bluuebunny/crossref_metadata_2025 huggingface dataset:
Robustness of Structural Health Monitoring Systems for Offshore Energy Structures based on Damage Characteristics
Authors:   University of Surrey, Shafiee Mahmoud ,   University of Surrey, Ciampa Francesco ,   Cranfield University, Etebu Ebitimitula
Published: June 2024

Offshore energy structures such as fixed/floating wind turbines, tidal turbines and wave energy converters are exposed to loads coming from different sources. Recent advances in SHM offer many unique opportunities to assess the structural integrity of offshore energy infrastructure. The process of SHM generally involves the use of a system of sensors mounted on a structure; the processing of signals received from the sensors to attain damage sensitive features; and the detection of damage in the structure. Over the past decades, most of the research in SHM has been focused on improving and developing new sensor technology, signal processing, and damage detection algorithms. However, there are very few studies assessing the performance of SHM systems with respect to damage detection, localization, and quantification. Moreover, most of the performance analysis models for SHM systems rely only on 'probability of detection (POD)', that is an assessment index used in NDT to evaluate the probability of detecting a damage as a function of its size. For SHM systems, the replicability of POD is influenced by the environment, measurement noise, and the deterioration of sensors and instruments. Therefore, the distribution of POD is often determined by performing an extensive number of test sets with existing damage of varying magnitude. This paper aims to reduce the volume of experimental data required, as well as the uncertainties associated with POD prediction by using a model-assisted probability of detection (MAPOD) approach. We develop a Bayesian network (BN) method to evaluate the performance of SHM systems in terms of damage detection, localization, and assessment. In addition to POD, the probability of accurate localization (POAL) and probability of accurate assessment (POAA) are also incorporated into the analysis. The BN and limit state functions are applied to a fixed offshore energy structure equipped with a vibration based SHM system.
Sensor platform for periodic integrity inspection of subsea structures with a permanently installed measuring system
Authors:   Fraunhofer Institute for Ceramic Technologies and Systems IKTS, Weihnacht Bianca ,   Fraunhofer Institute for Ceramic Technologies and Systems IKTS, Wollstadt Johann ,   Fraunhofer Institute for Ceramic Technologies and Systems IKTS, Gaul Tobias ,   Fraunhofer Institute for Ceramic Technologies and Systems IKTS, Schnabel Andreas ,   Fraunhofer Institute for Ceramic Technologies and Systems IKTS, Kruska Carsten ,   Fraunhofer Institute for Ceramic Technologies and Systems IKTS, Klesse Thomas ,   Fraunhofer Institute for Ceramic Technologies and Systems IKTS, Schubert Lars
Published: June 2024

Offshore structures are inspected with high costs and risks. In order to reduce both, the Fraunhofer IKTS has developed a sensor platform for automated periodic inspection by underwater vehicles like Remotely Operated Vehicles (ROV) or Autonomous Underwater Vehicles (AUV).
The monitoring tasks are numerous. Temperature or pressure measurements are widely needed to ensure safe operation. Welded seams are a critical element of offshore infrastructure. If they fail due to high wave loads, extraordinary ice collision or due to fatigue behaviour, the entire structure becomes critical regarding the stability and safety.
The Fraunhofer IKTS developed a technique for the permanent installation on subsea structures in order to enable a periodic inspection. The communication and energy supply are realized wireless by WiFi and inductive energy supply. The sensors used differ from application to application. For the welded seam inspection, shear wave laminable transducers ensure a flat design to prevent damage due to current or waves. Shear waves give a relative independency from fouling to make the interpretation of the data more comprehensive.
The device has been tested in maritime environment for 2,5 years to ensure reliability. Furthermore, a modular setup with integrated transducers into the circuit board provide market relevant potential low prices on the long term.
The connection unit has been designed in a way that also small ROV/AUV can operate the light-weight unit to ensure a broad applicability by almost any vehicle on the market.
Physics-driven deep neural networks for damage identification using guided wave damage interaction coefficients
Authors:   Johannes Kepler University Linz (JKU), Humer Christoph ,   Johannes Kepler University Linz (JKU), Höll Simon ,   Johannes Kepler University Linz (JKU), Kralovec Christoph ,   Johannes Kepler University Linz (JKU), Schagerl Martin
Published: June 2024

In this paper, recent advancements in the development of a guided wave-based damage identification approach using wave damage interaction coefficients (WDICs) and deep neural networks (DNNs) are presented. These WDICs uniquely describe the complex scattering of guided waves around possible damages and depend on the properties of the damage itself. Hence, they are utilized as physics-based and highly sensitive damage features herein. It is demonstrated, that DNNs can effectively learn intricate relationships between damage characteristics and complex-shaped WDIC patterns from a compact sized training dataset. In this study, two training datasets are created by numerical finite element simulations and experimental scanning laser Doppler vibrometer measurements using a pseudo-damage approach. Therefore, the orientation and thickness of surface-bonded artificial damages are varied to generate the training data of 12 selected damage scenarios.

