Application of the Monte Carlo method to estimate the uncertainty in the compressive strength test of high-strength concrete modelled with a multilayer perceptron

Abstract: The use of artificial neural networks as a modeling tool for the physic-mechanical properties of diverse materials has experienced great advances in the last ten years, mainly due to the increased in computing capacities of computers. This technique has been used in many different fields o...

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Autores principales:	Moromi-Nakata,Isabel, García-Fernández,Francisco, Torre-Carrillo,Ana, Espinoza-Haro,Pedro, Acuña-Pinaud,Luis
Lenguaje:	English
Publicado:	Escuela de Construcción Civil, Pontificia Universidad Católica de Chile 2018
Materias:	Artificial neural network compressive strength high-strength concrete uncertainty Monte Carlo GUM
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-915X2018000200319
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spelling	oai:scielo:S0718-915X20180002003192018-11-15Application of the Monte Carlo method to estimate the uncertainty in the compressive strength test of high-strength concrete modelled with a multilayer perceptronMoromi-Nakata,IsabelGarcía-Fernández,FranciscoTorre-Carrillo,AnaEspinoza-Haro,PedroAcuña-Pinaud,Luis Artificial neural network compressive strength high-strength concrete uncertainty Monte Carlo GUM Abstract: The use of artificial neural networks as a modeling tool for the physic-mechanical properties of diverse materials has experienced great advances in the last ten years, mainly due to the increased in computing capacities of computers. This technique has been used in many different fields of science and its effectiveness is sufficiently proven. Its application in the particle board industry complies with the requirements of the test regulations for the use in production control, as an alternative method to normalized one. However, in spite of providing a result with a great approximation, they do not indicate anything about the uncertainty of the result. This last point is crucial when the results have to be compared with a product standard. There are internationally accepted deterministic techniques for obtaining the uncertainty of a test result, always starting from the knowledge of the function that relates the measure with the measurement parameters. However, these techniques are not entirely adequate for the case of excessively complex functions such as an artificial neural network. In these cases, the use of stochastic simulation methods such as the Monte Carlo method is more appropriate. In this article, an artificial neural network will be developed to obtain the compressive strength of high-strength concrete to later obtain the uncertainty by a Monte Carlo simulation.info:eu-repo/semantics/openAccessEscuela de Construcción Civil, Pontificia Universidad Católica de ChileRevista de la construcción v.17 n.2 20182018-08-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-915X2018000200319en10.7764/rdlc.17.2.319
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Artificial neural network compressive strength high-strength concrete uncertainty Monte Carlo GUM
spellingShingle	Artificial neural network compressive strength high-strength concrete uncertainty Monte Carlo GUM Moromi-Nakata,Isabel García-Fernández,Francisco Torre-Carrillo,Ana Espinoza-Haro,Pedro Acuña-Pinaud,Luis Application of the Monte Carlo method to estimate the uncertainty in the compressive strength test of high-strength concrete modelled with a multilayer perceptron
description	Abstract: The use of artificial neural networks as a modeling tool for the physic-mechanical properties of diverse materials has experienced great advances in the last ten years, mainly due to the increased in computing capacities of computers. This technique has been used in many different fields of science and its effectiveness is sufficiently proven. Its application in the particle board industry complies with the requirements of the test regulations for the use in production control, as an alternative method to normalized one. However, in spite of providing a result with a great approximation, they do not indicate anything about the uncertainty of the result. This last point is crucial when the results have to be compared with a product standard. There are internationally accepted deterministic techniques for obtaining the uncertainty of a test result, always starting from the knowledge of the function that relates the measure with the measurement parameters. However, these techniques are not entirely adequate for the case of excessively complex functions such as an artificial neural network. In these cases, the use of stochastic simulation methods such as the Monte Carlo method is more appropriate. In this article, an artificial neural network will be developed to obtain the compressive strength of high-strength concrete to later obtain the uncertainty by a Monte Carlo simulation.
author	Moromi-Nakata,Isabel García-Fernández,Francisco Torre-Carrillo,Ana Espinoza-Haro,Pedro Acuña-Pinaud,Luis
author_facet	Moromi-Nakata,Isabel García-Fernández,Francisco Torre-Carrillo,Ana Espinoza-Haro,Pedro Acuña-Pinaud,Luis
author_sort	Moromi-Nakata,Isabel
title	Application of the Monte Carlo method to estimate the uncertainty in the compressive strength test of high-strength concrete modelled with a multilayer perceptron
title_short	Application of the Monte Carlo method to estimate the uncertainty in the compressive strength test of high-strength concrete modelled with a multilayer perceptron
title_full	Application of the Monte Carlo method to estimate the uncertainty in the compressive strength test of high-strength concrete modelled with a multilayer perceptron
title_fullStr	Application of the Monte Carlo method to estimate the uncertainty in the compressive strength test of high-strength concrete modelled with a multilayer perceptron
title_full_unstemmed	Application of the Monte Carlo method to estimate the uncertainty in the compressive strength test of high-strength concrete modelled with a multilayer perceptron
title_sort	application of the monte carlo method to estimate the uncertainty in the compressive strength test of high-strength concrete modelled with a multilayer perceptron
publisher	Escuela de Construcción Civil, Pontificia Universidad Católica de Chile
publishDate	2018
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-915X2018000200319
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Application of the Monte Carlo method to estimate the uncertainty in the compressive strength test of high-strength concrete modelled with a multilayer perceptron

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