• rubberized asphalt roads
• support vector machine
• tyre cavity noise
• tyre rolling noise

Exposure to noise has significant impacts on the physical, mental health and well-being
of humans and animals. Between the different anthropogenic noise sources, transport
noise plays a major source due to its extension. An increment of car transportation and
road freight traffic highlights the need to enhance the transportation system, as this is
connected to air and noise pollution. As the number of passenger vehicles increases,
this urges to address the traffic problem in different ways. Implementation of low-noise
pavements and pavement monitoring are ways to reduce the noise emission due to the
tyre pavement interaction and the air pollutant emissions due to the tyre consumption
and the vehicle engine.
In this thesis, pavement monitoring and tyre rolling noise mechanisms are studied with
methods different regarding traditional ones. The two main elements presented are the use
of tyre cavity microphones for pavement monitoring and the implementation of machine
learning algorithms to investigate the tyre rolling noise mechanisms. For this, an initial
research focused on experimentally verifying the tyre cavity noise acoustic classification
in relation to the standard pavement condition index. The research aimed to develop
a measurement device able to relate pavement surface condition with its emission. The
results show good agreements and the need to develop an improved system. Following, to
increase the knowledge about tyre cavity noise signal, various measurements at different
speeds were carried out on four low-noise pavements. The objective was the further
comprehension of the speed relationship of the tyre cavity noise and its relationship with
the exterior noise measured with the standard Close ProXimity method. The study also
focused on the resonance modes in loaded and travelling conditions. To introduce to the
complexity of the tyre road noise, the next research topic focused on the acoustic and
surface description of an innovative low-noise pavement that is characterized by its low
thickness and high porosity. The results in time of the pavement surface and noise emission
are presented, confirming the good acoustical benefits of the pavement. Finally, the use
of machine learning algorithm was applied to distinguish the noise emission of rubberized
asphalt pavements with crumb rubber insertion (Dry and Wet method), and pavements
with no crumb rubber insertion. The method here presented gives an introductory way
to compare different pavements mix by predicting the tyre road noise with a selected
pavement texture surface, air temperature and tyre hardness.