Enhanced Environmental Sustainability for the Acoustic Absorption Properties of Cabuya Fiber in Building Construction Using Machine Learning Predictive Model

Abstract

Sustainability in construction is a growing concern due to the significant polluting waste generated before, during, and after a building’s life cycle. The use of natural materials can significantly reduce the environmental footprint in obtaining, manufacturing, transportation, execution, use, maintenance, and demolition of the building, especially when locally sourced. Natural fibers, in particular, can be used in room acoustics, offering good acoustic absorption while meeting sustainability goals. The objectives of this paper are to evaluate cabuya fiber, grown in Ecuador, as an acoustic absorbing material and to introduce a novel approach using machine learning to simulate the material’s acoustic properties. Eight samples of cabuya fiber, bound with a solution of water and Ca(OH)2, were prepared with thicknesses between 12 and 30.6 mm. The sound absorption coefficients (SACs) were calculated using an impedance tube, following international standards. A Gaussian regression model was built for the predictions. The results showed that the 30.6 mm sample achieved maximum absorption coefficients of 0.91 at 2 kHz and 0.9 at 5 kHz. The model predictions are very accurate, with a mean square error of just 0.0002. These findings offer valuable insights into using cabuya fiber and advanced predictive models to enhance building acoustic performance and reduce environmental impact.