The generalization capabilities of the fully trained DNNs allow to accurately predict WDICs even for damage scenarios unseen during training. The presented damage identification method leverages this powerful ability to characterize properties of unknown damages. Once trained, the accurate DNN predictions become available promptly and can be compared with measured WDICs from an unknown damage for selected sensor positions. The performance of both simulation-based and experiment-based structural health monitoring approaches are assessed, while also addressing current limitations and possible enhancements. For example, the great potential of incorporating the phase information of the complex-valued WDICs in the identification procedure is discussed. Hence, this study highlights the latest advancements in the development of the presented damage identification method with promising results and provides valuable insights in the application of WDICs as physics-based features for DNNs.
Robust SHM: Redundancy approach with different sensor integration levels for long life monitoring systems
Authors:   Dresden University of Technology (TU Dresden), Bartels Jan-Hauke ,   Leibniz University Hannover, Potthast Thomas ,   Leibniz University Hannover, Möller Sören ,   Leibniz University Hannover, Grießmann Tanja ,   Leibniz University Hannover, Rolfes Raimund ,   Leibniz University Hannover, Beer Michael ,   Dresden University of Technology (TU Dresden), Marx Steffen
Published: June 2024

Monitoring structures such as bridges or wind turbines using Structural Health Monitoring (SHM) methods allows continuous assessment of their condition and considerably faster action compared to conventional on-site inspection. Inevitably, SHM systems suffer from the same problems as the structure itself: Not only does the structure age over time, but the measurement system itself is also subject to environmental aging. To ensure robust SHM, this paper presents a concept that combines optimal sensor placement with a hardware redundancy approach to provide the most efficient, durable, and reliable SHM for structures. For this purpose, a small-scale experiment is conducted in a climate chamber equipped with various sensor types (accelerometers and strain gauges). The different levels of sensor integration allow reliable detection of sensor errors in the raw data signal. Correlation matrices are used to decide which sensor functions are most reliable at what point in time and whether sensor replacement in the SHM system is required. Furthermore, data normalisation is applied to the raw data signal to compensate for the influence of environmental conditions (ECs) such as temperature and humidity. The proposed concept is validated on the small-scale dynamically excited experiment and shows promising results to operate reliable SHM systems for several decades in the future.
Drive-by modal identification of railway bridges via on-board measurements from a single railroad vehicle
Authors:   ETH Zürich, Stoura Charikleia ,   ETH Zürich, Dertimanis Vasilis K. ,   ETH Zürich, Chatzi Eleni
Published: June 2024

The global railway network spans over one million kilometers of tracks, and this extensive infrastructure is set to expand even further. The objective is to promote rail transportation as an environmentally sustainable solution to address the growing demands of mobility. A significant portion of these tracks spans across bridge structures, which must also accommodate the rising demands for mobility and increased travel speeds, placing additional demands in terms of imposed loads. At the same time, bridges worldwide suffer from aging, leading to the deterioration of their structural integrity. Such combined effects necessitate monitoring the structural health of bridges to ensure the quality and safety of railway networks by detecting possible alterations in their response characteristics. Traditional Structural Health Monitoring (SHM) techniques use stationary sensors affixed to the bridge structure, enabling the direct assessment of the collected data. While such approaches are reliable, they present limitations in the comprehensive inspection of multiple railway bridges within a network. As an alternative, mobile vibration-based sensing, utilizing sensors on passing trains, presents the potential to gather data from multiple railway bridges using only a few sensors installed on board trains. Furthermore, operating the sensor-equipped trains at frequent intervals allows for collecting continuous data, offering valuable insights into the ongoing deterioration of bridges. In light of this, our work proposes a model-based methodology for extracting the modal frequencies of bridges based on acceleration data collected from traversing trains. Our approach hinges on Kalman filtering for estimating the state and input of the train and employs a subspace identification method to ascertain the frequencies of the bridge. The ultimate goal is to develop a drive-by bridge monitoring scheme that accommodates the assessment of the remaining lifespan of bridges as well as timely repairs in the event of damage, ensuring the safety and reliability of rail transportation.
Structural Health Monitoring of Composite Leaf Spring Using Guided Elastic Waves
Authors:   Fraunhofer Institute for Ceramic Technologies and Systems IKTS, Savli Enes ,   Fraunhofer Institute for Ceramic Technologies and Systems IKTS, Tschöke Kilian ,   Fraunhofer Institute for Ceramic Technologies and Systems IKTS, Schubert Lars ,   INVENT GmbH, Huxdorf Oliver ,   INVENT GmbH, Sonnemann Tjark ,   INVENT GmbH, Wunderle Fabian
Published: June 2024

The integration of advanced materials, such as fiber-reinforced composites, has transformed numerous industries by offering lightweight and high-performance solutions. In the context of automotive applications, these composites enable producing more efficient vehicles. Nevertheless, there are still uncertainties regarding operational safety. Potential emergence of barely visible damages such as fiber fracture, delamination, and debonding accounts for a limited lifespan of the composite components. To address these concerns, permanent monitoring offers a compelling solution, enabling the early detection of damage, thus reducing maintenance costs and the risk of failures regarding human life. In this sense, guided waves based Structural Health Monitoring (SHM) has demonstrated promising results. However, it has so far primarily remained in laboratory settings with limited real-life applications due to some challenges, such as the costs involved and the transparency of results.

