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
Abstract
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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