Parametric study of the expansive joint model for cracking of concrete due to rebar corrosion

Un modelo numérico llamado elemento junta expansiva fue programado para simular la expansión mecánica del óxido y estudiar la fisuración en el hormigón circundante. El elemento junta expansiva trabaja con elementos finitos con fisura cohesiva embebida adaptable para simular la fractura del hormigón según el modelo de fisura cohesiva. Se ha comprobado que el modelo reproduce correctamente el patrón de fisuración del hormigón que se obtiene en ensayos de corrosión acelerada. En este trabajo, se realiza un estudio paramétrico del elemento junta expansiva para establecer los límites de los parámetros constitutivos del óxido. Se simula una cierta expansión variando los valores de los parámetros del óxido y se estudian la apertura de fisura y las tensiones resultantes en el hormigón. Se determina el rango de valores para los que los resultados de las simulaciones son prácticamente iguales, con el menor número posible de iteraciones.

Seasonal Dynamic Factor Analysis and Bootstrap Inference: Application to Electricity Market Forecasting

In this work, we propose the Seasonal Dynamic Factor Analysis (SeaDFA), an extension of Nonstationary Dynamic Factor Analysis, through which one can deal with dimensionality reduction in vectors of time series in such a way that both common and specific components are extracted. Furthermore, common factors are able to capture not only regular dynamics (stationary or not) but also seasonal ones, by means of the common factors following a multiplicative seasonal VARIMA(p, d, q) × (P, D, Q)s model. Additionally, a bootstrap procedure that does not need a backward representation of the model is proposed to be able to make inference for all the parameters in the model. A bootstrap scheme developed for forecasting includes uncertainty due to parameter estimation, allowing enhanced coverage of forecasting intervals. A challenging application is provided. The new proposed model and a bootstrap scheme are applied to an innovative subject in electricity markets: the computation of long-term point forecasts and prediction intervals of electricity prices. Several appendices with technical details, an illustrative example, and an additional table are available online as Supplementary Materials.

Automatic Bandwidth Estimation Strategy for High-Quality Non-Parametric Modeling based Moving Object Detection

Here, a novel and efficient moving object detection strategy by non-parametric modeling is presented. Whereas the foreground is modeled by combining color and spatial information, the background model is constructed exclusively with color information, thus resulting in a great reduction of the computational and memory requirements. The estimation of the background and foreground covariance matrices, allows us to obtain compact moving regions while the number of false detections is reduced. Additionally, the application of a tracking strategy provides a priori knowledge about the spatial position of the moving objects, which improves the performance of the Bayesian classifier

An efficient, parametric fixpoint algorithm for analysis of java bytecode

Abstract interpretation has been widely used for the analysis of object-oriented languages and, in particular, Java source and bytecode. However, while most existing work deals with the problem of flnding expressive abstract domains that track accurately the characteristics of a particular concrete property, the underlying flxpoint algorithms have received comparatively less attention. In fact, many existing (abstract interpretation based—) flxpoint algorithms rely on relatively inefHcient techniques for solving inter-procedural caligraphs or are speciflc and tied to particular analyses. We also argüe that the design of an efficient fixpoint algorithm is pivotal to supporting the analysis of large programs. In this paper we introduce a novel algorithm for analysis of Java bytecode which includes a number of optimizations in order to reduce the number of iterations. The algorithm is parametric -in the sense that it is independent of the abstract domain used and it can be applied to different domains as "plug-ins"-, multivariant, and flow-sensitive. Also, is based on a program transformation, prior to the analysis, that results in a highly uniform representation of all the features in the language and therefore simplifies analysis. Detailed descriptions of decompilation solutions are given and discussed with an example. We also provide some performance data from a preliminary implementation of the analysis.

An efficient, parametric fixpoint algorithm for incremental analysis of java bytecode

Abstract interpretation has been widely used for the analysis of object-oriented languages and, more precisely, Java source and bytecode. However, while most of the existing work deals with the problem of finding expressive abstract domains that track accurately the characteristics of a particular concrete property, the underlying fixpoint algorithms have received comparatively less attention. In fact, many existing (abstract interpretation based) fixpoint algorithms rely on relatively inefficient techniques to solve inter-procedural call graphs or are specific and tied to particular analyses. We argue that the design of an efficient fixpoint algorithm is pivotal to support the analysis of large programs. In this paper we introduce a novel algorithm for analysis of Java bytecode which includes a number of optimizations in order to reduce the number of iterations. Also, the algorithm is parametric in the sense that it is independent of the abstract domain used and it can be applied to different domains as "plug-ins". It is also incremental in the sense that, if desired, analysis data can be saved so that only a reduced amount of reanalysis is needed after a small program change, which can be instrumental for large programs. The algorithm is also multivariant and flowsensitive. Finally, another interesting characteristic of the algorithm is that it is based on a program transformation, prior to the analysis, that results in a highly uniform representation of all the features in the language and therefore simplifies analysis. Detailed descriptions of decompilation solutions are provided and discussed with an example.