This contribution focuses on the development of permanent monitoring of a novel spring leaf component manufactured of a carbon and glass fiber-reinforced composite. To establish permanent structural monitoring, piezoelectric transducer arrays have been integrated into the composite spring leaf structure during manufacturing. The placement of these transducers is determined through sensitivity analysis, considering ultrasound wave propagation characteristics. After a series of measurements, state-of-the-art damage identification methods customized to designed SHM setup are employed and compared. Outcomes are discussed with an emphasis on the efficient detection of narrow body composite structures. Additionally, damage detection evaluation of low-sensitive areas in such structures are explored in particular.

While reliability assessment and compensation of environmental effects have been left for future research, this study presents tested methods and results, aiming to contribute to the advancement of SHM techniques in practical applications.
Incorporating specialist engineering knowledge into IE models to facilitate enhanced SHM-based transfer learning within PBSHM
Authors:   Sheffield University, Brennan Daniel S. ,   Queen's University Belfast, Kent Connor ,   Queen's University Belfast, Hester David ,   Sheffield University, Worden Keith ,   Queen's University Belfast, O'Higgins Connor
Published: June 2024

Population-based Structural Health Monitoring (PBSHM) aims to gather additional knowledge on a structure's health by monitoring multiple structures (the population), compared to the level of knowledge available when utilising only a single structure's data. Before effective transfer learning can occur, a similarity between the structures must be established to prevent negative transfer.

Irreducible Element (IE) models, combined with graph theory, are the vehicle used within PBSHM to facilitate this process. Recent research has introduced the Canonical Form (CF) which facilitates a singular IE model per structure; however, detailed IE models solely rely on predefined rules to reduce down to the CF, and do not require the incorporation of specialist engineering knowledge within these reductions. In particular, the specific SHM problem of interest may constrain the reduction.

This work aims to enhance the capabilities of IE models by allowing authors to incorporate their specialist engineering knowledge into an IE model. A case study of a steel truss pedestrian bridge is presented, focusing on incorporating the engineering knowledge utilised in its design, construction, and maintenance within an IE model.

By enabling authors to leverage their domain expertise, a more comprehensive understanding of structural similarities can be achieved, thereby enhancing transfer learning potential in PBSHM. This paper outlines the methodology and provides insights into the application of domain knowledge in IE models, showcasing its potential benefits for the field.
Development of Dispersion Calculator SAFEDC of Guided Wave with Complex Material and Waveguide
Authors:   Harbin Institute of Technology, WANG Xin ,   Harbin Institute of Technology, Liu Menglong ,   Xiamen University, WANG Kai
Published: June 2024

The propagation characteristics of guided waves (GW) are related to both the material, loading and geometry parameters. Correct understanding of GW is the key step towards further GW-based structural health monitoring. A dispersion calculator of GW propagating in the complex geometry with viscoelastic and anisotropic material parameter in the open waveguide is developed based on the semi-analytical finite element (SAFE) method and perfectly matched layer (PML). By assuming the simple harmonic motion along the propagation direction and adopting the mesh discretization in the cross section, SAFE features the capability of GW calculation in the complex waveguide at only a cost of limited calculation resources. Hence, besides the capabilities of the most off-the-shelf tools focusing on the analytical solution of GW in simple plate or pipeline, the developed algorithm further offers the solution of GW in open waveguide with laminates of arbitrary ply stacking sequences and in arbitrarily complex cross section. Quadratic one-dimensional and triangular elements are adopted to automatically discretize the plate structure and complex cross section, respectively. The obtained results of GW in plates are in a good agreement with the results obtained with the off-the shelf-tools that adopt analytical derivations, and show stronger robustness of mode searching and sorting by avoiding the complex root-finding procedure required in the analytical derivation. The most GW features, including phase velocity, group velocity, attenuation, wave structure in terms of displacement, stress, and strain, and animation of wave propagation are directly calculated. All these functions are integrated and updated in the previously developed freeware SAFEDC, whose 1st version SAFEDC v1.0 was published in September 2023.
Acoustic emission monitoring for industrial pressure vessels and infrastructure
Authors:   TÜV Austria Services GmbH, Prokofyev Mikhail ,   TÜV Austria Services GmbH, Marihart Heribert ,   TÜV Austria Services GmbH, Lackner Gerald
Published: June 2024

