1,433 research outputs found
On the development of a population-based SHM strategy for aerospace structures
No full textL'abstract è presente nell'allegato / the abstract is in the attachmen
Towards a Population-based approach for dynamic monitoring of underground structures
No full textUnderground structures play an increasingly important role in transportation networks and urban areas. Thus, ensuring their structural integrity is essential for safety and operational efficiency. Among the Structural Health Monitoring (SHM) methods already proposed for this type of structure, only a few studies propose vibration-based analyses. Furthermore, data-driven monitoring of infrastructure networks would require the installation of several sensors on each structure, which may be prohibitively expensive for local administrations. The lack of sufficiently large and comprehensive datasets can be addressed through Population Based Structural Health Monitoring (PBSHM). The PBSHM approach, recently proposed for bridges, wind turbines and aircraft, adopts transfer learning algorithms to share damage-state knowledge among similar structures and establish a large-scale monitoring system when only a few data are available. This study investigates the potential extension of knowledge sharing to underground structures, such as metro tunnels, by analysing feasible features and damage identification strategies and exploiting the numerical results of two dynamic finite element simulations to provide a domain adaptation case study
Towards a Population-Based Approach for Dynamic Monitoring of Underground Structures: A Numerical Study on Metro Tunnel Models
No full textUnderground structures are becoming increasingly vital components of modern transportation networks and urban systems, making their structural integrity a critical factor for safety and operational reliability. However, despite considerable progress in Structural Health Monitoring (SHM), the application of data-driven and vibration-based strategies to underground infrastructures remains an open and under-explored field, often because of limited data availability. Population-Based Structural Health Monitoring (PBSHM) offers a promising pathway to overcome this challenge by leveraging transfer learning to share diagnostic knowledge among similar structures. This study investigates the feasibility of extending the PBSHM paradigm to underground infrastructures, with a particular focus on a metro tunnel application. Through dynamic finite element simulations, relevant vibration features are identified, and damage detection strategies based on transmissibilities and cross-correlation functions are evaluated. The numerical results show that transmissibility-based indicators enable accurate damage localisation along the tunnel lining, even under noisy conditions. In contrast, cross-correlation features exhibit more limited performance in some configurations. Building on this evidence, the transmissibility-based damage indicator is subsequently embedded within the PBSHM framework and used as a transferable feature between tunnel models, achieving reliable damage detection in a second tunnel with heterogeneous characteristics, with F1 scores exceeding 80% for all considered damage severities and above 94% for the most critical case, thereby highlighting the potential of knowledge transfer for large-scale underground networks
Using the inverse finite‐element method to harmonise classical modal analysis with fibre‐optic strain data for robust population‐based structural health monitoring
No full textVibration-based approaches to structural health monitoring (SHM) gained increasing significance for assessing the behaviour of existing structures because of their non-intrusive nature and high sensitivity to damage. However, data availability often limits the application of SHM approaches. The population-based structural health monitoring (PBSHM) theory addresses this challenge, enhancing diagnostic inferences by sharing knowledge across a population of similar structures. In real-life scenarios, sharing data from distinct structures requires dealing with results obtained with different experimental setups, multiple sensors, input choices and acquisition systems. Therefore, it is crucial to harmonise various features to achieve accurate and reliable results. The present study presents the results of a classic experimental modal analysis (EMA) using scanning laser Doppler vibrometer (SLDV) measurements and a strain-based EMA conducted using high-definition distributed fibre-optic strain sensors. The experimental case study of a laboratory-scale steel aircraft subjected to specific operating and damage conditions is introduced, allowing for a comprehensive discussion of the features extracted from the two EMA techniques, which can also be generalised to structures within different domains. This research highlights the advantages and limitations of fibre-optic-based EMA compared to classic methods, as fibre-optic strain sensors offer a cost-effective alternative to accelerometers or SLDV for dynamic testing. Furthermore, the feasibility of employing the inverse finite-element method (iFEM) in the dynamic domain is investigated. This method can estimate the whole displacement field of a structure from a limited number of strain values, thus harmonising strain measurements with the SLDV measurements. By analysing the features extracted from different EMA techniques within the PBSHM framework, this study contributes to advancing the understanding and application of the PBSHM approach in diverse experimental scenarios, laying the foundation for further investigation of features and adequate methods for sharing damage-state knowledge across a population of structures
