1,721,027 research outputs found
Distribution-free stochastic model updating of dynamic systems with parameter dependencies
Full text linkThis work proposes a distribution-free stochastic model updating framework to calibrate the joint probabilistic distribution of the multivariate correlated parameters. In this framework, the marginal distributions are defined as the staircase density functions and the correlation structure is described by the Gaussian copula function. The first four moments of the staircase density functions and the correlation coefficients are updated by an approximate Bayesian computation, in which the Bhattacharyya distance-based metric is proposed to define an approximate likelihood that is capable of capturing the stochastic discrepancy between model outputs and observations. The feasibility of the framework is demonstrated on two illustrative examples and a followed engineering application to the updating of a nonlinear dynamic system using observed time signals. The results demonstrate the capability of the proposed updating procedure in the very challenging condition where the prior knowledge about the distribution of the parameters is extremely limited (i.e., no information on the marginal distribution families and correlation structure is available)
Affidabilità di approcci predittivi di regionalizzazione delle proprietà geologico-tecniche dei depositi superficiali di versante
Full text linkLe frane indotte dagli eventi meteorologici intensi nelle terre al di sopra del substrato consistente (depositi superficiali di versante - DS) costituiscono un fattore di pericolo per gran parte del territorio collinare e montano. I caratteri geologico-tecnici dei DS svolgono un ruolo rilevante per la comprensione dei fenomeni di franosità superficiale. Il tempo ed i costi necessari per raccogliere dati geologico-tecnici sui DS riducono la densità areale con cui questi vengono acquisiti, determinando limitazioni di affidabilità per le valutazioni predittive regionalizzate di pericolosità da frana superficiale.
In questo quadro le performance degli approcci di regionalizzazione delle caratteristiche puntuali dei DS basate su fattori spazialmente continui, quali litologia, struttura e caratteristiche geologico-tecniche del substrato geologico, uso del suolo, morfometria dell’area indagata, hanno un ruolo di fondamentale importanza.
Vengono qui esposti i primi risultati di affidabilità di approcci di spazializzazione delle caratteristiche geologico-tecniche dei DS, in particolare la profondità (pDS), basate sulla stratificazione di criteri geologici e la segmentazione dello spazio morfometrico.
Lo spazio morfometrico viene descritto attraverso variabili quali pendenza, curvature e flow accumulation, derivate da un modello digitale del terreno con cella di 10 m. Impiegando un insieme di osservazioni puntuali (training dataset di circa 170 pDS tra profili e trivellate) raccolte in una prima area di studio (ADS1: regione a litologia costante del substrato corrispondente alla Formazione del Macigno, media valle del fiume Serchio), le variabili morfometriche sono state classificate mediante approcci hard e fuzzy di statistica multivariata (cluster analysis, neural network). È stata così ottenuta per l’ADS1 una rappresentazione continua in classi di pDS.
Le “firme morfometriche” (insieme delle relazioni pDS vs variabili morfometriche) individuate nell’ADS1 sono state utilizzate, con approccio supervisionato, per ottenere una rappresentazione in classi di pDS di una nuova area di studio (ADS2: Alpi Apuane occidentali) geograficamente distinta dalla precedente, ma localizzata sulla stessa formazione di substrato. Un nuovo dataset puntuale delle proprietà geologico-tecniche dei DS acquisito nella ADS2 (test dataset) ha consentito di valutare l’accuratezza predittiva delle pDS stimate tramite estrapolazione delle firme morfometriche di ADS1. Lo stesso dataset è stato utilizzato come training dataset per spazializzare le classi di pDS anche nella ADS2 tramite cluster analysis
The role of the Bhattacharyya distance in stochastic model updating
Full text linkThe Bhattacharyya distance is a stochastic measurement between two samples and taking into account their probability distributions. The objective of this work is to further generalize the application of the Bhattacharyya distance as a novel uncertainty quantification metric by developing an approximate Bayesian computation model updating framework, in which the Bhattacharyya distance is fully embedded. The Bhattacharyya distance between sample sets is evaluated via a binning algorithm. And then the approximate likelihood function built upon the concept of the distance is developed in a two-step Bayesian updating framework, where the Euclidian and Bhattacharyya distances are utilized in the first and second steps, respectively. The performance of the proposed procedure is demonstrated with two exemplary applications, a simulated mass-spring example and a quite challenging benchmark problem for uncertainty treatment. These examples demonstrate a gain in quality of the stochastic updating by utilizing the superior features of the Bhattacharyya distance, representing a convenient, efficient, and capable metric for stochastic model updating and uncertainty characterization
Bayesian Model Updating in Time Domain with Metamodel-Based Reliability Method