Acoustic emission (AE) is a phenomenon that occurs in materials that are exposed to stress. Defects in the material, such as cracks can grow and release energy in the form of a mechanical wave that propagates through the material. With the help of piezoelectric sensors, the mechanical wave is converted into an electrical signal. The signals are recorded by the measuring system and are available for further processing. Acoustic emission as a method that records and processes the incoming signals in real time is therefore very suitable for 24/7 long-term structural health monitoring. With new approaches in data processing, acoustic emission can be moved from the classic periodic test approach to an online monitoring process. The in-service monitoring brings measuring challenges with it, like application on hot surfaces and inside explosion hazard areas. New developments in hardware and software provide tailor-made solutions for a variety of monitoring tasks. The use of artificial intelligence algorithms helps to improve the analysis of measurement data and guarantees safe operation of pressure equipment and infrastructure around-the-clock. Some examples of continuous monitoring are shown on pressure vessels from the industry sector and on infrastructure buildings e.g., steel bridges. Also, a new lean system approach in structural health monitoring with acoustic emission is presented.
Constraints of local stiffness adaption for reduced-order models of large-scale steel bridges
Authors:   Technische Universität Braunschweig (TU), Brenner Christoph ,   Technische Universität Braunschweig (TU), Thiele Klaus ,   Technische Universität Braunschweig (TU), Unglaub Julian
Published: June 2024

Large and complex structures, such as bridges, are subjected to a variety of environmental factors and loads over time, leading to evolving structural states. To assess their current condition and plan predictive maintenance, Digital Twins enriched with Structural Health Monitoring (SHM) data are essential. However, for large-scale structures, the creation of physics-based Digital Twins necessitates reduced-order modeling to ensure efficient adaptation to SHM data. Many of these methods for structural damage assessment rely on localized stiffness reduction to characterize discrete damage states. The Static Condensation Reduced Basis Element (SCRBE) method is a promising approach for model order reduction by decomposing the structure into parametric components. However, SCRBE's effectiveness and the accuracy of the Digital Twin depend on an appropriate partitioning of the structure into to components with reduced stiffness. This paper systematically investigates the process and the constraints of component selection for steel bridges with discrete damages, considering factors like numerics, sensor placement and complex geometries. A comprehensive analysis is conducted using a real bridge case study augmented with synthetic measurement data representing damage scenarios. The results reveal both challenges and the potential of the SCRBE method for Digital Twins. A systematic approach for component selection within the SCRBE framework is proposed, specifically tailored for real-world application scenarios. This structured methodology facilitates the efficient and adaptive development of Digital Twins for large, intricate structures, such as steel bridges.
The Damage Process Monitoring in Structures with Quasi-Brittle behavior in the perspective of Complex System.
Authors:   Federal University of Rio Grande do Sul (UFRGS), Iturrioz Ignacio ,   Federal University of Rio Grande do Sul (UFRGS), Tanzi Boris Nahuel Rojo ,   Federal University of Rio Grande do Sul (UFRGS), Colpo Angelica ,   Federal University of Pampa, Friedrich Leandro ,   Politecnico di Torino, Cesar Ediblu ,   Politecnico di Torino, Lacidogna Giuseppe
Published: June 2024

The damage process in structures with quasi-brittle behavior is characterized by typical phenomena such as Size effect sensibility, the interaction of micro-fissures, and the transition of the continuum to discontinuation throughout the so-called localization effect. Structures are generally built with concrete, rocks, ceramic, and composite structures. This type of material could be considered. There are many approaches to modeling the mechanical behavior of this type of Material/Structure, but our strategy of simulation, a version of the Discrete Element Model, is aligned with the perspective of Prof. Krajcinovic; from his point of view, the random nature of the material is a key aspect that must due to consider. To address this problem, the Acoustical Emission Technique (AET) could also be useful to characterize the damage process. The combination of the signal captured in a different place in the structure determines the spatial and temporal distribution of the events. During the damage process. With this information, it is possible to compute global parameters and their evolution during the damage process of the structure, which could be used as precursors to local or global damage. There are several global indexes, and
to study their evolution, let us know when the structure will be close to global or local collapse. Let's consider that the failure of the structure could be seen as a phase transformation phenomenon. It is possible to understand this problem using the Renormalization Group Theory proposed by Wilson and the basic ideas of Anderson related to a Complex System. In this context, in the present work, we consider some applications related to the characterization of the Damage Process of Quasi-Brittle structures/materials carried out for our research group, searching for the link between these results and the ideas of Wilson and Anderson.
Keywords: Acoustic Emission Techniques, Discrete element Method, Critical Phenomena.
NDT Data-Supported Structural Reassessments based on Regression Relations in Measuring Data Analysis
Authors:   BAM Federal Institute for Materials Research and Testing, Bahrami Eskandari Ardalan ,   BAM Federal Institute for Materials Research and Testing, Küttenbaum Stefan
Published: June 2024

