Implementation in MATLAB of the adaptive Monte Carlo method for the evaluation of measurement uncertainties

Fernández, Manuel Solaguren-Beascoa; Alegre, J.M.; Díez, Pedro Miguel Bravo

doi:10.1007/s00769-008-0475-6

Cited by 49 publications

(18 citation statements)

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“…the robustness of the method should be tested using a higher number of Monte Carlo trials. For a more rigorous analysis, such as the calculation of confidence intervals, the adaptive Monte Carlo method given by [33,35] may be implemented in future work. The method can also be used to study the influence of each uncertainty by itself, such as the temperature sensor accuracy, to determine which input quantity uncertainty should be decreased primarily.…”

Section: Discussionmentioning

confidence: 99%

Characterizing phase change materials using the T-History method: On the factors influencing the accuracy and precision of the enthalpy-temperature curve

et al. 2018

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Section: Discussionmentioning

confidence: 99%

Characterizing phase change materials using the T-History method: On the factors influencing the accuracy and precision of the enthalpy-temperature curve

et al. 2018

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“…El número de simulaciones dependerá del grado de tolerancia que se desee para el resultado final. Por regla general para unos intervalos del 95% de cobertura suelen ser necesarias unas 10 6 simulaciones [ 3,8].…”

Section: Concreto De Alta Resistenciaunclassified

“…Sin embargo la red neuronal no proporciona ninguna información sobre los intervalos de confianza o la incertidumbre del valor de salida [1]. Esta incertidumbre es importante no sólo porque es indicativa de la calidad de nuestro proceso de medida, sino porque a la vez nos proporciona un intervalo de confianza sobre nuestro resultado [2,3].…”

Section: Introductionunclassified

Modelización del ensayo de resistencia a compresión del concreto de alta resistencia mediante una red neuronal artificial. Obtención de la incertidumbre del resultado

2018

Rev. ECIPeru

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Modelización del ensayo de resistencia a compresión del concreto de alta resistencia mediante una red neuronal artificial. Obtención de la incertidumbre del resultado Modeling the resistance to compression of high performance concrete test by means of an artificial neural network. Obtaining the uncertainty of the results Francisco García Fernández1, Ana Torre Carrillo2, Isabel Moromi Nakata2, Pedro Espinoza Haro3 y Luis Acuña Pinaud3 1 Dpto. Sistemas y Recursos Naturales. Universidad Politécnica de Madrid. Ciudad Universitaria S/N, 28040 Madrid, España 2 Facultad de Ingeniería Civil, Universidad Nacional de Ingeniería. Av. Túpac Amaru, 210. Lima 25, Perú 3 Facultad de Ingeniería Industrial y de Sistemas. Universidad Nacional de Ingeniería. Av. Túpac Amaru, 210. Lima 25, Perú DOI: https://doi.org/10.33017/RevECIPeru2015.0012/ Resumen En los últimos años las ANN han tenido un gran desarrollo en el control de procesos industriales debido principalmente a su capacidad de modelizar relaciones complejas, que los sistemas tradicionales no han sido capaces de hacer, y predecir satisfactoriamente si las características de un producto se adecuan o no a las especificaciones correspondientes. Estas estructuras han sido ampliamente utilizadas en la caracterización de otros materiales como cemento, hormigón, algunos metales o la madera. El perceptrón multicapa, una de las redes neuronales artificiales más populares, se ha convertido en los últimos tiempos en una potente herramienta de modelización en numerosos campos que van desde las finanzas, a la ingeniería o la medicina. Esta herramienta consigue mejorar sustancialmente cualquier modelo previo propuesto para modelizar cualquier sistema independientemente de la naturaleza de éste, con la ventaja añadida de que no necesitan ninguna suposición previa sobre la estructura de los datos Sin embargo, la red sólo proporciona el valor de la salida sin ninguna información acerca de su precisión. La obtención de la incertidumbre de salida es importante, no sólo porque proporciona un intervalo de confianza sobre el valor de salida, sino porque da una idea de la calidad del método de medida. Esta incertidumbre procede de dos fuentes, por un lado el ruido inherente a los valores de entrada y por otro la simplificación del fenómeno que todo modelo matemático supone. En este trabajo se va a desarrollar una nueva metodología para obtener tanto la incertidumbre como los intervalos de confianza de la salida de un modelo específico de red neuronal, el perceptrón multicapa, basándose en el método de simulación de Montecarlo especificado en Suplemento 1 de la GUM para posteriormente aplicarlo a la modelizacion del ensayo de resistencia a compression del concreto. Descriptores: Concreto de alta resistencia, red neuronal artificial, resistencia a compresión, incertidumbre, Método de Monte Carlo Abstract Major advances have been made with the use of ANNs in recent years in industrial process control, mainly because they are capable of modeling complex relations, unlike conventional systems, and can adequately predict whether or not the characteristics of a product are in line with specifications. They have been widely used to characterize other materials such as cement, concrete, certain metals or wood. The multilayer perceptron, one of the most popular artificial neural networks, has become a powerful modeling tool in numerous fields, ranging from finances to engineering and medicine. This tool is capable of considerably improving on all previous models proposed for modeling any system, regardless of its nature, with the added advantage that no prior assumption on the structure of the data is necessary. However, the network provides only the output value, with no information about its accuracy. Obtaining the output uncertainty is important, not only because it provides a coverage interval for the output value, but also because it indicates the quality of the measuring method. This uncertainty comes from two sources: firstly, the inherent uncertainty in the input data, and secondly, the simplification of the phenomenon involved in any mathematical model. This study develops a new methodology for obtaining both the output uncertainty and coverage intervals of a specific neural network model - the multilayer perceptron - based on the Monte Carlo simulation method indicated in Supplement 1 to the Guide to the Expression of Uncertainty in Measurement (GUM), in order to use it when modelling the test of resistance to compression of concrete. Keywords: High performance concrete, artificial neural network, resistance to compression, uncertainty, Monte Carlo method.

