1,720,991 research outputs found
Modellazione numerica sperimentale a supporto della Artificial Neural Network: valutazione predittiva della resistenza all’accelerazione di collasso tramite curve di progetto degli edifici in muratura
Full text linkThe aim of this research is to develop an innovative and efficient methodology for the expedited
assessment of seismic vulnerability in masonry buildings, leveraging the capabilities of
artificial neural networks (ANN) integrated with an experimental numerical modeling
approach. Following catastrophic seismic events, masonry structures are often significantly
compromised, resulting in their classification as unsafe and uninhabitable. Traditionally, the
evaluation of such buildings relies on qualitative assessments performed by inspectors, who
provide preliminary estimates of structural reliability based on visual inspection and
experience. However, this process is inherently subjective and prone to inaccuracies, leading to
potential misclassifications that can either overestimate or underestimate the actual risk posed
by these structures.
To overcome these limitations, the proposed research adopts a machine learning framework,
specifically an ANN, to estimate the seismic response of masonry buildings with rectangular
geometries. This method allows for a comprehensive and data-driven evaluation of structural
vulnerability by incorporating a wide range of building geometries and material properties. The
study considers twelve distinct building geometries, twenty-four unique combinations of
mechanical parameters, and five different seismic loading directions, resulting in the simulation
of 34,560 configurations. These extensive simulations were then summarized through a
synthetic polynomial representation, which efficiently encapsulates the complexity of the
dataset while enabling streamlined analysis.
The ANN was trained, tested, and validated using results from an experimental numerical
approach grounded in the Distinct Element Method (DEM), a well-established analytical
method for the assessment of structural behavior under seismic loads. The performance of the
ANN, when compared to DEM-generated results, demonstrated a high level of accuracy, with
predictions differing by approximately 10%.
This confirms the viability of using machine learning techniques for the reliable prediction of
seismic performance in masonry structures. The primary outcome of this research is the
development of a comprehensive database of design curves, which can be employed for the
rapid assessment of the seismic vulnerability of masonry buildings. These design curves offer
a practical tool for engineers and decision-makers in the aftermath of earthquakes, providing a
quantitative and objective basis for classifying buildings as safe or unsafe.
The proposed methodology represents a significant advancement over traditional assessment
techniques, which are often limited by their reliance on subjective judgment. By combining
machine learning with established numerical methods, this research contributes to the
development of more reliable and scalable tools for the assessment of building safety in seismicprone areas
Overcoming the natural excitation assumption: a strategy to deal with colouration and time/space correlation in output-only modal analysis
No full textOperational-modal-analysis-based processing of no-next engineering applications datasets: A generalized power spectral density modal model formulation
No full textOperational Modal Analysis, OMA, even referred to as Output-only Modal Analysis, as opposed to the Input-Output technique, is a powerful technique used to identify the dynamic properties of a vibration system in steady working conditions. Starting from the only measured output signals, OMA allows achieving the estimation of resonance frequencies, damping ratios, and modes, i.e. the modal parameters. The main drawback of OMA approach consists in the NExT assumptions of uncorrelated white noises excitations. These hypotheses, in fact, are violated in all those cases in which the exerted environmental loads cannot be described as white noises, as in the cases of systems having rotating parts (machine tools, engines or wind turbines) or characterized by speed and/or time correlated inputs (road and rail vehicles). In this paper, we derive an OMA formulation not based on the NExT assumptions but incorporating the relationship between outputs, inputs, and modal parameters in a suitable way. Specifically, the proposed OMA technique requires some knowledge about the inputs acting on the system and, thus, it is applicable to systems for which something about the inputs is somehow known. We show the existence of a modal model of the output Power Spectral Densities, PSDs, which contain the dependence not only by the modal parameters, but also by the input PSDs. This model is referred to as the generalized PSD modal model. Examples of the usage of this approach are illustrated in the case of the identification of a lumped parameter system in the presence of both stochastic and harmonic excitations and in that of the rigid body modes of a road/railway vehicle from numerical data
METHOD FOR DETERMINING THE MODAL PARAMETERS OF ROAD OR RAIL VEHICLES AND FOR THE INDIRECT CHARACTERIZATION OF ROAD OR RAIL PROFILES
No full text
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
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe 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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