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

Isabel Moromi Nakata, Francisco García Fernández, Ana Torre Carrillo, Pedro Espinoza Haro, Luis Acuña Pinaud


DOI: 10.7764/RDLC.17.2.319

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.



Artificial neural network, compressive strength, high-strength concrete, uncertainty, Monte Carlo, GUM

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