Non-destructive Testing (NDT) yields valuable data on actual structural elements, supporting the assessment of existing infrastructures while minimizing additional structural damage caused by inspections. Simultaneously, the uncertainty in measurement, which quantifies the quality of measurement results, plays a crucial role in decisions aimed at, e.g., optimizing maintenance strategies, rehabilitation works and Structural Health Monitoring (SHM) implementations. The Guide to the Expression of Uncertainty in Measurement (GUM) framework has been already applied to non-destructive concrete testing. This necessitates an obligatory process in which extensive measurements with strictly limited scopes need to be conducted by expert personnel.
This study aims to simplify and optimize existing NDT data analysis procedures by employing regression analysis, enabling the detection of structural features. The regression analysis yields a modifier for determining the correct depth of an object within the structural element as well as an intercept in the linear input-output formulation, with time of flight serving as the variable and wave velocity as its slope. The method is validated through laboratory experiments, including the use of an ultrasonic measurement system. The introduced method provides unbiased and accurately measured results, while the measurement data can be uncorrelated. Major advantages are faster computations, a wider scope and a notable reduction in the amount of needed information about the measuring process.
Furthermore, the study illustrates how the regression relation approach, in combination with ultrasonic data describing the inner structure of a concrete element, can effectively support the reassessment of bridges in operation. To achieve this, the measurable positions of the resisting longitudinal tendons of a bridge structure are varied to investigate the displacement change under dynamic service loads. The findings reveal that the application of the proposed NDT analysis method, with its increased practicability, can enhance the efficiency of NDT data-supported structural reassessments in different scenarios.
Machine Learning-Enabled Acoustic Emission Signal Processing for real-time crack growth monitoring in heterogeneous structures
Authors:   University of Bourgogne, Hamdi Seif Eddine ,   Université de Bretagne Occidentale, Diakhate Malick ,   University of Bourgogne, Kneip Jean-Christophe ,   ISET Sfax, Hamdi Zied ,   University of Bourgogne, Jouvard Jean-Marie
Published: June 2024

Heterogeneous structures pose unique challenges for structural health monitoring, demanding advanced techniques for early detection of crack propagation. Acoustic Emission (AE) is a non-destructive testing technique that relies on the detection and analysis of stress-induced high-frequency acoustic waves. It is commonly used for identifying and monitoring the development of cracks, defects, or other structural anomalies. However, raw AE data is often noisy and contains background noise and interference, which make them not suitable for reliable real-time crack monitoring applications. This study integrates AE data with machine learning algorithms to provide a proactive and accurate system for continuous crack propagation monitoring. The performance of the proposed approach for features extraction to derive relevant information from the AE signals, data labeling for training machine learning models, and the deployment of these models for real-time monitoring, are discussed.
Damage Identification at Off-Grid Locations on Plate-like Structures using Frequency-Coupled Group Lasso
Authors:   KU Leuven (Catholic University of Leuven), Dwek Nathan ,   KU Leuven (Catholic University of Leuven), Janssens Dennis ,   KU Leuven (Catholic University of Leuven), Kirchner Matteo ,   KU Leuven (Catholic University of Leuven), Deckers Elke
Published: June 2024

Localizing elastic wave scatterers is the working principle behind a family of model-based approaches to detect and localize damage in plate-like structures. These approaches take advantage of a physical model of propagation and scattering, which enables an efficient data-driven strategy to decompose the effect of damage on the structure. This strategy consists in measuring on the healthy structure the responses to excitations at predefined candidate damage locations, and then in building from these a dictionary that maps damage at candidate locations to changes in responses. At inspection time, inverse methods are used to identify damage, based on this dictionary and the responses measured on the structure under test. One such inverse method is Frequency-Coupled Group Lasso, which exploits the inherent sparsity of damage to reduce the number of required measurements.

This paper examines the performance of the above-described approach in the “off-grid” case, that is, when the damage is not located at one of the predefined candidate locations. This is an important extension because real-world damage can occur at a large or infinite number of locations, but the number of candidate locations is limited by measurement and/or computing cost. Off-grid cases are simulated by removing from the dictionary the entries corresponding to the actual damage locations and then running Frequency-Coupled Group Lasso with this incomplete dictionary. The results are then compared to the on-grid case, when the actual damage locations are included in the dictionary.

Damage identification is demonstrated on a 600mm by 600mm composite plate with up to 6 damage locations, using a single accelerometer and 7 hammer excitations. Off-grid performance is evaluated for 4 different accelerometer locations, for SNRs ranging from 30 to 10dB, and compared to on-grid performance. The proposed damage identification approach is shown to remain robust in the off-grid case, further proving its real-world applicability.
Damage Detection In Concrete Beams Strengthened With Frp Using Principal Component Analysis And K-Means Clustering
Authors:   Technical University of Madrid (UPM), Perera Ricardo ,   Technical University of Madrid (UPM), Huerta Consuelo ,   Universitat de Girona, Baena Marta ,   Universitat de Girona, Barris Cristina
Published: June 2024

