and also electro-mechanical impedance measurements were carried out. First experiments show very promising results.
Enhancing infrastructure management with InSAR monitoring
Authors:   TRE ALTAMIRA, Henrion Eric ,   TRE ALTAMIRA s.l.u., Sanchez Jordi ,   TRE ALTAMIRA, Walter Diana
Published: June 2024

Over the last few years, the management of multi-asset infrastructure over wide regions has experienced a significant evolution due to novel technologies or accessibility to cheaper sensors combined with the Internet of Things (IoT). Interferometric Synthetic Aperture Radar (InSAR) data has proven to be the most cost-effective technology to meet users’ requests. However, InSAR’s large volume of data presents challenges for end-users. Data aggregation and visualisation are needed to streamline operations and information transfer between teams.

This proposed methodology seeks to address the challenges associated with InSAR data by integrating InSAR observations with the digital equivalent of existing structures in a repeatable and scalable manner.

To achieve this, deformation profiles are generated adaptively, considering the asset’s characteristics, such as width and orientation, as well as the satellite acquisition geometry. In parallel, a two-dimensional approach is followed to associate InSAR information with each structure element and thus organise the information spatially.

It should be noted that, even when using high-resolution radar data, it is sometimes challenging to associate radar returns with specific asset segments due to the complex interaction of microwaves with particular targets. Consequently, a key feature of this methodology is to classify InSAR data according to a precise geocoding of each scatterer, including an accurate estimation of its elevation.

This methodology makes it possible to analyse many different types of infrastructure, which can be time-consuming for large highway or railway operators. The coverage of the InSAR results on a regional scale, and the semi-automated method gives decision-makers an overview of their structures to send maintenance teams, saving time and costs.

This methodology allows for InSAR-derived product analysis, making the results more readable, accessible, and impactful. This type of infrastructure monitoring reflects a new maturity of InSAR in this field, demonstrated in real case studies.
An unsupervised damage detection method for an operating wind turbine blade
Authors:   University College Dublin (UCD), Ashkarkalaei Mohadeseh ,   University College Dublin (UCD), Ghiasi Ramin ,   University College Dublin (UCD), Pakrashi Vikram ,   University College Dublin (UCD), Malekjafarian Abdollah
Published: June 2024

Wind turbine blades are normally exposed to significant dynamic loads over their lifetime, including extreme conditions. This can lead to several types of damages such as cracking and delamination. Monitoring the structural condition of the blades to detect damages before they become catastrophic can be important. In recent years, the concept of data-driven structural health monitoring (SHM) approaches for wind turbine blade damage detection has become popular. These methods employ features collected from time series obtained from monitored wind turbine blades to identify damages. However, selecting an optimum number of features, which can reduce computational cost and minimize the effects of environmental and operational conditions, is still a challenge. In this paper, a data-driven approach is developed combining a feature extraction strategy and un-supervised anomaly detection. Monitoring data collected from several accelerometers mounted on a blade of an operating Vestas V27 wind turbine in healthy and damaged scenarios are used in this regard. Several time-domain, temporal, and time-frequency domain features are employed for the purpose of anomaly detection using unsupervised learning methods. Isolation Forest and One-class Support Vector Machine are used to train for anomaly detection. A threshold of detection is established to distinguish between the healthy and damaged states. Results show that the proposed approach can successfully identify the damage cases, but environmental and operational conditions still have some effect when it comes to identifying the extent of the damage.
Vibration measurement technique by using OFDR with in-line interferometers
Authors:   University of Tokyo, Shi Muyu ,   Public University of Navarre, Leandro Daniel ,   Shanghai University, Zhu Mengshi ,   University of Tokyo, Murayama Hideaki
Published: June 2024

