Assessment of asphalt concrete acoustic performance in urban streets

Smartphone Sensing of Road Surface Condition and Defect Detection

Road surface condition is vitally important for road safety and transportation efficiency. Conventionally, road surface monitoring relies on specialised vehicles equipped with professional devices, but such dedicated large-scale road surveying is usually costly, time-consuming, and prohibitively difficult for frequent pavement condition monitoring—for example, on an hourly or daily basis. Current advances in technologies such as smartphones, machine learning, big data, and cloud analytics have enabled the collection and analysis of a great amount of field data from numerous users (e.g., drivers) whilst driving on roads. In this regard, we envisage that a smartphone equipped with an accelerometer and GPS sensors could be used to collect road surface condition information much more frequently than specialised equipment. In this study, accelerometer data were collected at low rate from a smartphone via an Android-based application over multiple test-runs on a local road in Ireland. These data were successfully processed using power spectral density analysis, and defects were later identified using a k-means unsupervised machine learning algorithm, resulting in an average accuracy of 84%. Results demonstrated the potential of collecting crowdsourced data from a large population of road users for road surface defect detection on a quasi-real-time basis. This frequent reporting on a daily/hourly basis can be used to inform the relevant stakeholders for timely road maintenance, aiming to ensure the road’s serviceability at a lower inspection and maintenance cost.

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.

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

Sound level in close proximity to the contact patch of the tire and road (L(CPtr)) is analyzed as a function of the vehicle speed in the acoustic frequency range, showing different behavior depending on frequency. At least two regions are observed; one at low frequencies, where the variation in sound with speed (coefficient B) increases with frequency, and the other at higher frequencies, where such sound/speed variation does not increase significantly with frequency. The dependence of B at low frequencies seems to be correlated with the high sound absorption of this porous surface.