Fiber reinforced polymers (FRP) strengthening systems have been considered as an effective technique to retrofit concrete structures and their use nowadays is more and more extended. However, one of their main disadvantages is a possible brittle failure mode provided by a sudden debonding of the FRP. Digital image correlation (DIC) has been used in the past for the detection of abnormalities from surface digital images. In this work, an efficient computer-aided diagnosis (CAD) system for damage identification at its earliest stages for this type of FRP strengthening is developed. For it, a clustering classifier is implemented from the cells of the strain maps built with a DIC system. Additionally, to optimize the procedure, principal component analysis (PCA) is used for feature selection.
Enhancing Landslide Prediction Through Advanced Transformer-Based Models: Integrating SAR Imagery and Environmental Data
Authors:   University of Naples Federico II, Khalili Mohammad Amin ,   University of Naples Federico II, Palumbo Simone ,   Kharazmi University, Madadi Sadegh ,   University of Naples Federico II, Bausilio Giuseppe ,   K. N. Toosi University of Technology (KNT), Voosoghi Behzad ,   University of Naples Federico II, Calcaterra Domenico ,   University of Naples Federico II, Di Martire Diego
Published: June 2024

Landslides, a significant natural hazard driven predominantly by rainfall infiltration, pose a continuous threat to the terrain, buildings, and ecosystem of Caiazzo, located in southern Italy (Caserta). This research undertakes a novel approach to predict and understand the complex patterns of landslide deformations in this region. Our study leverages the advanced capabilities of COSMO-SkyMed satellite imagery, integrating a comprehensive dataset that includes factors such as elevation, slope, Topographic Wetness Index, Stream Power Index, geology, flow direction, curvature, Normalized Difference Vegetation Index, and several other relevant indices. We employ Transformer-based models, renowned for their effectiveness in capturing long-range dependencies within sequential data. The Transformer models in our study are adept at analyzing the temporal sequences of environmental factors and their intricate interactions, thereby offering a more nuanced understanding of the temporal patterns leading to landslides. These Transformer models are designed to process the entire sequence of satellite data obtained by the Coherent Pixels - Temporal Phase Coherence within the SUBSOFT package to ensure data integrity and precision, encompassing 132 images in ascending geometry and 143 in descending geometry, spanning from 2013 to 2021. By doing so, they efficiently identify critical patterns and dependencies over time, such as the interplay between rainfall events and subsequent soil and vegetation changes. The models are fine-tuned to our specific dataset, ensuring high precision and accuracy in landslide deformation prediction. Our evaluation metrics, including Mean Absolute Error, Root Mean Square Error, and R^2 score, demonstrate the superior performance of the Transformer-based approach, with significant improvements over the conventional Deep Learning model. The visual correlation of our predictions with actual landslide occurrences further corroborates the effectiveness of this method. This transformative approach not only enhances our understanding and predictive capability for landslides in Caiazzo but also sets a benchmark for landslide prediction in geologically vulnerable regions worldwide.
GW-MUSIC focusing algorithm with high accuracy for composite damage diagnosis
Authors:   Nanjing University of Aeronautics and Astronautics  (NUAA), Zheng Fei ,   Nanjing University of Aeronautics and Astronautics  (NUAA), Yuan Shenfang ,   Nanjing University of Aeronautics and Astronautics  (NUAA), Qiu Lei ,   Nanjing University of Aeronautics and Astronautics  (NUAA), Ren Yuanqiang
Published: June 2024

The Guided Wave (GW)-based Multiple Signal Classification (MUSIC) method for damage imaging in composite structures offers high-resolution and flexible sensor array placement, demonstrating considerable potential for precise damage localization. However, the application to real aircraft composite structures, characterized by complex structural features, presents substantial challenges. These include complex boundary reflections, pronounced anisotropy, and diminished scattered signals from damage sites. This paper presents a novel single-peak anisotropy-compensated MUSIC method for damage imaging of complex composites. This method, designed for improved localization accuracy, combines a self-excited calibration strategy to mitigate anisotropy with single wave peak focusing to amplify signals and reduce boundary reflections. Experimental validation on a complex composite wing structure demonstrated significant enhancements in localization precision, affirming the method's efficacy.
Comparative Study on PSD and HHT Techniques for Condition Assessment of Steel Truss Bridge Using Acceleration Data
Authors:   Indian Institute of Technology Madras (IITM), Gupta Vaibhav ,   Indian Institute of Technology Madras (IITM), Umakanthan Saravanan
Published: June 2024

Bridge structures deteriorate over time due to factors like corrosion, fatigue, aging of materials, and unexpected natural or accidental events. Consequently, the vibrations measured in these structures exhibit time-varying and non-stationary characteristics. Additionally, non-linearities are inherent in these structures due to material behavior, boundary condition changes, and joints or connections. Analysis of non-linear and non-stationary structural vibration data is necessary to extract information on the condition of the bridge.

Most methods assessing bridge conditions from acceleration responses rely on the Power Spectral Density (PSD) which is based on the Fourier Transform. However, the accuracy of the Fourier transforms for non-linear and non-stationary signals limits its application potential. The Hilbert–Huang Transform (HHT) has emerged as an alternative for handling these non-stationary signals due to its time-frequency-energy representation.

