Application of Artificial Neural Networks for Noise Barrier Optimization

• Main Authors: Paulo Henrique Trombetta Zannin, Eriberto Oliveira do Nascimento, Elaine Carvalho da Paz, Felipe do Valle
• Format: Article
• Language: English
• Published: MDPI AG 2018-12-01
• Series: Environments
• Subjects: Artificial Neural Networks; effects analysis; Design of Experiments; traffic noise; noise impact; sound pollution; sound barrier; educational environment
• Online Access: https://www.mdpi.com/2076-3298/5/12/135

In the modern world, noise pollution continues to be a major problem that impairs people’s health, and road traffic is a primary contributor to noise emissions. This article describes an environmental impact study of the noise generated by the reconstruction of an urban section of a highway. Noise maps were calculated, and an environmental impact matrix was generated to determine the environmental impact of this reconstruction. The implementation of noise barriers was simulated based on these noise maps, and the effectiveness of the barriers was evaluated using Artificial Neural Networks (ANNs) combined with Design of Experiments (DoE). A functional variable significance analysis was then made for two parameters, namely, the coefficient of absorption of the barrier material and the barrier height. The aim was to determine the influence of these parameters on sound attenuation and on the formation of acoustic shadows. The results obtained from the ANNs and DoE were consistent in demonstrating that the absorption coefficient strongly influences the noise attenuation provided by noise barriers, while barrier height is correlated with the formation of larger areas of acoustic shadow. The environmental impact matrix also indicates that the existence of noise pollution has a negative effect on the environment, but that this impact can be reversed or minimized. The application of simulated noise barriers demonstrated that noise levels can be reduced to legally acceptable levels.