1,720,967 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
Full text linkUnderground 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
Full text linkUnderground 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
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 use of the inverse finite element method to enhance knowledge sharing in population-based structural health monitoring
Full text linkEfficient 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
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Investigación sobre el rendimiento del alineamiento estadístico para mejorar la identificación de daños en una población de estructuras de corte heterogéneas
Full text linkPráctica Supervisada (I.C.)--FCEFN-UNC, 2024Tesis de Maestría (I.C.)--Politécnico de Torino, 2024Fil: Badariotti, Sebastian. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Física y Naturales; Argentina.Fil: Badariotti, Sebastián. Politécnico de Turín. Programa de Maestría en Ingeniería Civil; Italia.Fil: Badariotti, Sebastián. Politecnico di Torino. Corso di laurea magistrale in Ingegneria Civile; Italia.The 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. Thus, 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. Therefore, this study implements a simple domain adaptation technique based on Statistical Alignment (SA) on a population of heterogeneous shear structures to investigate how the performance changes due to the variations within the population. The scenarios proposed are solved with normal-condition alignment (NCA) and normal-correlation alignment (NCORAL). Two case studies are analysed. The first is related to numerical structures. It is created by simulating multiple source and target datasets, containing the features and labels of each data point. The features consist of the natural frequencies of each structure, and the label is a binary vector indicating if the data point corresponds to a damage condition or not. To calculate the natural frequencies, the structure is modelled as a shear-type with chain-like models, and the mass and stiffness matrices are calculated considering the equation of motion. The damage is then introduced with a reduction of the stiffness of a column, leading to reduced values of the related frequencies. It is important to highlight that, in each sample, a variation of the material properties is introduced, trying to simulate the actual variability on measured data. The second case study extends the implementation to an experimental case study of a three-story frame structure to test this methodology for sharing knowledge between real and simulated data.El desarrollo de algoritmos de aprendizaje automático para el Monitoreo de Salud Estructural (SHM) avanza rápidamente, pero su aplicación en estructuras reales enfrenta complicaciones debido a la falta de datos completos para entrenar los algoritmos. El Monitoreo de Salud Basado en Población (PBSHM) resuelve estos problemas al compartir información entre estructuras. Este estudio implementa una técnica de adaptación de dominio basada en Alineamiento Estadístico (SA) en estructuras de corte heterogéneas, para analizar cómo varía el rendimiento con las diferencias dentro de la población. Se resuelven los escenarios con alineamiento en condiciones normales (NCA) y alineamiento de correlación normal (NCORAL). El primer estudio de caso simula estructuras numéricas, usando frecuencias naturales y etiquetas para identificar daños. El segundo caso extiende la técnica a una estructura experimental de tres pisos, para probar el intercambio de conocimiento entre datos reales y simulados. (resumen provisto por el ctalogador)Fil: Badariotti, Sebastian. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Física y Naturales; Argentina.Fil: Badariotti, Sebastián. Politécnico de Turín. Programa de Maestría en Ingeniería Civil; Italia.Fil: Badariotti, Sebastián. Politecnico di Torino. Corso di laurea magistrale in Ingegneria Civile; Italia
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