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...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
uthm
2023
|
| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/10054/1/J16125_4f1e8defb35ea1e9460d8e31a1e82cac.pdf |
| _version_ | 1796870086327271424 |
|---|---|
| author | Sid Ahmed Remmani, Sid Ahmed Remmani Madun, Aziman Mohd Kamaruddin, Nurul Hidayah Oussama Laghouiter, Oussama Laghouiter |
| author_facet | Sid Ahmed Remmani, Sid Ahmed Remmani Madun, Aziman Mohd Kamaruddin, Nurul Hidayah Oussama Laghouiter, Oussama Laghouiter |
| author_sort | Sid Ahmed Remmani, Sid Ahmed Remmani |
| collection | UTHM |
| 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. |
| first_indexed | 2024-03-05T22:04:20Z |
| format | Article |
| id | uthm.eprints-10054 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-03-05T22:04:20Z |
| publishDate | 2023 |
| publisher | uthm |
| record_format | dspace |
| spelling | uthm.eprints-100542023-10-11T03:22:19Z http://eprints.uthm.edu.my/10054/ Artificial Neural Network in Seismic Reflection Method for Measuring Asphalt Pavement Thickness Sid Ahmed Remmani, Sid Ahmed Remmani Madun, Aziman Mohd Kamaruddin, Nurul Hidayah Oussama Laghouiter, Oussama Laghouiter 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/10054/1/J16125_4f1e8defb35ea1e9460d8e31a1e82cac.pdf Sid Ahmed Remmani, Sid Ahmed Remmani and Madun, Aziman and Mohd Kamaruddin, Nurul Hidayah and Oussama Laghouiter, Oussama Laghouiter (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 |
| spellingShingle | T Technology (General) Sid Ahmed Remmani, Sid Ahmed Remmani Madun, Aziman Mohd Kamaruddin, Nurul Hidayah Oussama Laghouiter, Oussama Laghouiter Artificial Neural Network in Seismic Reflection Method for Measuring Asphalt Pavement Thickness |
| title | 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_short | 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 |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/10054/1/J16125_4f1e8defb35ea1e9460d8e31a1e82cac.pdf |
| work_keys_str_mv | AT sidahmedremmanisidahmedremmani artificialneuralnetworkinseismicreflectionmethodformeasuringasphaltpavementthickness AT madunaziman artificialneuralnetworkinseismicreflectionmethodformeasuringasphaltpavementthickness AT mohdkamaruddinnurulhidayah artificialneuralnetworkinseismicreflectionmethodformeasuringasphaltpavementthickness AT oussamalaghouiteroussamalaghouiter artificialneuralnetworkinseismicreflectionmethodformeasuringasphaltpavementthickness |