1,720,957 research outputs found
Rank aggregation to predict the fundamental frequency of historic masonry towers
Get PDFThe fundamental frequency is a key dynamic parameter for evaluating the seismic vulnerability and structural integrity of historic masonry towers. Its estimation with empirical laws is feasible but it is often complicated by the variability in geometric features, material properties, and boundary conditions. This work proposes an original methodology that combines multiple predictive empirical models and laws using a rank aggregation approach based on the Plackett-Luce model. Rather than selecting a single law, the method considers results from empirical equations and data-driven models to produce a unified and more reliable prediction. Two distinct estimation scenarios are examined: one relying exclusively on geometric properties, and another that also takes into account mechanical features. Both are trained and validated on a broad dataset of historic masonry towers. The novelty of the approach lies in its ability to integrate different sources of knowledge while reducing individual model errors. Since many structural characteristics of the towers may be unknown, this method seeks to combine models with different input features, ranging from complex models to simpler formulations based on easily measurable parameters. By exploiting the best features of each candidate and by ranking their contributions, the method shows improved performance across different towers. This strategy can be a valuable tool in structural health monitoring and seismic assessment of heritage towers, especially when experimental dynamic data are not available and when dealing with complex modeling uncertainties
Using Similarity Distance Measures for Multiclass Damage Detection in Dynamically Monitored Structures
Get PDFDomain adaptation (DA) techniques have recently been developed as a promising approach to enhance the performance of structural damage classification algorithms. Unlike traditional methods, DA imposes fewer constraints on the nature and completeness of datasets, although its effectiveness largely depends on the similarity between the datasets used for knowledge transfer. This paper proposes a novel approach for assessing structural similarity to improve DA in structural health monitoring (SHM). The identification of suitable source data for knowledge transfer in damage detection is an open issue in SHM, especially when dealing with important geometric, mechanical, and topological differences between the structures. To address this issue, damage detection accuracy is increased by investigating similarity in the modal features of different framed structures, with the aim of understanding their dynamic behavior through a similarity index based on divergence measures. In detail, this work proposes a novel modal sensitivity-based similarity index which relies on the Kullback-Leibler divergence computed from vibration-based dynamic features. This similarity index effectively reveals how structures differing in highly sensitive parameters exhibit greater divergence. When DA is applied, source datasets with higher similarity lead to improved multiclass damage classification accuracy on the target framed structure. The proposed index can be used to systematically rank candidate source structures before applying DA, allowing a more efficient selection process. Its applicability extends to large-scale structures, where managing heterogeneous structural datasets is essential, supporting data-driven SHM strategies with enhanced transferability and reliability in real-world monitoring scenarios
Automated mode tracking via supervised classification and adaptive parameter calibration for seismic monitoring with sparse sensors
Get PDFOne of the most important issues to address in the practical implementation of permanent dynamic Structural Health Monitoring (SHM) systems is undoubtedly that of Mode Tracking (MT). Indeed, the influence of environmental and random fluctuations, as well as the uncertainty inherent in the identification algorithms themselves, especially the spill-over effects linked to unmodeled dynamics, can make it difficult to disentangle the various modal behaviours. This separation process, i.e the MT procedure, involves comparing vibration mode estimates with a reference set of modal properties. Although this operation can be straightforward for simple structures, in many practical applications of structural engineering, when there is strong modal concentration (e.g. lattice structures) or high geometric and mechanical complexity (e.g. monumental buildings) greater challenges arise, which grow in the presence of sparse sensor setups (civil structures in general),
the superposition of exogenous frequency components (industrial structures, bell towers etc.) and environmental fluctuations. This study presents an innovative MT methodology that combines supervised classification, using advanced machine learning algorithms, with adaptive multi-threshold calibration to overcome the limitations of current MT techniques. The approach incorporates clustering analysis to characterize vibration modes by their natural frequencies and mode shapes, ensuring accurate identification and rejection of spurious data. The method was validated with a simplified numerical model and then demonstrated on a baroque monumental structure equipped with a long-term monitoring system. In addition to being efficient and robust compared to traditional techniques, the proposed procedure is effective for automating the monitoring of modal parameters in SHM systems, even in scenarios with limited sensor deployments
Knowledge Transfer Between Dynamically Monitored Masonry Bell Towers
No full textStructural health monitoring (SHM) is really important in cultural heritage (CH) structures, where invasive interventions are often not allowed. An important challenge in data-driven approaches is the lack of data, especially corresponding to damage states, which hampers the development of pattern recognition algorithms in the field of Machine Learning (ML) models. For CH structures, obtaining a complete dataset of health states—including both undamaged and damaged conditions—is often impossible, particularly for damaged states, as it would require harming the structure in situ. Domain adaptation (DA), a sub-sector of Transfer Learning (TL) methods, addresses this problem by adapting data from a more accessible and monitored system (source) transferring information by leveraging data domains to systems with more limited data (target) but with similar properties. In this work, DA techniques are used to expand information on the health state of the special class of monitored structures represented by CH masonry bell towers. In more detail, continuous-time dynamic monitoring data are obtained from a system installed on the masonry bell tower (source) of the Church of S. Maria and S. Giovenale (Fossano, Piedmont, Italy). This tower is currently monitored because, following the evolution of vertical cracking phenomena, in 2012 it was subjected to reinforcement interventions, through the installation of 11 steel tie rods on its four external sides. Another damaged masonry bell tower, belonging to the ancient parish Church of S. Antonio Abate (Montà, Piedmont, Italy), is instead assumed as target structure, with limited knowledge gained from a single testing campaign
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
Get PDFWe 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
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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