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Optimized height of noise barrier for non-urban highway using artificial neural network

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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.

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Acknowledgments

Financial support by grant in aid (Grant No.: 09/143/(0721)/2009-EMR-I) to one of the authors (First Author of Manuscript) from the Council of Scientific and Industrial Research (CSIR), New Delhi, India has been gainfully utilized for this research work and the same is thankfully acknowledged.

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Authors and Affiliations

  1. Department of Mathematics, Indian Institute of Technology, Roorkee, 247667, India

    K. Kumar & V. K. Katiyar

  2. Department of Civil Engineering, Indian Institute of Technology, Roorkee, 247667, India

    M. Parida

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  1. K. Kumar
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  2. M. Parida
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Correspondence to K. Kumar.

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Kumar, K., Parida, M. & Katiyar, V.K. Optimized height of noise barrier for non-urban highway using artificial neural network. Int. J. Environ. Sci. Technol. 11, 719–730 (2014). https://doi.org/10.1007/s13762-013-0257-5

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  • DOI: https://doi.org/10.1007/s13762-013-0257-5

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