A machine-learning based approach to estimate acoustic macroscopic parameters of porous concrete

Abstract

Porous concrete with expanded clay inherent porosity makes it an interesting and effective acoustic material, applied in numerous scenarios such as highways, airports and architectural structures, due to its capacity to mitigate noise pollution, by absorbing and damping sound waves. It is usually accepted that macroscopic properties such as open porosity, tortuosity or airflow resistivity of such materials play a fundamental role in the definition of the internal absorption process. This study explores the application of tailored artificial neural networks (ANNs) for predicting first the macroscopic properties (open porosity, tortuosity and airflow resistivity) and then the sound absorption coefficient (α) of these porous concrete mixtures, using only two input parameters (size class of the expanded clay and density of the test specimens). The results demonstrate the efficacy of the proposed ANN approach in accurately predicting macroscopic properties and the sound absorption coefficient of these mixtures, making it possible to obtain such important parameters in an effective and much simpler way.