Automated construction noise prediction by considering the variability of noise sources and outdoor sound propagation
Noise has become a serious concern due to increase of construction development. Continuous exposures to excessive noise result in physical, physiological and psychological effects. To reduce these effects, the prediction of noise from construction in the early planning stage is suggested. In Malaysi...
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| Format: | Thesis |
| Language: | English |
| Published: |
2014
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/78098/1/ZanariahJahyaMFKA20141.pdf |
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| Summary: | Noise has become a serious concern due to increase of construction development. Continuous exposures to excessive noise result in physical, physiological and psychological effects. To reduce these effects, the prediction of noise from construction in the early planning stage is suggested. In Malaysia, the prediction is based on the BS5228: Part 1: 2009 procedure. However, the equivalent noise level (LAeq) prediction from BS5228 was claimed to be inaccurate, and previous research suggested that the primary solution is to predict noise using stochastic approach. Nonetheless, the predictions of noise using stochastic approach were not carried out in a detail manner and not all factors that may affect the noise were considered. Therefore, this study further investigates the accuracy of the noise prediction by using BS5228 procedure, followed by improving the method of noise prediction using stochastic approach and develops an automated model for noise prediction. Among considered factors include the variability of position and height of the sources, as well as receiver and variability of outdoor sound propagation. The automated model was designed using MATLAB’s Graphical User Interface (GUI) and produced equivalent continuous sound level, LAeq, standard deviation and other parameters of noise levels such as L10, L90 and Lmax. The accuracy between measured and predicted noise levels was measured using statistical tests in SPSS (Statistical Package for Social Science) software and also using MAPE (Mean Absolute Percentage Error) method. The result of t-test showed significant difference between LAeq obtained from measurement and BS 5228 procedure. Meanwhile, the comparison of LAeq between measurement and simulation was insignificant throughout t-test and overall, the results from MAPE method were also in the acceptable range. As a conclusion, noise prediction using Monte Carlo approach can be used as the alternative way in predicting noise from construction. |
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