This study provides a comparative study of both techniques by utilizing the data obtained from continuously monitored Pamban Bridge. This steel truss bridge was fitted with accelerometers to record bidirectional acceleration time histories at twenty bottom node points on the bridge. This study examines the continuous variations in the frequency component by both methods for 136 days. A linear best-fit trendline is obtained for the frequency parameters at different sensor positions. The slope and intercept values of this linear best fit trendline is further studied.

The HHT showcases various evolving dominant frequency components over time in contrast to the PSD representation. The frequency spectrum from the HHT displays less variation over time at each sensor position compared to the predominant peak value derived from the PSD of the acceleration signal. The spatial variability in the predominant frequency obtained from HHT is also less than the PSD. Therefore, the ability of HHT to discriminate damages needs further investigations.
Transfer learning via intermediate structures
Authors:   Sheffield University, Dardeno Tina A. ,   University of Cambridge, Bull Lawrence A. ,   Sheffield University, Dervilis Nikolaos ,   Sheffield University, Worden Keith
Published: June 2024

Recent advances have been made in the field of population-based structural health monitoring (PBSHM), which seeks to share valuable information across a population to improve inferences regarding the health states of the members. However, enabling knowledge transfer between structures with highly disparate features (i.e., heterogeneous populations) is an ongoing challenge. The current work proposes a technique for knowledge transfer within a heterogeneous population, via intermediate structures that help bridge the gap in information between the structures of interest. The transformation from the source to the intermediate and target domains is based on geodesic flows, to incrementally map between changes in geometric and statistical properties. The proposed technique is demonstrated using simulated lumped-mass systems.
SGAM – Smart Geotechnical Asset Management
Authors:   NHAZCA S.r.l., Di Renzo Maria Elena ,   NHAZCA S.r.l., Belcecchi Niccolò ,   NHAZCA S.r.l., Brunetti Alessandro ,   NHAZCA S.r.l., Chessa Andrea ,   Sapienza University of Rome, Mazzanti Paolo ,   NHAZCA S.r.l., Scancella Stefano ,   NHAZCA S.r.l., Valerio Emanuela
Published: June 2024

SGAM (Smart Geotechnical Asset Management) is a service provided through a web-platform running the cloud-based Software as a Service (SaaS) or via API to connect external systems with our service.
It represents an innovative and semi-automatic decision-making supporting system for asset management and predictive maintenance purposes to improve the financial resilience of assets, taking advantages of innovative data-fusion algorithms and satellite Earth Observation (EO) technologies (e.g., A-DInSAR and PhotomonitoringTM).
SGAM focuses on the risks related to structures and infrastructures, analysing their interaction with the local geological and environmental context, by automatically evaluating the direct or potential future interferences with geohazards (e.g., landslides, floods, fires). The service aims to support Decision Makers in defining and prioritizing the maintenance operations based on the asset vulnerability and loss scenarios. This goal is reached through the integration of the big amount of archived and newly acquired EO data, the available worldwide and regional geodatabases, the information collected by field surveys or other ground-based Structural Health Monitoring and Geotechnical/Geomorphological Monitoring systems, and the proven scientific know-how and experience of the proponent team on geohazards effects in the civil engineering field.
SGAM is also designed to be able to implement satellite products from new future space missions, ensuring a vision oriented to the maximum exploitation of the most recent technologies. These components are managed and analysed by specific Process Automation solutions, based on Artificial Intelligence, allowing for a redundant first-level risk-based priority action assessment for cost-effective and optimized maintenance investments.
On Conserving Privacy in Structural Health Monitoring
Authors:   Sheffield University, Windus-Smith Tom R. ,   Sheffield University, Worden Keith ,   Sheffield University, Rogers Timothy J.
Published: June 2024

The ability to make informed decisions with regards to maintenance and service life is critical across many high-value industries. Making accurate predictions about the condition of assets is a key goal and one supported by structural health monitoring (SHM) strategies. To help support this aim, the development of a population-based approach to structural health monitoring (PBSHM), has been making progress in recent years. This solution for dealing with structures, aims to group them together in “populations” and transfer information between individuals, within the population. PBSHM necessitates the sharing of information between structures, which may not all be controlled by a single stakeholder. Therefore, a concern arises that PBSHM may compromise “private” information between different organisations or individuals if their assets are included in a PBSHM scheme. This work will seek to demonstrate a solution to this problem. Differential Privacy is a probabilistic privacy regime that retain population trends in a dataset whilst keeping individual data private from others by the injection of random noise into the data. In this context, privacy is a one-way mapping which obscures sensitive information in a dataset, in a non-reversible manner; as opposed to encryption which may be “broken”. Being differentially-private gives a bound on the amount of information which is ever recoverable. Being able to protect discriminating features of different structure designs from other parties will promote and accelerate the adoptions of PBSHM strategies in industrial settings, where protecting information is a key concern. This paper will demonstrate the application of differential privacy to a simulated homogeneous population of structures.
Structural evaluation of urban bridges in Amsterdam through InSAR-based displacement data
Authors:   Delft University of Technology (TU Delft), Macchiarulo Valentina ,   Delft University of Technology (TU Delft), Kuai Hao ,   Delft University of Technology (TU Delft), Karamitopoulos Pantelis ,   University of Houston, Milillo Pietro ,   Delft University of Technology (TU Delft), Giardina Giorgia
Published: June 2024