Novelty detection across a small population of real structures: A negative selection approach
No full textVibration-based Structural Health Monitoring (SHM), exploits a variety of approaches for novelty detection. In particular, many data-based methods try to recognise patterns by exploiting analogies with the human body's natural defences at a cellular level. These algorithms fall within the Artificial Immune System (AIS) class and can be chosen, according to their peculiarities, to solve specific problems in diverse application areas. This study investigates the damage-detection process in different operational conditions, obtained by applying structural modifications to a laboratory-scale aeroplane, which follows the geometric features of the GARTEUR benchmark project. Damage identification is performed by exploiting the Negative Selection Algorithm (NSA), already applied by some of the authors on numerically-simulated case studies, and chosen for its capability of self/non-self discrimination under varying operational or environmental conditions. The research is expanded by using sparse autoencoders for feature dimensionality reduction. The method is applied to an experimental dataset acquired via Scanning Laser Doppler Vibrometer (SLDV) measurements, to identify consistent damage-sensitive features from the frequency response functions, and to obtain a reliable fault-detection performance
On the influence of structural attributes for assessing similarity in population-based Structural Health Monitoring
No full textThe viability of many machine learning methods within Structural Health Monitoring (SHM) is often limited by the lack, or the incompleteness, of the data required for implementing these algorithms.
Indeed, learning a data-based SHM predictive model usually requires the dynamic response availability for undamaged and damaged states, and the assumption that both training and test data refer to the same domain.
In this framework, the population-based approach to Structural Health Monitoring (PBSHM) aims at improving the performance and the robustness of diagnostic inferences, exploiting the transfer of damage-state knowledge across a population of structures.
However, sharing these data produces a meaningful inference only if the structures, and their datasets, are sufficiently similar.
Therefore, an initial phase of similarity assessment becomes essential before being able to apply transfer learning algorithms.
This phase shows which structures are suitable for knowledge sharing, if any, reducing the possibility of negative transfer.
Some distance metrics have been proposed, exploiting abstract representations of structures, such as Irreducible Element (IE) models and Attributed Graphs (AGs).
Although these metrics can consider the structure attributes, many performed comparisons mainly concern structural topology.
This study aims at broadening the application of similarity assessment, focussing on the geometrical and material differences in the distance metrics.
Therefore, a heterogeneous population of laboratory-scale aircraft is analysed.
These structures predominantly follow the geometry of a benchmark study conducted by the Structures and Materials Action Group (SM-AG19) of the Group for Aeronautical Research Technology in EURope (GARTEUR).
The IE models of these aircraft are produced. Subsequently, Graph Matching Network (GMNs) are used to determine the similarity matrix.
The structures in the Garteur population are topologically homogeneous, which enables a more accurate investigation of how attributes can influence distance metrics.
This paper constitutes the first step in the Garteur structures population investigation
On the Influence of Structural Attributes for Transferring Knowledge in Population-Based Structural Health Monitoring
No full textThe recently proposed theory of Population-Based Structural Health Monitoring (PBSHM) aims at improving diagnostic inferences, by sharing damage-state knowledge across a population of structures via transfer-learning algorithms - specifically domain adaptation.
Before applying these algorithms, the similarity between structures, or substructures, should be evaluated. This assessment helps prevent negative transfer, ensuring better performance and higher robustness of data-based SHM.
When structures are sufficiently similar, different transfer-learning strategies can be applied, according to the original features and the specific case study.
In this framework, structural attributes play a crucial role, especially for heterogeneous populations in which the main differences can be caused by material properties, geometry or dimensions.
Therefore, investigating how to consider the influence of these properties in distance metrics became necessary, and new similarity metrics have been adopted to focus on geometric features and dimensions.