Due to their compact size, electromagnetic interference immunity, and compatibility with embedding in composite materials, optical fiber sensors have emerged as a versatile and integral solution for structural health monitoring (SHM) in various structures. Among the key contributors to the evolution of this field is the significant progress in optical frequency domain reflectometry (OFDR), a technology that offers unparalleled high resolution and sensitivity in distributed strain sensing, thereby establishing itself as an indispensable tool in the realm of SHM. Through the implementation of OFDR, we devised a distributed fiber optic sensor tailored for the precise measurement of vibration or strain within structures.
In previous works, vibration monitoring has been achieved using an OFDR with an array of 20 in-line interferometers, serving as weak reflectors that not only facilitated vibration monitoring but also furnished valuable strain information. In this study takes a step further by introducing an innovative technique wherein in-line interferometers are strategically arranged in a non-uniform fashion along the optical fiber.
To validate the practical utility of this technology, extensive simulations and experiments were conducted, utilizing a cantilever beam as a representative structure. The results obtained from these endeavors showcase the tangible potential and reliability of optical fiber sensors in the practical implementation of SHM across various applications.
By enhancing the precision and scope of structural monitoring, optical fiber sensors equipped with non-uniform interferometer arrangements pave the way for more effective and comprehensive structural health assessment in diverse settings.
Investigation of Mixed-Mode Crack Quantification with Distributed Fiber Optic Sensors
Authors:   Hong Kong University of Science and Technology (HKUST), Lu Xuanyi ,   Hong Kong University of Science and Technology (HKUST), Zhang Shenghan
Published: June 2024

The development of high-resolution distributed fiber optic sensing (DFOS) technologies, particularly optical frequency domain reflectometry (OFDR), has proven effective in measuring crack widths. However, most existing research assumes that the crack opening direction is perpendicular to the cable direction, with limited studies addressing mixed-mode cracks using distributed fiber optic sensors. This research aims to create a novel method for quantifying mixed-mode cracks using DFOS, combining experimental investigations and numerical simulations. A calibration rig was designed and fabricated to simulate mixed-mode crack conditions in a 2D plane, allowing for the reproduction of various crack opening scenarios. Multiple fiber optic cable types with distinct strain transfer mechanisms were used, crossing the artificial crack surface at varying angles. The mechanical behavior of fiber optic cables in mixed-mode crack situations was further examined through numerical simulations. Based on findings from both experimental and numerical studies, an algorithm was developed to determine crack width and crack opening direction using DFOS data. As mixed-mode cracks frequently occur in reinforced concrete structures under complex stress conditions, this research enhances the understanding of fiber optic sensor performance in realistic cracking situations and offers valuable insights for more precise damage assessment using DFOS.
Advanced Acoustic Emission-based SHM for Concrete Structures: Real-Time, High-Precision Imaging of Crack Geometry and Damage Source-type Using Moment Tensor Inversion
Authors:   Swiss Federal Institute of Technology  (EPFL), Momeni Seyyedmaalek ,   Portland State University, Schumacher Thomas ,   SRK Consulting Ltd, Linzer Lindsay ,   Swiss Federal Institute of Technology  (EPFL), Lecampion Brice
Published: June 2024

Acoustic Emission (AE) monitoring is a powerful technique for continuously inspecting the health of structures, thereby aiding in the prevention of potential failures. AE consists of elastic waves that are produced and emitted during fracture processes and are captured by sensors. Utilizing quantitative geophysics-based methods, these recorded signals can be processed to monitor and delineate the spatiotemporal progression of fractures in opaque materials, such as concrete, in real-time. Moreover, the classification of damage source types, namely shear versus tensile, by processing AE waveform features, is crucial for appropriate structural treatment. Owing to the complex nature of the recorded elastic signals and the heterogeneity of the media, data are typically processed manually, covering both AE localization and source type classification. This, coupled with the high processing costs of extensive datasets, often exceeding terabytes, has limited the method's practical applications. In this paper, we introduce a novel automated and high-precision AE monitoring algorithm and software (Momeni et al., 2021a, b) suitable for SHM. It is designed to work with various standard data formats and is capable of handling both trigger-based and continuous data, and medium heterogeneity. We present results from implementing this software in the AE monitoring of two 4.88-meter-long concrete beams in a laboratory setting, comparing it with manually processed AE data (Mhamdi, 2015). Our approach enabled the identification of over three times more AE sources than manual processing, achieving higher precision. By processing waveform features in both time and frequency domains, we successfully classified the damage sources into three categories: tensile, shear, and mixed-mode, at different stages of the experiment. With adequate processing units, the software can operate in parallel, facilitating real-time SHM with exceptional precision in imaging both crack geometry and source types. This provides invaluable information to decision-makers regarding the nature of the data captured.
Nonlinear elastic waves generated by surface wave interaction with local plasticity in thick-walled structures
Authors:   The Hong Kong Polytechnic University, Zhang Yuanman ,   Tongji University, Shan Shengbo ,   The Hong Kong Polytechnic University, Cheng Li
Published: June 2024

