Investigation of Acoustic Properties of Poroelastic Asphalt Mixtures in Laboratory and Field Conditions

Abstract

Measures for the improvement of acoustic conditions in the vicinity of roads include the construction of pavement structures with low-noise surfaces with optimal macrotexture and the highest possible sound absorption coefficient. Laboratory evaluation of acoustic properties of a designed asphalt mixture before its placement in the pavement is a good solution. Currently, the most popular method for the determination of the sound absorption coefficient of various construction materials under laboratory conditions is the Kundt’s tube test. Sound absorption coefficient can also be assessed based on field and laboratory measurements performed using a Spectronics ACUPAVE System. Other parameters characterising the acoustic properties of road pavement courses include air void content and water drainability or permeability. The article presents an analysis of results of sound absorption coefficient obtained using a Spectronics ACUPAVE System and water drainability and permeability of poroelastic mixtures obtained both in laboratory and on test sections, in relation to air void content and grading of the mixtures. It was established that poroelastic mixtures containing an aggregate of maximum particle size of 5 mm are characterised by better acoustic properties than mixtures with a maximum aggregate particle size of 8 mm. Changes of crumb rubber aggregate grading and bitumen type (within the tested range of values) as well as the addition of lime have shown no evident influence on the sound absorption coefficient. Noise level values at the speed of 30 km/h according to the CPX method were measured as well. Relationships between sound absorption coefficient, water drainability/permeability, and air void content were determined. The performed analyses confirmed that Spectronics ACUPAVE System may be applied for evaluation of acoustic properties of asphalt mixtures in laboratory conditions, but further research is needed to reduce the uncertainty of the results.