New regression models for predicting noise exposure in the driver's compartment of Malaysian army three-tonne trucks

Shamsul Akmar Ab Aziz, Mohd. Zaki Nuawi, Mohd. Jailani Mohd Nor, Dian Darina Indah Daruis

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

The objective of this study is to present a new method for determination of noise exposure in the driver's compartment of Malaysian Army (MA) three-tonne trucks based on changing vehicle speed using regression models and the statistical analysis method known as Integrated Kurtosis-based Algorithm for Z -notch filter (I-kaz). The test was conducted on two different road conditions: tarmac and dirt roads. Noise exposure was measured using a sound level meter which is capable of recording raw sound pressure in Pa, and comparisons were made between the two types of roads. The prediction of noise exposure was done using the developed regression models and 3D graphic representations of the I-kaz coefficient Z ∞. The results of the regression models show that Z ∞ increases when vehicle speed and noise exposure increase. For model validation, predicted and measured noise exposures were compared, and a relatively good agreement has been obtained between them. It was found that the predictions had high accuracies and low average relative errors. By using the regression models, we can easily predict noise exposure inside the truck driver's compartment. The proposed models are efficient and can be extended to the automotive industry for noise exposure monitoring.

Original languageEnglish
Article number616093
JournalAdvances in Mechanical Engineering
Volume2014
DOIs
Publication statusPublished - 2014

Fingerprint

Trucks
Truck drivers
Acoustic waves
Notch filters
Automotive industry
Acoustic noise
Statistical methods
Monitoring

ASJC Scopus subject areas

  • Mechanical Engineering

Cite this

New regression models for predicting noise exposure in the driver's compartment of Malaysian army three-tonne trucks. / Ab Aziz, Shamsul Akmar; Nuawi, Mohd. Zaki; Mohd Nor, Mohd. Jailani; Daruis, Dian Darina Indah.

In: Advances in Mechanical Engineering, Vol. 2014, 616093, 2014.

Research output: Contribution to journalArticle

@article{ca02c46c3c084bd786b150c7c958c9f8,
title = "New regression models for predicting noise exposure in the driver's compartment of Malaysian army three-tonne trucks",
abstract = "The objective of this study is to present a new method for determination of noise exposure in the driver's compartment of Malaysian Army (MA) three-tonne trucks based on changing vehicle speed using regression models and the statistical analysis method known as Integrated Kurtosis-based Algorithm for Z -notch filter (I-kaz). The test was conducted on two different road conditions: tarmac and dirt roads. Noise exposure was measured using a sound level meter which is capable of recording raw sound pressure in Pa, and comparisons were made between the two types of roads. The prediction of noise exposure was done using the developed regression models and 3D graphic representations of the I-kaz coefficient Z ∞. The results of the regression models show that Z ∞ increases when vehicle speed and noise exposure increase. For model validation, predicted and measured noise exposures were compared, and a relatively good agreement has been obtained between them. It was found that the predictions had high accuracies and low average relative errors. By using the regression models, we can easily predict noise exposure inside the truck driver's compartment. The proposed models are efficient and can be extended to the automotive industry for noise exposure monitoring.",
author = "{Ab Aziz}, {Shamsul Akmar} and Nuawi, {Mohd. Zaki} and {Mohd Nor}, {Mohd. Jailani} and Daruis, {Dian Darina Indah}",
year = "2014",
doi = "10.1155/2014/616093",
language = "English",
volume = "2014",
journal = "Advances in Mechanical Engineering",
issn = "1687-8132",
publisher = "Hindawi Publishing Corporation",

}

TY - JOUR

T1 - New regression models for predicting noise exposure in the driver's compartment of Malaysian army three-tonne trucks

AU - Ab Aziz, Shamsul Akmar

AU - Nuawi, Mohd. Zaki

AU - Mohd Nor, Mohd. Jailani

AU - Daruis, Dian Darina Indah

PY - 2014

Y1 - 2014

N2 - The objective of this study is to present a new method for determination of noise exposure in the driver's compartment of Malaysian Army (MA) three-tonne trucks based on changing vehicle speed using regression models and the statistical analysis method known as Integrated Kurtosis-based Algorithm for Z -notch filter (I-kaz). The test was conducted on two different road conditions: tarmac and dirt roads. Noise exposure was measured using a sound level meter which is capable of recording raw sound pressure in Pa, and comparisons were made between the two types of roads. The prediction of noise exposure was done using the developed regression models and 3D graphic representations of the I-kaz coefficient Z ∞. The results of the regression models show that Z ∞ increases when vehicle speed and noise exposure increase. For model validation, predicted and measured noise exposures were compared, and a relatively good agreement has been obtained between them. It was found that the predictions had high accuracies and low average relative errors. By using the regression models, we can easily predict noise exposure inside the truck driver's compartment. The proposed models are efficient and can be extended to the automotive industry for noise exposure monitoring.

AB - The objective of this study is to present a new method for determination of noise exposure in the driver's compartment of Malaysian Army (MA) three-tonne trucks based on changing vehicle speed using regression models and the statistical analysis method known as Integrated Kurtosis-based Algorithm for Z -notch filter (I-kaz). The test was conducted on two different road conditions: tarmac and dirt roads. Noise exposure was measured using a sound level meter which is capable of recording raw sound pressure in Pa, and comparisons were made between the two types of roads. The prediction of noise exposure was done using the developed regression models and 3D graphic representations of the I-kaz coefficient Z ∞. The results of the regression models show that Z ∞ increases when vehicle speed and noise exposure increase. For model validation, predicted and measured noise exposures were compared, and a relatively good agreement has been obtained between them. It was found that the predictions had high accuracies and low average relative errors. By using the regression models, we can easily predict noise exposure inside the truck driver's compartment. The proposed models are efficient and can be extended to the automotive industry for noise exposure monitoring.

UR - http://www.scopus.com/inward/record.url?scp=84900026365&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84900026365&partnerID=8YFLogxK

U2 - 10.1155/2014/616093

DO - 10.1155/2014/616093

M3 - Article

AN - SCOPUS:84900026365

VL - 2014

JO - Advances in Mechanical Engineering

JF - Advances in Mechanical Engineering

SN - 1687-8132

M1 - 616093

ER -