Thick-walled structures (TWSs) are frequently utilized as primary load-bearing structures across diverse industrial sectors, illustrated by train axles. Proper health monitoring, particularly for early damage detection, is required to ensure the operational safety and structural integrity of such structures. Nonlinear elastic-wave-based structural health monitoring techniques present a potentially efficacious methodology, which relies heavily upon the understanding of nonlinear wave generation and propagation characteristics. Unfortunately, due to the complexity introduced by various wave modes in TWSs, a comprehensive understanding of nonlinear elastic waves is still lacking. Considering practical local plasticity on the surface of a TWS, this study investigates the characteristics of nonlinear elastic wave generation and propagation resulting from the fundamental surface wave-local plasticity interaction. Theoretical analyses were carried out to examine the scattering features of the generated nonlinear bulk waves based on Snell’s law. Finite element simulations were then performed to confirm the predicted scattering characteristics of the nonlinear bulk waves, with the introduction of a local material nonlinearity to replicate the plasticity in a TWS. Using the extrusion method, a local plastic zone was produced on an aluminum thick-walled beam. Experiments were finally carried out using a laser vibrometer to confirm the scattering features of nonlinear elastic waves. It was found that the nonlinear shear bulk waves are mainly generated due to the fundamental surface interaction with local plasticity. Furthermore, the generated nonlinear shear bulk waves propagate at a fixed angle towards the interior of the TWS, which can be measured on the opposite surface. The elucidation of the nonlinear elastic wave generation and propagation characteristics paves the way for an effective sensor arrangement in early damage detection applications.
Active transfer learning for SHM of bridges under changing environmental conditions
Authors:   Sheffield University, Poole Jack ,   Sheffield University, Hughes Aidan J. ,   Sheffield University, Gardner P ,   Sheffield University, Bull Lawrence A. ,   Sheffield University, Dervilis Nikolaos ,   University of Perugia, Giglioni Valentina ,   Sheffield University, Mills Robin ,   Sheffield University, Worden Keith
Published: June 2024

Obtaining data for training structural health monitoring (SHM) systems is often expensive and/ or impractical, particularly labelled data. Population-based SHM (PBSHM) presents a potential solution to this issue by considering the available data across a population of structures. However, inherent dissimilarities among structures will result in divergent training and testing distributions. Consequently, specialised machine learning methods are required to ensure models can effectively generalise between structures. As such, a transfer learning methodology is proposed to exploit data pertaining to various damage-states and environmental conditions from a source structure, with the aim of enhancing predictions in a target structure with limited data; an active learning framework is used to improve the transfer learning method. The effectiveness of this methodology is evaluated on a novel population of experimental bridges, enabling robust validation of transfer learning for PBSHM in realistic scenarios. Specifically, this population includes data corresponding to several damage-states, as well as, a comprehensive set of environmental conditions; for example, freezing conditions and waterlogging of the deck.
Predicting damage location information based on single actuator sensor path information using regularised multilinear regression
Authors:   Deutsches Zentrum für Luft- und Raumfahrt (DLR), German Aerospace Center, Behrens Tim ,   Deutsches Zentrum für Luft- und Raumfahrt (DLR), Moix-Bonet Maria ,   Deutsches Zentrum für Luft- und Raumfahrt (DLR), German Aerospace Center, Schmidt Daniel ,   Deutsches Zentrum für Luft- und Raumfahrt (DLR), German Aerospace Center, Wierach Peter
Published: June 2024

In structural health monitoring with guided ultrasonic waves, probability reconstruction algorithms are a method to locate a damage. They work by calculating the probability for each actuator sensor path on whether there is a damage on the path or not. By superposition of each path and its damage probability, a damage localisation is done. The disadvantage of this is, that the damage localisation resolution is limited by the number of paths crossing each other.

To overcome this, the hypothesis of this investigation is that the information of a path can not only be used to determine whether a damage is present, but that additional information about the location within the path can be calculated as well. This way a localisation resolution can be higher than by only relying on the path density.

To verify this assumption, an experimental setup was chosen in which the path lengths always remain the same while the distance between damage and the direct path varies. This is implemented by a moving ultrasonic microphone simulating the sensor. The varying distance is the local information, which is determined in this study using the information of a single path. For this purpose, a prediction is calculated using regularised multilinear regression. The input features are characteristic values of five sections of the sensor signal in the time domain. The sections are manually chosen based on arriving wave events.

