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Optimized height of noise barrier for non-urban highway using artificial neural network
Kumar, K.; Parida, M. & Katiyar, V. K.
Abstract
This study applies artificial neural network
(ANN) for the determination of optimized height of a
highway noise barrier. Field measurements were carried
out to collect traffic volume, vehicle speed, noise level, and
site geometry data. Barrier height was varied from 2 to 5 m
in increments of 0.1 m for each measured data set to
generate theoretical data for network design. Barrier
attenuation was calculated for each height increment using
Federal Highway Administration model. For neural network
design purpose, classified traffic volume, corresponding
traffic speed, and barrier attenuation data have
been taken as input parameters, while barrier height was
considered as output. ANNs with different architectures
were trained, cross validated, and tested using this theoretical
data. Results indicate that ANN can be useful to
determine the height of noise barrier accurately, which can
effectively achieve the desired noise level reduction, for a
given set of traffic volume, vehicular speed, highway
geometry, and site conditions.
Keywords
Attenuation; Central pollution control board; Federal highway administration; Traffic
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