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“…The simulations performed for this study incorporated in part a MATLAB code developed by the Group of Structural Integrity of the University of Burgos [18,19] for adaptive Monte Carlo simulations in accordance with the guidelines of ISO Guide 98-3/Supplement 1.…”

Section: Summary Of Monte Carlo Simulationsmentioning

confidence: 99%

On the Uncertainty of the EN12354-1: Estimate of the Flanking Sound Reduction Index Due to the Uncertainty of the Input Data

Mahn

Pearse

2009

Building Acoustics

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Equations to calculate the uncertainty of the EN12354-1 estimate of the flanking sound reduction index due to the uncertainty of the input data are derived using the method of the ISO Guide to the Expression of Uncertainty in Measurement (GUM). The uncertainty equations have been validated using Monte Carlo simulations. It is shown that the magnitude of the uncertainty depends on the uncertainty of the resonant sound reduction indices of the elements, the uncertainty of the vibration reduction index and the uncertainty of the equivalent absorption lengths and areas of the elements. However, equations could not be derived to calculate the uncertainty of the EN12354 estimate of the apparent sound reduction index which has a lognormal probability density function and is therefore outside of the scope of the method of GUM. Monte Carlo simulations must be used to calculate the uncertainty of the apparent sound reduction index. It is recommended that guidance for calculating and declaring the uncertainty is included in future versions of EN12354, ISO10848 and ISO15712.

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Implementation in MATLAB of the adaptive Monte Carlo method for the evaluation of measurement uncertainties

Cited by 49 publications

References 9 publications

Characterizing phase change materials using the T-History method: On the factors influencing the accuracy and precision of the enthalpy-temperature curve

Characterizing phase change materials using the T-History method: On the factors influencing the accuracy and precision of the enthalpy-temperature curve

Modelización del ensayo de resistencia a compresión del concreto de alta resistencia mediante una red neuronal artificial. Obtención de la incertidumbre del resultado

On the Uncertainty of the EN12354-1: Estimate of the Flanking Sound Reduction Index Due to the Uncertainty of the Input Data

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