However, to gain a comprehensive understanding of attribute relevance, and to address it at the similarity-evaluation phase, it is necessary to evaluate the performance of transfer-learning algorithms as these structural features vary.
The present work extends this research by examining the effect of material and dimension attributes on the performance of a domain adaptation method - the Transfer Component Analysis (TCA).
This analysis is applied to an experimental population of laboratory-scale aircraft, comprising structures with different materials and dimensions, and similar topology. A confusion matrix is employed to compare the findings and show how these properties can influence the transfer-learning performance, especially for localised damage, thus highlighting the importance of their evaluation in the context of PBSHM
On the use of the inverse finite element method to enhance knowledge sharing in population-based structural health monitoring
No full textEfficient Structural Health Monitoring (SHM) is critical for ensuring safety and improving the operation and maintenance of aerospace structures. This study focusses on advanced shape-sensing methods, such as the inverse Finite Element Method (iFEM), which can estimate the complete displacement field of a structure based on a restricted number of strain measurements, fostering continuous and real-time monitoring. This approach additionally provides valuable insights into the dynamic behaviour of a structure by extracting its Frequency Response Functions (FRFs) and modal properties to perform vibration-based SHM. However, effectively extending SHM to a fleet or population of structures would require a significant amount of data for each one, which may be unavailable or incomplete. A population-based Structural Health Monitoring (PBSHM) strategy can solve data scarcity by sharing knowledge between similar structures via transfer-learning algorithms. In PBSHM, handling data from diverse sources is paramount for achieving accurate results. Therefore, this study integrates iFEM into the PBSHM framework, enhancing knowledge transfer by harmonising fibre-optic strain measurements to vibration-based features and providing reliable source data to inform diagnostics on similar structures. The proposed approach is validated on a population of laboratory-scale steel aircraft subjected to specific operating and damage conditions tested using three different sensor setups
On the influence of attributes for assessing similarity and sharing knowledge in heterogeneous populations of structures
No full textThe effectiveness of data-driven Structural Health Monitoring methods, which require dynamic response data from undamaged and damaged states within the same domain, is often hindered by the scarcity of data. The population-based approach to Structural Health Monitoring (PBSHM) aims to overcome this challenge by exploiting the transfer of damage-state knowledge across a population of structures via transfer-learning algorithms — specifically domain adaptation. However, meaningful inference through data sharing is possible only when the structures and their datasets are sufficiently similar. This study advances PBSHM by focusing on its two main phases: assessing structural similarity and developing effective transfer-learning strategies for heterogeneous populations. Furthermore, it explores the interdependence of these phases by addressing the role of structural attributes – such as material properties, geometry, and dimensions – in the performance of domain adaptation and similarity metrics. The proposed methodology employs Normal Condition Alignment (NCA) and Joint Distribution Adaptation (JDA) as domain-adaptation techniques across a population of laboratory-scale aircraft, considering tasks of increasing complexity. Additionally, the effect of material properties, geometry or dimensional differences in similarity assessment is investigated, comparing Graph Matching Networks and correlation-based distance metrics
On Statistic Alignment Performance for Enhancing damage localisation across a Population of Heterogeneous shear-frame Structures
No full textThe development of machine learning algorithms for Structural Health Monitoring (SHM) is rapidly advancing. However, their application for real-world structures finds a high number of complications. One is the need for comprehensive data for training the proper algorithms. The theory of Population-based Health Monitoring (PBSHM) overcomes these challenges by sharing information between different structures. In this framework, it is necessary to understand to what extent knowledge can be shared, especially for heterogeneous datasets. This study implements a simple domain-adaptation technique based on Statistic Alignment (SA) on a population of heterogeneous structures to investigate how the performance changes because of the variations within the population.
The study focusses on the numerical simulation of a population of bi-dimensional shear-frames under multiple sources of heterogeneity and damage conditions. The knowledge transfer within the population is investigated by performing damage localisation on multiple pairs of source and target domains to highlight how variations in the structures' topology, materials and geometry affect the transfer-learning and monitoring performance
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