The result confirms the hypothesis. Therefore, it is plausible to increase the detection resolution of probability reconstruction algorithms by calculating damage location estimations for each path.
POD methodologies for SHM application in civil engineering
Authors:   Applied Validation of NDT (AV-NDT), Rentala Vamsi Krishna ,   Applied Validation of NDT (AV-NDT), Kanzler Daniel ,   Helmut-Schmidt-University / University of the Federal Armed Forces Hamburg, Kessler Sylvia ,   Helmut-Schmidt-University / University of the Federal Armed Forces Hamburg, Jaelani Yogi ,   Helmut-Schmidt-University / University of the Federal Armed Forces Hamburg, Knoth Sven ,   Helmut-Schmidt-University / University of the Federal Armed Forces Hamburg, Beering Carina
Published: June 2024

Structural Health Monitoring (SHM) technologies can significantly enhance safety through continuous monitoring. The successful deployment of real-time SHM systems rely mainly on reliability evaluations. While numerous standards exist for reliability assessments or Probability of Detection (POD) of NDT techniques, there are no specific standards guiding reliability procedures for SHM data. Consequently, NDT POD methods are often applied to SHM data, despite the time and statistically dependent nature of SHM data. Especially in the field of civil engineering, there is the need for a SHM specific POD approach able to perform a meaningful reliability evaluation.
Hence, in the current work, different SHM POD models including random intercept model and length at detection (LaD) model are implemented for a vibration-based monitoring data relevant for civil engineering. Unlike the linear response observed in NDT inspection data concerning damage, SHM data can exhibit non-linear characteristics in certain cases. In those cases, an advanced LaD model suitable for non-linear data is also developed in the current study. In addition, a comparison between the results obtained using NDT POD models and SHM POD models is demonstrated using an open-source GUI (TU1402) based on FE model. Moreover, the results obtained by the application of modified LaD POD model to a real test bridge data is demonstrated.
Keywords: SHM, reliability, POD, civil engineering.
In-service Load Monitoring for an UAV Digital Twin
Authors:   Beihang University (BUAA), Zhou Xuan ,   Air Force Institute of Technology, Dziendzikowski Michal ,   Air Force Institute of Technology, Dragan Krzysztof ,   Beihang University (BUAA), Dong Leiting ,   Politecnico di Milano, Giglio Marco ,   Politecnico di Milano, Sbarufatti Claudio
Published: June 2024

A load monitoring system plays an important role in recovering the actual load spectra of aeronautical structures, contributing to the online evolution of airframe digital twins. In scenarios where many aircrafts lack on-board strain sensors during the service phase, yet some strain data is available during the test flight phase, our innovative approach utilises deep learning-based flight-strain prediction and an inverse-direct approach for in-service load monitoring. Initially, a deep learning approach is employed during the test flight prior to service to establish a flight parameter-strain prediction model. This model, incorporating time series features of flight and strain da-ta, exhibits superior predictive accuracy compared to traditional regression methods. Moving into the subsequent service phase, the flight parameter-strain prediction model seamlessly integrates with an inverse-direct load monitoring method. This integrated approach facilitates real-time monitoring of full-field load distribution, relying solely on flight parameters. Validation of the approach utilises flight test data from an unmanned aerial vehicle, revealing better performance compared with the strain-measurement-based method. Notably, our method's efficacy extends across diverse aircraft types, as it does not rely on on-board strain sensors during the service phase.
A weighted correction of RAPID for precise damage localization in composites using guided waves and principal component analysis
Authors:   Leibniz University Hannover, Abbassi Abderrahim ,   Leibniz University Hannover, Römgens Niklas ,   Leibniz University Hannover, Dörpinghaus Arved ,   Politecnico di Milano, Lomazzi Luca ,   Leibniz University Hannover, Grießmann Tanja ,   Leibniz University Hannover, Rolfes Raimund
Published: June 2024

