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

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.