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

• Main Authors: Sid Ahmed Remmani, Sid Ahmed Remmani, Madun, Aziman, Mohd Kamaruddin, Nurul Hidayah, Oussama Laghouiter, Oussama Laghouiter
• Format: Article
• Language: English
• Published: uthm 2023
• Subjects: T Technology (General)
• Online Access: http://eprints.uthm.edu.my/9606/1/J16125_4f1e8defb35ea1e9460d8e31a1e82cac.pdf; http://eprints.uthm.edu.my/9606/; https://doi.org/10.30880/ijscet.2023.14.02.011

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