Airborne sound insulation prediction of masonry walls using artificial neural networks

Abstract

In this paper, an alternative method for the calculation of masonry walls sound insulation is investigated. Thirty-four simple monolithic brick walls were examined. For the considered walls, the measurements made by accredited laboratories in compliance with ISO 10140 standard were collected. For each building element, the experimental measurements and all the information concerning the geometric and physical characteristics of all the components (material, dimensions, mass, density, Young’s modulus, Poisson’s module, hole content, etc.) were classified. Then, from the collection of measurements obtained in the standard laboratories and the division of them into homogeneous groups, a statistical sensitivity analysis to determine the most statistically significant parameter for the sound insulation of building walls was carried out. This analysis was a useful tool for selecting parameters to be used in forecast models and for calculating statistical effects. Finally, an alternative method based on the use of artificial neural networks (ANN) was proposed. This paper discusses the results obtained by applying the models to a specific kind of construction: the masonry walls. The results will show a good correlation of the values obtained on this first type of building construction. This encourages the extension of the method explained in this work to other types of walls.