Modeling of Construction Noise for Environmental Impact Assessment

This study measured the noise levels generated at different construction sites in reference to the stage of construction and the equipment used, and examined the methods to predict such noise in order to assess the environmental impact of noise. It included 33 construction sites in Kuwait and used...

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Main Author: Hamoda, Mohamed F.
Format: Article
Language:English
Published: Penerbit Universiti Sains Malaysia 2008
Subjects:
Online Access:http://eprints.usm.my/42212/
http://eprints.usm.my/42212/1/5_Mohamed_Hamoda_%28p._79-89%29.pdf
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author Hamoda, Mohamed F.
author_facet Hamoda, Mohamed F.
author_sort Hamoda, Mohamed F.
building USM Institutional Repository
collection Online Access
description This study measured the noise levels generated at different construction sites in reference to the stage of construction and the equipment used, and examined the methods to predict such noise in order to assess the environmental impact of noise. It included 33 construction sites in Kuwait and used artificial neural networks (ANNs) for the prediction of noise. A back-propagation neural network (BPNN) model was compared with a general regression neural network (GRNN) model. The results obtained indicated that the mean equivalent noise level was 78.7 dBA which exceeds the threshold limit. The GRNN model was superior to the BPNN model in its accuracy of predicting construction noise due to its ability to train quickly on sparse data sets. Over 93% of the predictions were within 5% of the observed values. The mean absolute error between the predicted and observed data was only 2 dBA. The ANN modeling proved to be a useful technique for noise predictions required in the assessment of environmental impact of construction activities.
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spelling usm-422122018-09-28T09:06:44Z http://eprints.usm.my/42212/ Modeling of Construction Noise for Environmental Impact Assessment Hamoda, Mohamed F. TH1-9745 Building construction This study measured the noise levels generated at different construction sites in reference to the stage of construction and the equipment used, and examined the methods to predict such noise in order to assess the environmental impact of noise. It included 33 construction sites in Kuwait and used artificial neural networks (ANNs) for the prediction of noise. A back-propagation neural network (BPNN) model was compared with a general regression neural network (GRNN) model. The results obtained indicated that the mean equivalent noise level was 78.7 dBA which exceeds the threshold limit. The GRNN model was superior to the BPNN model in its accuracy of predicting construction noise due to its ability to train quickly on sparse data sets. Over 93% of the predictions were within 5% of the observed values. The mean absolute error between the predicted and observed data was only 2 dBA. The ANN modeling proved to be a useful technique for noise predictions required in the assessment of environmental impact of construction activities. Penerbit Universiti Sains Malaysia 2008 Article PeerReviewed application/pdf en http://eprints.usm.my/42212/1/5_Mohamed_Hamoda_%28p._79-89%29.pdf Hamoda, Mohamed F. (2008) Modeling of Construction Noise for Environmental Impact Assessment. Journal of Construction in Developing Countries , 13 (1). pp. 79-89. ISSN 1823-6499 http://web.usm.my/jcdc/vol13_1_2008/5_Mohamed%20Hamoda%20(p.%2079-89).pdf
spellingShingle TH1-9745 Building construction
Hamoda, Mohamed F.
Modeling of Construction Noise for Environmental Impact Assessment
title Modeling of Construction Noise for Environmental Impact Assessment
title_full Modeling of Construction Noise for Environmental Impact Assessment
title_fullStr Modeling of Construction Noise for Environmental Impact Assessment
title_full_unstemmed Modeling of Construction Noise for Environmental Impact Assessment
title_short Modeling of Construction Noise for Environmental Impact Assessment
title_sort modeling of construction noise for environmental impact assessment
topic TH1-9745 Building construction
url http://eprints.usm.my/42212/
http://eprints.usm.my/42212/
http://eprints.usm.my/42212/1/5_Mohamed_Hamoda_%28p._79-89%29.pdf