Parametric analysis of gravity offshore structures: Part 1

The objective of this paper is to analyse the influence of the variation of some parameters used in the analysis of the dynamic response of offshore structures under the action of wind generated waves. The structural response has been obtained by stochastic methods using two discretization models. One with lumped parameters, using translational degrees of freedom (d.o.f.) and the other with one-dimensional finite elements. Using each of these methods the problem has been solved with several d.o.f., analysing the influence of the number of d.o.f. on the results.

Improving web learning through model optimization using bootstrap for a tour-guide robot

We perform a review of Web Mining techniques and we describe a Bootstrap Statistics methodology applied to pattern model classifier optimization and verification for Supervised Learning for Tour-Guide Robot knowledge repository management. It is virtually impossible to test thoroughly Web Page Classifiers and many other Internet Applications with pure empirical data, due to the need for human intervention to generate training sets and test sets. We propose using the computer-based Bootstrap paradigm to design a test environment where they are checked with better reliability.

Detection and Isolation of Simultaneous Additive and Parametric Faults in Nonlinear Stochastic Dynamical Systems

This paper presents a new fault detection and isolation scheme for dealing with simultaneous additive and parametric faults. The new design integrates a system for additive fault detection based on Castillo and Zufiria, 2009 and a new parametric fault detection and isolation scheme inspired in Munz and Zufiria, 2008 . It is shown that the so far existing schemes do not behave correctly when both additive and parametric faults occur simultaneously; to solve the problem a new integrated scheme is proposed. Computer simulation results are presented to confirm the theoretical studies.

Aplicación de la metodología bootstrap para la estimación de la movilidad de furgonetas

En este trabajo se muestran los resultados de la aplicación de la metodología bootstrap a datos de 3369 encuestas realizadas en 2009 a nivel nacional entre conductores de furgonetas, para obtener datos de movilidad interurbana y total, según edad de los vehículos, uso, conductores y otras características de este tipo de vehículo. Se obtienen estimaciones puntuales e intervalos de confianza para la movilidad total de furgonetas, así como para los cuatro tipos de furgonetas según la clasificación realizada en el proyecto de referencia. Se comparan los resultados obtenidos con estimaciones alternativas realizadas con otras fuentes de datos para el mismo colectivo (encuestas realizadas en inspecciones en carretera realizadas por la ATGC de la DGT e inspecciones en ITV) y datos publicados por fuentes oficiales. Estos resultados de movilidad (en término de vehículo-kilómetro) se usarán para la estimación de ratios de accidentalidad en un estudio comparado con otros colectivos de vehículos.

Computational Methods for Identification of Vibrating Structures

System identification deals with the problem of building mathematical models of dynamical systems based on observed data from the system" [1]. In the context of civil engineering, the system refers to a large scale structure such as a building, bridge, or an offshore structure, and identification mostly involves the determination of modal parameters (the natural frequencies, damping ratios, and mode shapes). This paper presents some modal identification results obtained using a state-of-the-art time domain system identification method (data-driven stochastic subspace algorithms [2]) applied to the output-only data measured in a steel arch bridge. First, a three dimensional finite element model was developed for the numerical analysis of the structure using ANSYS. Modal analysis was carried out and modal parameters were extracted in the frequency range of interest, 0-10 Hz. The results obtained from the finite element modal analysis were used to determine the location of the sensors. After that, ambient vibration tests were conducted during April 23-24, 2009. The response of the structure was measured using eight accelerometers. Two stations of three sensors were formed (triaxial stations). These sensors were held stationary for reference during the test. The two remaining sensors were placed at the different measurement points along the bridge deck, in which only vertical and transversal measurements were conducted (biaxial stations). Point estimate and interval estimate have been carried out in the state space model using these ambient vibration measurements. In the case of parametric models (like state space), the dynamic behaviour of a system is described using mathematical models. Then, mathematical relationships can be established between modal parameters and estimated point parameters (thus, it is common to use experimental modal analysis as a synonym for system identification). Stable modal parameters are found using a stabilization diagram. Furthermore, this paper proposes a method for assessing the precision of estimates of the parameters of state-space models (confidence interval). This approach employs the nonparametric bootstrap procedure [3] and is applied to subspace parameter estimation algorithm. Using bootstrap results, a plot similar to a stabilization diagram is developed. These graphics differentiate system modes from spurious noise modes for a given order system. Additionally, using the modal assurance criterion, the experimental modes obtained have been compared with those evaluated from a finite element analysis. A quite good agreement between numerical and experimental results is observed.

