Toward the acoustical characterization of asphalt pavements: analysis of the tire/road sound from a porous surface

Assessment of an action against environmental noise: Acoustic durability of a pavement surface with crumb rubber

Environmental noise is a worldwide problem that has an adverse effect in the quality of life of urban population. Some work has shown that there is a correlation between environmental noise and health issues as sleep disturbance or annoyance. This study presents the time evolution of a test track fabricated with an asphalt mixture with 20% of crumb rubber by weight of bitumen, added by the wet process. A complete surface characterization has been performed by determining tire/pavement sound levels, road texture profiles, in-situ dynamic stiffness and sound absorption of compacted and extracted sample cores. Two measurement campaigns were performed: just after mixture laying and after 3 years in service. This study confirms that the use of crumb rubber as a modifier of bituminous binders (CRMB) can improve the pavement characteristics: gap-graded mixtures with crumb rubber can be used in the action plans as urban rehabilitation measure to fight noise pollution. However, this noise reduction seems to decrease with age at a rate of approximately 0.15 dB(A) per year.

Statistical classification of road pavements using near field vehicle rolling noise measurements

Low noise surfaces have been increasingly considered as a viable and cost-effective alternative to acoustical barriers. However, road planners and administrators frequently lack information on the correlation between the type of road surface and the resulting noise emission profile. To address this problem, a method to identify and classify different types of road pavements was developed, whereby near field road noise is analyzed using statistical learning methods. The vehicle rolling sound signal near the tires and close to the road surface was acquired by two microphones in a special arrangement which implements the Close-Proximity method. A set of features, characterizing the properties of the road pavement, was extracted from the corresponding sound profiles. A feature selection method was used to automatically select those that are most relevant in predicting the type of pavement, while reducing the computational cost. A set of different types of road pavement segments were tested and the performance of the classifier was evaluated. Results of pavement classification performed during a road journey are presented on a map, together with geographical data. This procedure leads to a considerable improvement in the quality of road pavement noise data, thereby increasing the accuracy of road traffic noise prediction models.