Composite materials are widely used in various engineering applications such as aerospace, automotive, construction, and sports equipment due to their superior mechanical properties, including a high strength-to-weight ratio, design flexibility, and resistance to fatigue and corrosion. However, the complex nature of composite materials and their damage mechanisms poses a challenge to effective Structural Health Monitoring (SHM). Guided waves have been shown to be an effective non-destructive testing technique for localizing damage in Carbon Fiber Reinforced Polymer (CFRP) materials when used with the RAPID (Reconstruction Algorithm for Probabilistic Inspection of
Damage) algorithm, which has high robustness and efficiency. However, in some cases, the RAPID algorithm may suffer from inaccuracies in predicting damage positions due to the intersection points among transducer paths. To address this issue, this paper proposes a correction to the original RAPID algorithm. The correction introduces weights to the probability of damage position, computed from the number of intersections that cross that position, to mitigate the impact of the intersection problem. Additionally, the Q-statistic, which is calculated through principal components analysis (PCA), is employed as a damage position indicator. To evaluate the effectiveness of the proposed correction, the measurement data from 12 transducers mounted on a 500 × 500 mm CFRP plate with a reversible damage model placed in different positions are used. The results of the proposed method are compared to the results of RAPID, a suggested geometrical correction of the RAPID algorithm. The findings demonstrate that the proposed correction provides an effective means to predict damage positions with greater accuracy than the original RAPID algorithm. Furthermore, the proposed
correction does not compromise the computational simplicity of the RAPID algorithm, which is one of its key advantages.
An Advanced Nonlinear Framework for Early Detection and Prognosis of Fatigue Cracks in Plate-Like Structures
Authors:   Politecnico di Milano, Liang Haiying ,   Northeast University of China, Liu Yang ,   Nanjing University of Aeronautics and Astronautics  (NUAA), Chen Jian ,   Nanjing University of Aeronautics and Astronautics  (NUAA), Yuan Shenfang ,   Politecnico di Milano, Giglio Marco ,   Politecnico di Milano, Sbarufatti Claudio
Published: June 2024

Plate-like structures are prone to the initiation of fatigue cracks during operation, primarily due to the harmonic loading imposed by the external environment. These micro-cracks possess the potential to progress into more severe forms, compromising structural integrity and, in extreme cases, leading to catastrophic accidents. The primary focus of this research is to introduce an online nonlinear framework for the early diagnosis and prognosis of fatigue cracks. This framework aims to detect nascent fatigue cracks in plate-like structures, facilitating proactive measures to prevent structural failure and preserve the overall integrity. The initial phase of this study involves capturing Lamb waves generated by subjecting the inspected plate-like structures to an excitation signal. The excitation signal, along with the S0 mode Lamb wave, is employed to identify a Nonlinear Autoregressive with Exogenous inputs (NARX) model, serving as a representation of the underlying nonlinear dynamic mapping intrinsic to the inspected specimen. Subsequently, the Nonlinear Output Frequency Response Functions (NOFRFs) are identified based on the established NARX model. An early fatigue crack damage index, denoted as optimal NOFRFs weighted contribution rate based on the KL divergence (KLRm), is extracted from the NOFRFs and employed for estimating crack lengths. Additionally, the Particle Filter (PF) is utilized for predicting the Remain Useful Life (RUL). To validate the effectiveness of the proposed framework, fatigue experiments are conducted on aluminum lug joint specimens. The experimental results demonstrate the effectiveness and precision of the framework in estimating crack lengths and predicting RUL for nonlinear early fatigue cracks.
Increased accuracy of service life prediction for fiber metal laminates by consideration of the manufacturing-induced residual stress state
Authors:   Technische Universität Braunschweig (TU), Wiedemann Johannes ,   Technical University of Dortmund (TU Dortmund), Mrzljak Selim ,   Technische Universität Braunschweig (TU), Hühne Christian ,   Technical University of Dortmund (TU Dortmund), Walther Frank
Published: June 2024

Fiber metal laminates (FML) combine the high specific properties of fiber-reinforced polymers with the ductility and damage tolerance of metals. However, during the manufacturing process of FMLs, in-plane residual stresses arise in the single layers of the laminate. The stresses mainly originate from the different coefficients of thermal expansion of the two materials and lead to an inhomogeneous stress state in the thickness direction of the laminate.

This work investigates the influence of such thermal residual stress (TRS) on the fatigue life in an FML by experimentally varying the TRS in identical layups using modified cure cycles. The experimental investigations show that the amount of TRS directly influences the fatigue performance of such laminates. Reducing the TRS, particularly the tensile residual stresses in the metal layers, slows the fatigue crack growth during cyclic loading. This leads to a significantly delayed crack initiation and slower crack propagation.