Automatic generation of real-time deformable parametric model of the aorta for a VR-based catheterism guidance system

The purpose of this work is twofold: first, to develop a process to automatically create parametric models of the aorta that can adapt to any possible intraoperative deformation of the vessel. Second, it intends to provide the tools needed to perform this deformation in real time, by means of a non-rigid registration method. This dynamically deformable model will later be used in a VR-based surgery guidance system for aortic catheterism procedures, showing the vessel changes in real time.

O Botafumeiro: parametric pumping in the Middle Age

The pendular motion of a giant censer (O Botafumeiro) that hangs in the transept of the cathedral of Santiago de Compostela, and is cyclically pumped by men who pull at the supporting rope, is analyzed. Maximum angular amplitude attainable, and number of cycles and time needed to attain it, are calculated; the results agree with observed values (~ 82°, ~ 17 cycles, ~ 80 seconds) to the few percent accuracy of both the analysis and the observations and parameter measurements. The energy gain in a pumping cycle is obtained for an arbitrary pumping procedure to two orders in the small fractional change of pendular length; the relevance of the ratio (characteristic radial acceleration during pumping)/g to the gain is discussed- Effects due to rope mass, air drag on both Censer and rope, and the fact that the Censer is not a point mass, are considered. If the pumping cycle is inverted once the maximum amplitude has been attained, the Censer could be swiftly brought to rest, avoiding the usual violent stop. Historically recorded accidents, rope shape, and the influence of relevant parameters on the motion are discussed.

Versatile Bayesian classifier for moving object detection by non-parametric background-foreground modeling

Along the recent years, several moving object detection strategies by non-parametric background-foreground modeling have been proposed. To combine both models and to obtain the probability of a pixel to belong to the foreground, these strategies make use of Bayesian classifiers. However, these classifiers do not allow to take advantage of additional prior information at different pixels. So, we propose a novel and efficient alternative Bayesian classifier that is suitable for this kind of strategies and that allows the use of whatever prior information. Additionally, we present an effective method to dynamically estimate prior probability from the result of a particle filter-based tracking strategy.

A 2D computational parametric analysis of the sheltering effect of fences on a railway vehicle standing on a bridge and experiencing crosswinds

In a crosswind scenario, the risk of high-speed trains overturning increases when they run on viaducts since the aerodynamic loads are higher than on the ground. In order to increase safety, vehicles are sheltered by fences that are installed on the viaduct to reduce the loads experienced by the train. Windbreaks can be designed to have different heights, and with or without eaves on the top. In this paper, a parametric study with a total of 12 fence designs was carried out using a two-dimensional model of a train standing on a viaduct. To asses the relative effectiveness of sheltering devices, tests were done in a wind tunnel with a scaled model at a Reynolds number of 1 × 105, and the train’s aerodynamic coefficients were measured. Experimental results were compared with those predicted by Unsteady Reynolds-averaged Navier-Stokes (URANS) simulations of flow, showing that a computational model is able to satisfactorily predict the trend of the aerodynamic coefficients. In a second set of tests, the Reynolds number was increased to 12 × 106 (at a free flow air velocity of 30 m/s) in order to simulate strong wind conditions. The aerodynamic coefficients showed a similar trend for both Reynolds numbers; however, their numerical value changed enough to indicate that simulations at the lower Reynolds number do not provide all required information. Furthermore, the variation of coefficients in the simulations allowed an explanation of how fences modified the flow around the vehicle to be proposed. This made it clear why increasing fence height reduced all the coefficients but adding an eave had an effect mainly on the lift force coefficient. Finally, by analysing the time signals it was possible to clarify the influence of the Reynolds number on the peak-to-peak amplitude, the time period and the Strouhal number.