Continuous noise mapping predication techniques using the stochastic modelling

A strategic noise map provides important information for noise impact assessment. However, current practices still use the unstandardised way which produces unreliable information for noise exposure monitoring. This research aims to develop new noise mapping prediction technologies in order to enhan...

Full description

Saved in:

Bibliographic Details
Main Author:	Lim, Ming Han
Format:	Thesis
Language:	English
Published:	2017
Subjects:	TA Engineering (General) Civil engineering (General)
Online Access:	http://eprints.utm.my/id/eprint/79351/1/LimMingHanPFKA2017.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utm-ep.79351
record_format	uketd_dc
spelling	my-utm-ep.793512018-10-14T08:45:53Z Continuous noise mapping predication techniques using the stochastic modelling 2017 Lim, Ming Han TA Engineering (General). Civil engineering (General) A strategic noise map provides important information for noise impact assessment. However, current practices still use the unstandardised way which produces unreliable information for noise exposure monitoring. This research aims to develop new noise mapping prediction technologies in order to enhance the current noise prediction method and noise monitoring practices. The research work was divided into preliminary and primary studies. In the preliminary study, a survey was conducted to investigate current noise exposure problems among Malaysian industries. Questionnaires were designed based on the proposed theoretical framework and distributed to 215 respondents from six workplaces with different industrial background. The finding shows that only 10.7 % of respondents wear hearing protectors regularly, thus implies a high risk of noise exposure problems in these industries. Based on the results of the Chi-square test, the utilisation rate of hearing protectors was not affected by noise awareness and training factors, but it could be increased through supervision and provision of safety information. The primary research study proposed two prediction methods, namely a noise prediction chart and stochastic modelling to be used in the development of both the automation and stochastic simulation frameworks. An automation framework is a system that automatically refers to a noise prediction chart in predicting the noise levels at receiving points. A stochastic simulation framework incorporates a random walk process and Monte Carlo approach to simulate movement and noise emission levels of machinery in a defined mapping area. Two prototyping softwares, namely Prototype I and Prototype II, were programmed using the MATLAB programming software in order to facilitate each proposed framework. Both prototyping software generated outputs such as strategic noise map, noise risk zone, and noise information. For software validation, a comparison of prediction and measurement results from case studies was performed. Eight case studies of field measurements from different industries were used to obtain the prediction inputs and noise levels from control points. The absolute differences between prediction and measurement values at the control points were computed to determine the accuracy of prediction results for each prototype. In general, the prediction results of Prototype I and II had a good agreement (≤ 3 dBA) with the results obtained from measurement for most of the case studies. Both prototypes could reflect the complex and dynamic noise circumstances in a workplace. This study has significantly in advanced noise mapping prediction technologies and the prototypes produced could be beneficial as new noise monitoring tools in current industrial practices. 2017 Thesis http://eprints.utm.my/id/eprint/79351/ http://eprints.utm.my/id/eprint/79351/1/LimMingHanPFKA2017.pdf application/pdf en public phd doctoral Universiti Teknologi Malaysia, Faculty of Civil Engineering Faculty of Civil Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TA Engineering (General) Civil engineering (General)
spellingShingle	TA Engineering (General) Civil engineering (General) Lim, Ming Han Continuous noise mapping predication techniques using the stochastic modelling
description	A strategic noise map provides important information for noise impact assessment. However, current practices still use the unstandardised way which produces unreliable information for noise exposure monitoring. This research aims to develop new noise mapping prediction technologies in order to enhance the current noise prediction method and noise monitoring practices. The research work was divided into preliminary and primary studies. In the preliminary study, a survey was conducted to investigate current noise exposure problems among Malaysian industries. Questionnaires were designed based on the proposed theoretical framework and distributed to 215 respondents from six workplaces with different industrial background. The finding shows that only 10.7 % of respondents wear hearing protectors regularly, thus implies a high risk of noise exposure problems in these industries. Based on the results of the Chi-square test, the utilisation rate of hearing protectors was not affected by noise awareness and training factors, but it could be increased through supervision and provision of safety information. The primary research study proposed two prediction methods, namely a noise prediction chart and stochastic modelling to be used in the development of both the automation and stochastic simulation frameworks. An automation framework is a system that automatically refers to a noise prediction chart in predicting the noise levels at receiving points. A stochastic simulation framework incorporates a random walk process and Monte Carlo approach to simulate movement and noise emission levels of machinery in a defined mapping area. Two prototyping softwares, namely Prototype I and Prototype II, were programmed using the MATLAB programming software in order to facilitate each proposed framework. Both prototyping software generated outputs such as strategic noise map, noise risk zone, and noise information. For software validation, a comparison of prediction and measurement results from case studies was performed. Eight case studies of field measurements from different industries were used to obtain the prediction inputs and noise levels from control points. The absolute differences between prediction and measurement values at the control points were computed to determine the accuracy of prediction results for each prototype. In general, the prediction results of Prototype I and II had a good agreement (≤ 3 dBA) with the results obtained from measurement for most of the case studies. Both prototypes could reflect the complex and dynamic noise circumstances in a workplace. This study has significantly in advanced noise mapping prediction technologies and the prototypes produced could be beneficial as new noise monitoring tools in current industrial practices.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Lim, Ming Han
author_facet	Lim, Ming Han
author_sort	Lim, Ming Han
title	Continuous noise mapping predication techniques using the stochastic modelling
title_short	Continuous noise mapping predication techniques using the stochastic modelling
title_full	Continuous noise mapping predication techniques using the stochastic modelling
title_fullStr	Continuous noise mapping predication techniques using the stochastic modelling
title_full_unstemmed	Continuous noise mapping predication techniques using the stochastic modelling
title_sort	continuous noise mapping predication techniques using the stochastic modelling
granting_institution	Universiti Teknologi Malaysia, Faculty of Civil Engineering
granting_department	Faculty of Civil Engineering
publishDate	2017
url	http://eprints.utm.my/id/eprint/79351/1/LimMingHanPFKA2017.pdf
_version_	1747818207307104256

Continuous noise mapping predication techniques using the stochastic modelling

Similar Items