Thousands of bridges worldwide face growing risks due to aging materials, increased traffic loads, and climate change-induced weather extremes. Managing these assets is financially demanding, necessitating prioritisation strategies for interventions. Consequently, innovative approaches are urgently required to evaluate the structural conditions of these bridges continuously and regularly. Recent advancements in space-borne Interferometric Synthetic Aperture Radar (InSAR) technology offer cost-effective remote monitoring capabilities, ensuring extensive coverage and high spatial resolution. Multi Temporal (MT) InSAR techniques enable the reconstruction of millimetre-scale deformation measurements for a large number of assets, opening opportunities for long-term regional-scale monitoring of bridge deformations. However, a major challenge in utilising MT-InSAR-based displacement data operationally is that MT-InSAR analysis reconstructs only the projection of displacements along the satellite Line of Sight (LOS) direction. Due to the typical availability of only two satellite viewing geometries, in most cases the three-dimensional displacement field cannot be fully reconstructed. Consequently, without accounting for the anticipated motion of a given structure and its alignment with respect to the satellite flight path, the actual asset movement is likely to be underestimated, leading to erroneous interpretation. In this paper, we propose a method using the bridge typologies and their associated likely failure mechanisms to derive assumptions regarding expected displacement directions. Then, the information on bridge alignments with respect to the satellite flight direction is used to assess the MT-InSAR sensitivity to the expected displacement directions and define ad-hoc damage indicators. We tested the proposed method on urban bridges in Amsterdam, the Netherlands, using deformation measurements derived from TerraSAR-X data spanning 2011 to 2020. Findings have potential to enhance current procedures for the structural evaluation of bridges.
A framework for structural vulnerability assessment with consideration of seismic uncertainty
Authors:   Tongji University, Xiong Qingsong ,   Tongji University, Kong Qingzhao ,   The Hong Kong Polytechnic University, Xia Yong
Published: June 2024

In recent decades, performance-based earthquake engineering (PBEE) has been dedicated to the structural vulnerability assessment (SVA) as a critical research area for seismic decision-making. Among the various approaches to SVA, the classical linear regression method (LRM) has gained wide and prominent use. However, the conventional LRM relies on the quantile regression method (QRM) to approximate a deterministic probability density distribution (PSD) of structural fragility against a specific intensity measure (IM) associated with an earthquake. This approach may lack credibility when evaluating a newly occurred seismic event due to its neglect of the seismic uncertainty of stochastic ground motion. To address this concern, this present study introduces a meticulously designed framework that integrates physics-based and machine learning models to probabilistically assess seismic fragility while accounting for seismic uncertainty. Additionally, novel fragility hyperparameters are estimated within this framework. The study provides a detailed elucidation of the underlying design philosophy and the mechanism of translation. In order to validate the proposed method, both the LRM and the new framework are applied to a six-story frame structure, utilizing a novel fully-automatic batch processing approach that combines APDL and coding languages for structural analysis. The comprehensive validation cases convincingly confirm the exceptional capability and significant advantages of the proposed framework in achieving rapid probabilistic seismic fragility assessment, which is of paramount importance to modern PBEE.
Low-velocity impact localization on a full composite wing of UAV using fiber optic sensors under operating environments
Authors:   Korea Aerospace Research Institute, Jang Byeong-Wook
Published: June 2024

In this paper, low-velocity impact localization algorithms were suggested using fiber optic sensing system. In order to obtain high frequency signals due to low-velocity impact, a commercial high speed fiber Bragg grating (FBG) sensing system (a sampling speed of 100 kHz) was adopted. The test article is a full-scale composite wing box structure as the main wing of an unmanned aerial vehicle. The multiplexed FBG sensor array was attached on the lower surface of the upper skin. Low-velocity impact experiments were performed to obtain the impact signals for the reference data set. The reference data set is composed of the impact signals from the impacts on the reference points.
The suggested algorithm in this study is based on the comparisons between impact signals and the reference signals in the database set. The main idea is to determine the reference point which has the most similar data set as the impact location. This idea was validated from the experiments of the non-reference points. As the next step, operational environments were considered to enhance the reliability of the suggested algorithm. The considered environments are the static loads and vibrations. These days, most of UAVs use the electric motors and propellers as the main propulsion system. Thus, harsh vibrations inherently occur and affect the performance of impact localization algorithms. From the experiments under these operating environments, it can be checked that the suggested algorithm has sufficient robustness to such environments.