Knowledge of material behavior is essential for utilizing the full potential of structural health monitoring (SHM) in component lifetime predictions. Predictive models require a set of input parameters to perform this task. Since the TRS in an FML significantly contributes to the component's fatigue life, its consideration in predictive approaches is inevitable. This finding is discussed based on a phenomenological damage accumulation model initially developed for composite materials.
Acoustic Emission Detection in Noisy Environments using Linear Prediction
Authors:   Leibniz University Hannover, Lange Alexander ,   Dresden University of Technology (TU Dresden), Xu Ronghua ,   MKP GmbH, Käding Max ,   Dresden University of Technology (TU Dresden), Marx Steffen ,   L3S Research Center, Ostermann Jörn
Published: June 2024

In non-destructive testing, acoustic emission testing is an established technique with numerous applications in academia and the industry. In nearly all applications, the detection of relevant acoustic emission signals in continuous structure-borne sound recordings is a crucial step that forms the basis for subsequent analyses and should therefore neither miss any relevant acoustic emissions nor detect too many irrelevant events. That is because, in its most prevalent form, acoustic emission testing utilizes ultrasound signals that require large amount of storage, which might be an economically limiting factor especially for structural health monitoring of large civil infrastructures. In these monitoring applications, the signal-to-noise ratio is also expected to be worse compared to more controlled laboratory conditions, as they can be often found, for example, in materials research. We therefore propose the use of linear prediction, a time series forecasting technique, to extract relevant acoustic emissions from continuous recordings. The proposed methodology utilizes the residual between the signal predicted from a linear combination of previous time steps and the actual measurement to detect any anomalous events. Since the linear predictor can be initialized using the environmental noise only, no specific knowledge of the acoustic emission signals of interest is required beforehand and hence can automatically adapt to each measurement environment. We compare our approach with a simple amplitude-based detection as it is commonly implemented in commercially available acoustic emission systems. Especially for low signal-to-noise ratios, an increase in the area under the receiver operating characteristic curve of up to 0.7 compared to the amplitude-based detection is found.
A model assisted probability of detection for guided wave SHM systems: comparative study on recent statistical developments
Authors:   University Paris-Saclay, Yilmaz Bengisu ,   University Paris-Saclay, Recoquillay Arnaud ,   University Paris-Saclay, Chapuis Bastien ,   University Paris-Saclay, Calmon Pierre
Published: June 2024

Despite the potential benefits, industrial implementation of guided wave monitoring systems remains limited today. One of the reason behind this is the lack of consolidated reliability assessment techniques. Recent works clearly illustrated that the classical statistical methodology adopted from nondestructive evaluation to estimate POD is not directly applicable to structural health monitoring (SHM): the hypothesis of independence does not hold for the data obtained from permanently attached sensors and growing defects. The experimental determination of POD for SHM systems requires extensive and costly studies, in addition to the numerous representative structures. Involving numerical studies can complement experimental trials, inform POD evaluation and reduce this cost drastically. We propose in this work a comparative study to demonstrate the application of Model Assisted Probability Of Detection approach (MAPOD) for elastic guided wave monitoring systems. We apply classical and recently developed statistical methods proposed in the literature, using simulated data obtained thanks to the CIVA SHM module. The MAPOD estimation is validated in comparison to experimental studies.
Application of regression and remote sensing technology for determining sufficiency of contact-based sensors in long-term SHM of civil structures
Authors:   Politecnico di Milano, Entezami Alireza ,   Politecnico di Milano, Behkamal Bahareh ,   Politecnico di Milano, De michele Carlo ,   Politecnico di Milano, Mariani Stefano
Published: June 2024

Long-span bridges are huge, complex, and vital civil engineering structures for every society. Due to the critical importance of such structures, structural health monitoring (SHM) systems utilizing both contact and non-contact sensor platforms are often considered to measure influential parameters, such as environmental/operational factors and various structural responses. In some circumstances, such as bridge locations, services, weather conditions, harsh environments, and budget constraints, it may not be practical or necessary to utilize all possible sensor systems and measure all parameters, especially environmental/operational (E/O) factors. To devise and implement an affordable SHM program for large-scale civil structures, it is necessary to develop more efficient sensing systems compared to state-of-the-art ones. Against this backdrop, this paper proposes a new methodology for verifying the sufficiency of contact-based E/O sensors installed in long-span bridges using spaceborne remote sensing and regression modeling. The main premise of the proposed methodology is that structural responses obtained from certain remote sensing products enable us to assess the sufficiency of contact sensors and the impacts of both measured and unmeasured E/O conditions. Utilizing structural displacement responses from remote sensing and measured E/O data from contact-based sensors, a supervised regression model using a feedforward artificial neural network is developed to reconstruct or predict displacement responses and to determine the regression accuracy using the R-squared measure. When the accuracy rate is high, one can infer that the contact-based E/O sensors are sufficient; otherwise, it may be essential to install additional E/O sensors and apply the proposed methodology with new information. Real-world long-span bridges are examined to validate the proposed methodology, using displacement responses and recorded temperature data from contact-based thermocouples. The results demonstrate that the methodology offers an effective but affordable system for long-term SHM programs.
Robust Structural Health Monitoring of CFRP Structures under Varying Loads using NSFD and Machine Learning with Combined Usage of Acceleration and Strain Data
Authors:   University of Siegen, Adam Tobias ,   University of Siegen, Lambat Mukul ,   University of Siegen, Kraemer Peter
Published: June 2024