Full text linkIn this study, a two-step approximate Bayesian computation (ABC) updating framework using dynamic response data is developed. In this framework, the Euclidian and Bhattacharyya distances are utilized as uncertainty quantification (UQ) metrics to define approximate likelihood functions in the first and second steps, respectively. A new Bayesian inference algorithm combining Bayesian updating with structural reliability methods (BUS) with the adaptive Kriging model is then proposed to effectively execute the ABC updating framework. The performance of the proposed procedure is demonstrated with a seismic-isolated bridge model updating application using simulated seismic response data. This application denotes that the Bhattacharyya distance is a powerful UQ metric with the capability to recreate wholly the distribution of target observations, and the proposed procedure can provide satisfactory results with much reduced computational demand compared with other well-known methods, such as transitional Markov chain Monte Carlo (TMCMC)
The Bhattacharyya distance: Enriching the P-box in stochastic sensitivity analysis
Full text link© 2019 Elsevier Ltd The tendency of uncertainty analysis has promoted the transformation of sensitivity analysis from the deterministic sense to the stochastic sense. This work proposes a stochastic sensitivity analysis framework using the Bhattacharyya distance as a novel uncertainty quantification metric. The Bhattacharyya distance is utilised to provide a quantitative description of the P-box in a two-level procedure for both aleatory and epistemic uncertainties. In the first level, the aleatory uncertainty is quantified by a Monte Carlo process within the probability space of the cumulative distribution function. For each sample of the Monte Carlo simulation, the second level is performed to propagate the epistemic uncertainty by solving an optimisation problem. Subsequently, three sensitivity indices are defined based on the Bhattacharyya distance, making it possible to rank the significance of the parameters according to the reduction and dispersion of the uncertainty space of the system outputs. A tutorial case study is provided in the first part of the example to give a clear understanding of the principle of the approach with reproducible results. The second case study is the NASA Langley challenge problem, which demonstrates the feasibility of the proposed approach, as well as the Bhattacharyya distance metric, in solving such a large-scale, strong-nonlinear, and complex problem
Uncertainty on shallow landslide hazard assessment: from field data to hazard mapping
Full text linkShallow landsliding that involve Hillslope Deposits (HD), the surficial soil that cover the bedrock, is an important
process of erosion, transport and deposition of sediment along hillslopes. Despite Shallow landslides generally
mobilize relatively small volume of material, they represent the most hazardous factor in mountain regions due
to their high velocity and the common absence of warning signs. Moreover, increasing urbanization and likely
climate change make shallow landslides a source of widespread risk, therefore the interest of scientific community
about this process grown in the last three decades. One of the main aims of research projects involved on this
topic, is to perform robust shallow landslides hazard assessment for wide areas (regional assessment), in order to
support sustainable spatial planning.
Currently, three main methodologies may be implemented to assess regional shallow landslides hazard: expert
evaluation, probabilistic (or data mining) methods and physical models based methods. The aim of this work is
evaluate the uncertainty of shallow landslides hazard assessment based on physical models taking into account
spatial variables such as: geotechnical and hydrogeologic parameters as well as hillslope morphometry. To achieve
this goal a wide dataset of geotechnical properties (shear strength, permeability, depth and unit weight) of HD was
gathered by integrating field survey, in situ and laboratory tests. This spatial database was collected from a study
area of about 350 km2 including different bedrock lithotypes and geomorphological features. The uncertainty
associated to each step of the hazard assessment process (e.g. field data collection, regionalization of site specific
information and numerical modelling of hillslope stability) was carefully characterized.
The most appropriate probability density function (PDF) was chosen for each numerical variable and we assessed
the uncertainty propagation on HD strength parameters obtained by empirical relations with geotechnical index
properties. Site specific information was regionalized at map scale by (hard and fuzzy) clustering analysis taking
into account spatial variables such as: geology, geomorphology and hillslope morphometric variables (longitudinal
and transverse curvature, flow accumulation and slope), the latter derived by a DEM with 10 m cell size. In order
to map shallow landslide hazard, Monte Carlo simulation was performed for some common physically based
models available in literature (eg. SINMAP, SHALSTAB, TRIGRS). Furthermore, a new approach based on the
use of Bayesian Network was proposed and validated. Different models, such as Intervals, Convex Models and
Fuzzy Sets, were adopted for the modelling of input parameters. Finally, an accuracy assessment was carried
out on the resulting maps and the propagation of uncertainty of input parameters into the final shallow landslide
hazard estimation was estimated. The outcomes of the analysis are compared and discussed in term of discrepancy
among map pixel values and related estimated error.
The novelty of the proposed method is on estimation of the confidence of the shallow landslides hazard mapping
at regional level. This allows i) to discriminate regions where hazard assessment is robust from areas where more
data are necessary to increase the confidence level and ii) to assess the reliability of the procedure used for hazard
assessment
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
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