Alexandre Lorenzi
Universidade Federal do Rio Grande do Sul
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Publication
Featured researches published by Alexandre Lorenzi.
International Journal of Materials & Product Technology | 2006
Alexandre Lorenzi; Joao Luiz Campagnolo; Luiz Carlos Pinto da Silva Filho
Concrete is an essential material for civil engineers. However, its properties can vary considerably, depending on the nature and proportions of the constituents, the construction methods and the loading and environmental conditions. Therefore, the development of methods to determine the state condition and ascertain the quality of concrete elements is critical. This paper describes a study carried out to evaluate the feasibility of developing Artificial Neural Networks (ANN) that use data about concrete properties and ultrasonic readings to estimate compressive strength. The results obtained indicate that the estimation power of a neural network can surpass that of traditional statistical techniques and that this might become a very interesting tool to model certain problems in civil engineering.
International Scholarly Research Notices | 2014
Alexandre Lorenzi; Luciane Fonseca Caetano; Josué Argenta Chies; Luiz Carlos Pinto da Silva Filho
Adoption of periodic or continuous monitoring strategies to assess condition state of infrastructure elements is a vital part of service life management (SLM). NDT methods are increasingly seen as an attractive and viable strategy to support condition monitoring. Over the last 15 years, the LEME research group at UFRGS has investigated several aspects related to the use of the ultrasonic pulse velocity (UPV) method and its potential for real field applications. One of the main advances involved the development of artificial neural network (ANN) models for correlating compressive strength and UPV measurements. Another examined problem was how to deal with the large amount of raw data derived from inspection of large structures. Several studies were carried out to check different mapping techniques, as reported by Lorenzi et al. 2011. This paper relates one investigation where UPV and rebound hammer (RH) measurements were collected from a beam containing several induced defects, simulated using different materials. The results were processed using a mapping strategy, which indicated suspicious points where core extraction was undertaken. All cores taken from points derived from UPV results were found to have flaws providing evidence that this may be a suitable tool to assess concrete structures, when data is properly interpreted.
Revista IBRACON de Estruturas e Materiais | 2011
Alexandre Lorenzi; Luiz Carlos Pinto da Silva Filho; Joao Luiz Campagnolo
Nondestructive Testing (NDT) techniques are useful tools for analyzing reinforced concrete (RC) structures. The use of Ultrasonic Pulse Velocity (UPV) measurements enables monitoring changes in some critical characteristics of concrete over the service life of a structure. Nonetheless, the current techniques for UPV data analysis are largely based on the sensitivity of the professionals who apply these tests. For accurate diagnosis it is necessary to consider the different factors and conditions that can affect the results. In order to properly control and inspect RC facilities it is essential to develop appropriate strategies to make the task of data interpretation easier and more accurate. This study is based on the idea that using Artificial Neural Networks (ANNs) is a feasible way to generate workable estimation models correlating concrete characteristics, density and compressive strength. The study shows that this goal is achievable and indicates that neural models perform better than traditional statistical models.
Revista IBRACON de Estruturas e Materiais | 2017
Alexandre Lorenzi; Bruno do Vale Silva; M. P. Barbosa; L. C. P. Silva Filho
Resumo This study aims the possibility of using the pull-out test results – bond tests steel-concrete, that has been successfully carried out by the research group APULOT since 2008 [1]. This research demonstrates that the correlation between bond stress and concrete compressive strength allows estimate concrete compressive strength. However to obtain adequate answers testing of bond steel-concrete is necessary to control the settings test. This paper aims to correlate the results of bond tests of type pull-out with its variables by using Artificial Neural Networks (ANN). Though an ANN is possible to correlate the known input data (age rupture, anchorage length, covering and compressive strength of concrete) with control parameters (bond stress steel-concrete). To generate the model it is necessary to train the neural network using a database with known input and output parameters. This allows estimating the correlation between the neurons in each layer. This paper shows the modeling of an ANN capable of performing a nonlinear approach to estimate the concrete compressive strength using the results of steel-concrete bond tests.
International Journal of Environment and Sustainable Development | 2017
Gisele Santoro Lamb; Isaltino A. Oliveira; G.G. Perera; Alexandra Passuello; Alexandre Lorenzi; Luiz Carlos Pinto da Silva Filho
The current trend in the management of urban drainage systems is to return to pre-development conditions that mimic the flow characteristics of natural ecosystems. To do so, cities must implement technologies designed to increase water infiltration and reduce the runoff speed. Pervious concrete is among the new technologies seeking to return paved urban areas to conditions that mirror original soil drainage properties. The prototype described in this study is based on specifications provided by Caderno de Encargos do Departamento de Esgotos Pluviais (DEP) from Porto Alegre, where prototypes of grids similar to those produced by DEP are described, however our prototype was made using pervious concrete. Tests were conducted to compare the mechanical strength of our pervious model and those of DEP, made with conventional concrete. Pervious concrete performed better than conventional concrete in all assessments, suggesting that the application of this technology could be a viable alternative.
Revista de la Asociación Latinoamericana de Control de Calidad, Patología y Recuperación de la Construcc | 2012
Alexandre Lorenzi; L. Fonseca Caetano; Joao Luiz Campagnolo; L. C. Pinto da Silva Filho
Revista de la Asociación Latinoamericana de Control de Calidad, Patología y Recuperación de la Construcc | 2011
L. C. Pinto da Silva Filho; Alexandre Lorenzi; Joao Luiz Campagnolo; A. J. Strieder; U. C. de M. Quinino; Luciane Fonseca Caetano
Revista IBRACON de Estruturas e Materiais | 2017
Deise Santos Adamatti; Alexandre Lorenzi; Josué Argenta Chies; L. C. P. Silva Filho
Learning and Nonlinear Models | 2011
Alexandre Lorenzi; Luiz Carlos Pinto da Silva Filho
Revista IBRACON de Estruturas e Materiais | 2018
Fernanda Bianchi Pereira da Costa; Alexandre Lorenzi; Liv Haselbach; Luiz Carlos Pinto da Silva Filho
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Luiz Carlos Pinto da Silva Filho
Universidade Federal do Rio Grande do Sul
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