Structural health monitoring (SHM) of objects in situ often requires a suitable combination of different methods and measurement techniques. In the field of aerospace applications, the previous research project "Combined acoustic and modal structure monitoring" dealt with the development of a robust SHM system for damage detection in carbon-fibre-reinforced polymer (CFRP) structures under varying realistic loads. The methods combined within the project were based on guided waves, acoustic emission, different vibration monitoring techniques and strain measurement.
The present paper deals with the application of the nullspace-based fault detection algorithm (NSFD) combined with a machine learning approach based on strain measurements to classify different load conditions of the structure. Due to the high sensitivity to changes in the statistical properties of the measurement data, the NSFD algorithm reacts very sensitively to structural changes, but also to changes in the environmental conditions, such as changes in the external loads and the resulting stress state of the structure.
In the paper, the impact of varying external loads on the NSFD damage indicator is analysed and shown. To prevent the NSFD algorithm from causing false alarms due to changes in the load conditions, but still being sensitive to small structural changes due to impact damages, the strain sensor data is used to predict the external loads on the structure. For this, machine learning is used to create clusters of similar stress states. For every cluster, a baseline for the NSFD algorithm must be made. The resulting approach is robust against changes in external loads and sensitive to small changes in the structure due to damages at the same time.
The theory and the algorithms are successfully tested with measured data sets from a CFRP-airplane structure.
A Composite Fuselage under Mechanical Load: a case study for Guided Wave-based SHM
Authors:   Deutsches Zentrum für Luft- und Raumfahrt (DLR), German Aerospace Center, Moix-Bonet Maria ,   Deutsches Zentrum für Luft- und Raumfahrt (DLR), German Aerospace Center, Schmidt Daniel ,   Airbus Operations GmbH, Eckstein Benjamin ,   Deutsches Zentrum für Luft- und Raumfahrt (DLR), German Aerospace Center, Wierach Peter
Published: June 2024

The introduction of composite materials in aeronautics has brought numerous advantages, along with unique damage and failure modes. The structure health is currently ensured by a damage-tolerant design and non-destructive inspections. Among other techniques, Guided Wave-based Structural Health Monitoring (GW-SHM) has gained interest as a cost and time effective alternative to traditional non-destructive techniques. One of the main challenges for GW-SHM is the influence of environmental and operational conditions on the damage identification capability.
Aircraft structures undergo a broad range of mechanical load conditions, affecting the GW-SHM system. The study of load influence is meaningful in view of applications where the SHM system is expected to work under varying load conditions. Such applications may be in-flight monitoring, but also on-ground only usage and structural test monitoring.
A full-scale CFRP door surrounding structure was instrumented with a GW-SHM system and tested under mechanical load. A hydraulic test rig was used to apply three representative load cases in quasi-static conditions on the structure. A network of robust piezocomposite transducers to monitor the structure has been designed and manufactured. The network is organized in arrays, which include the transducers, cabling and a connecting base plate for optimized sensor installation. A multiplexing module is directly connected to the base plate enabling a fast and reliable sensor connection, a drastic cable weight reduction, and a modular design.
The combined influence of load and temperature on the propagation of Guided Waves has been assessed. Finally, a data-driven approach to damage identification has allowed the detection and localization of barely visible impact damage introduced during the test campaign.
A systematic correlation analysis for regression model selection: Application to bridge response prediction using contact and remote sensor systems
Authors:   Politecnico di Milano, Entezami Alireza ,   Politecnico di Milano, Behkamal Bahareh ,   Politecnico di Milano, De michele Carlo ,   Politecnico di Milano, Mariani Stefano
Published: June 2024

