Title: Experimental measurements and mathematical model of vehicle noise using artificial neural network

Authors: Hichem Hassine; Maher Barkallah; Jamel Louati; Mohamed Haddar

Addresses: Laboratory of Mechanics, Modelling and Production (LA2MP), National School of Engineers of Sfax, University of Sfax, BP 1173, Sfax 3083, Tunisia Fax: (00216)74666535 ' Laboratory of Mechanics, Modelling and Production (LA2MP), National School of Engineers of Sfax, University of Sfax, BP 1173, Sfax 3083, Tunisia Fax: (00216)74666535 ' Laboratory of Mechanics, Modelling and Production (LA2MP), National School of Engineers of Sfax, University of Sfax, BP 1173, Sfax 3083, Tunisia Fax: (00216)74666535 ' Laboratory of Mechanics, Modelling and Production (LA2MP), National School of Engineers of Sfax, University of Sfax, BP 1173, Sfax 3083, Tunisia Fax: (00216)74666535

Abstract: The road transport sector plays a vital role in economic development. Although it is an essential element in regional development schemes, it generates negative externalities, thus constituting one of the most important sources of environmental pollution. Indeed, noise pollution will continue to increase in magnitude and severity as a result of population growth, urbanisation and growth associated with automobile use. In this paper, we propose to model vehicle noise based on an experimental study of vehicle noise in three different sites. We implement an artificial neural network (ANN) to model vehicle and traffic noise. The results demonstrate that vehicle characteristics, traffic flow and infrastructure have an important influence on the noise level of road traffic. It is observed that the ANN model can predict traffic noise with a correlation coefficient in the range of 0.98-0.99, which demonstrates the efficiency of the developed model to estimate vehicle noise.

Keywords: sustainable development; noise measurement; artificial neural network; ANN; vehicle noise.

DOI: 10.1504/IJVNV.2021.123414

International Journal of Vehicle Noise and Vibration, 2021 Vol.17 No.3/4, pp.121 - 136

Received: 27 Feb 2020
Accepted: 22 Oct 2020
Published online: 20 Jun 2022 *

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