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Artificial Neural Network in Seismic Reflection Method for Measuring Asphalt Pavement Thickness

A non‐ destructive measurement of asphalt pavement layer thickness using seismic reflection was adopted together with coring test at similar site for comparison. The test was carried out on pavements around university campus’s road to measure the asphalt pavement layer thickness. The on-site seismic...

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Main Authors: Remmani, Sid Ahmed, Madun, Aziman, Mohd Kamaruddin, Nurul Hidayah, Laghouiter, Oussama
Format: Article
Language:English
Published: uthm 2023
Subjects:
T Technology (General)
Online Access:http://eprints.uthm.edu.my/11617/1/J16125_4f1e8defb35ea1e9460d8e31a1e82cac.pdf
http://eprints.uthm.edu.my/11617/
https://doi.org/10.30880/ijscet.2023.14.02.011
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spelling my.uthm.eprints.116172024-09-25T07:20:55Z http://eprints.uthm.edu.my/11617/ Artificial Neural Network in Seismic Reflection Method for Measuring Asphalt Pavement Thickness Remmani, Sid Ahmed Madun, Aziman Mohd Kamaruddin, Nurul Hidayah Laghouiter, Oussama T Technology (General) A non‐ destructive measurement of asphalt pavement layer thickness using seismic reflection was adopted together with coring test at similar site for comparison. The test was carried out on pavements around university campus’s road to measure the asphalt pavement layer thickness. The on-site seismic reflection testing was carried out using three piezoelectric sensors to capture time travel of wave motion, a light ball bearing to produce a high frequency seismic wave source and a data logger for data acquisition. The data processing is conducted in the time domain exclusively using a feedforward artificial neural network (ANN) using MATLAB software. A graphical interface is developed for viewing and extracting the result to make the processing of the seismic data feasible and user-friendly. The seismic reflection method analysis using the ANN successfully measured the asphalt pavement layer thickness. This study of the reflection method for measuring the pavement thickness compared with coring indicates the average accuracy of five testing sites was 93%. It shows that the seismic reflection able to demonstrate the capability to measure thickness of pavement in non-destructive way at a reliable accuracy. uthm 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/11617/1/J16125_4f1e8defb35ea1e9460d8e31a1e82cac.pdf Remmani, Sid Ahmed and Madun, Aziman and Mohd Kamaruddin, Nurul Hidayah and Laghouiter, Oussama (2023) Artificial Neural Network in Seismic Reflection Method for Measuring Asphalt Pavement Thickness. INTERNATIONAL JOURNAL OF SUSTAINABLE CONSTRUCTION ENGINEERING AND TECHNOLOGY, 14 (2). pp. 105-113. ISSN 2180-3242 https://doi.org/10.30880/ijscet.2023.14.02.011
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Remmani, Sid Ahmed
Madun, Aziman
Mohd Kamaruddin, Nurul Hidayah
Laghouiter, Oussama
Artificial Neural Network in Seismic Reflection Method for Measuring Asphalt Pavement Thickness
description A non‐ destructive measurement of asphalt pavement layer thickness using seismic reflection was adopted together with coring test at similar site for comparison. The test was carried out on pavements around university campus’s road to measure the asphalt pavement layer thickness. The on-site seismic reflection testing was carried out using three piezoelectric sensors to capture time travel of wave motion, a light ball bearing to produce a high frequency seismic wave source and a data logger for data acquisition. The data processing is conducted in the time domain exclusively using a feedforward artificial neural network (ANN) using MATLAB software. A graphical interface is developed for viewing and extracting the result to make the processing of the seismic data feasible and user-friendly. The seismic reflection method analysis using the ANN successfully measured the asphalt pavement layer thickness. This study of the reflection method for measuring the pavement thickness compared with coring indicates the average accuracy of five testing sites was 93%. It shows that the seismic reflection able to demonstrate the capability to measure thickness of pavement in non-destructive way at a reliable accuracy.
format Article
author Remmani, Sid Ahmed
Madun, Aziman
Mohd Kamaruddin, Nurul Hidayah
Laghouiter, Oussama
author_facet Remmani, Sid Ahmed
Madun, Aziman
Mohd Kamaruddin, Nurul Hidayah
Laghouiter, Oussama
author_sort Remmani, Sid Ahmed
title Artificial Neural Network in Seismic Reflection Method for Measuring Asphalt Pavement Thickness
title_short Artificial Neural Network in Seismic Reflection Method for Measuring Asphalt Pavement Thickness
title_full Artificial Neural Network in Seismic Reflection Method for Measuring Asphalt Pavement Thickness
title_fullStr Artificial Neural Network in Seismic Reflection Method for Measuring Asphalt Pavement Thickness
title_full_unstemmed Artificial Neural Network in Seismic Reflection Method for Measuring Asphalt Pavement Thickness
title_sort artificial neural network in seismic reflection method for measuring asphalt pavement thickness
publisher uthm
publishDate 2023
url http://eprints.uthm.edu.my/11617/1/J16125_4f1e8defb35ea1e9460d8e31a1e82cac.pdf
http://eprints.uthm.edu.my/11617/
https://doi.org/10.30880/ijscet.2023.14.02.011
_version_ 1811687208788164608
score 13.211869

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