Correlation analysis is a crucial step prior to any regression modeling for data prediction, as it can unveil the relationship between predictors and responses, particularly in terms of linearity and nonlinearity. This step is often mandatory for selecting the most suitable regression model. The major challenge is that linear correlation measures are only appropriate for linear cases and there are limited nonlinear measures for nonlinearity assessment. Apart from these challenges, the demanding issue relates to the influence of unknown and unmeasured predictor data in regression model selection thereby yielding unrealistic and inaccurate outputs from both linear and nonlinear correlation measures. To tackle these challenges, this paper proposes a hierarchical correlation analysis method that initially indicates the influence of unknown predictors and then assists in selecting the best regressor for modeling and forecasting. The proposed method utilizes a linear measure known as canonical correlation analysis and a nonlinear measure known as the maximal information criterion. Based on the correlation values derived from these measures, it is possible to classify them into three levels of correlation: low, moderate, and high. In the first and second scenarios, it is inferred that the response data is linearly or nonlinearly influenced by the measured predictors, respectively. In the third scenario, three conditions are derived from the correlation levels, suggesting that the response data is not influenced by the measured predictors. To validate the proposed hierarchical correlation analysis method's applicability and effectiveness, it is tested with predictor and response data related to real-world long-span bridges. The only measured predictor is air temperature, while the response data comprises limited bridge displacements obtained from synthetic aperture radar images, in line with remote sensing technology. The results indicate that the proposed method is highly effective and applicable for selecting the best regression model for prediction.
Drive-by Structural Monitoring using GNSS measurements at alpine bridges
Authors:   Graz University of Technology, Schönberger Caroline ,   Graz University of Technology, Lienhart Werner
Published: June 2024

Most bridges in Europe were built in the 1960s and 1970s and are now reaching the end of their design life. It is therefore important to monitor these bridges to ensure a safe operation for many years to come. Load tests are a common way of checking the health of an infrastructure. Such a test was carried out on a ~560m long motorway bridge (7 spans) in Austria. Previous studies have shown that geodetic GNSS (Global Navigation Satellite System) receivers with a data rate of 20Hz can be used for vibration monitoring. Therefore, for the load test, geodetic GNSS receivers were mounted on the moving load (2 trucks of 50 tons each) to test the capability of drive-by GNSS measurements for structural monitoring of bridges. Additional sensors (accelerometers, fibre optic cables) were mounted on one span and data was collected during the load tests. The frequency analysis performed with the GNSS measurements agrees well with the frequency analysis performed with the data from the other sensors, where the main frequencies occurred between 2-3Hz with amplitudes in the millimetre range. The advantage of drive-by GNSS measurements is the acquisition of data from the entire bridge. Therefore, the shape of the bridge (slack span of each span) and the dynamic behaviour (frequency response, mode shapes) can be derived from the same data at the same time.
Mitigation of unmeasured environmental and operational variability in long-term bridge health monitoring by unsupervised statistical learning
Authors:   Politecnico di Milano, Entezami Alireza ,   Politecnico di Milano, Mariani Stefano
Published: June 2024

Environmental and operational variations present a significant challenge in the long-term health monitoring of bridge structures due to their potential to cause serious confounding influences and wrong decisions. These influences may lead to false alarms of damage during normal operation of a civil structure or, conversely, mask actual damage, resulting in a failure to correctly signal a warning. Consequently, a critical task in structural health monitoring, particularly for bridges, is to mitigate the negative impact of environmental variability. An initial approach is to measure the most influential environmental and operational factors and then apply statistical tools to eliminate their effects. However, this approach may not be effective under all conditions. Therefore, the primary objective of this paper is to propose an innovative unsupervised statistical learning method that addresses the challenges posed by environmental and operational variability by considering unmeasured factors. The proposed method involves three steps: data clustering, unsupervised feature selection, and novelty detection. The initial step utilizes k-means clustering to categorize features (modal frequencies) into five types of clusters, reflecting the influence of five environmental and operational factors on bridge structures; that is, temperature, humidity/rain/snow, wind speed/direction, traffic, and damage. These clustered features are then processed through an unsupervised feature selection algorithm based on reconstruction independent component analysis to extract new features. These reduced features are subsequently employed in a novelty detection model, developed using the Mahalanobis-squared distance, to analyze the environmental and operational effects. Real dynamic data of a steel arch bridge is utilized to verify the proposed method. Results demonstrate that this method can effectively handle the demanding issue stemming from unmeasured environmental and operational variability conditions.