Remmania, Sid Ahmed (2023) Integrating seismic reflection method with artificial neural network processing for asphalt pavement thickness evaluation. Doctoral thesis, Universiti Tun Hussein Onn Malaysia.

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Abstract

Geophysical methods play a crucial role in the field of civil engineering, particularly in assessing pavement thickness. While conventional methods have been utilised in the past and are still in use today, there is a constant pursuit for better solutions. This study focuses on the seismic reflection method as a non-destructive quality assurance technique for measuring pavement thickness. preliminary experimentation is executed within a controlled laboratory milieu, encompassing a comprehensive evaluation and refinement process for diverse seismic sources and receivers. This endeavor aims to attain an optimized configuration conducive to high-frequency acquisition. An automated data processing method is developed using a feed-forward multi-layered neural network implemented in MATLAB. A user-friendly graphical user interface is also constructed to enhance and facilitate data processing and result calculations. The developed method is successfully tested on several asphalt pavement sites within the university campus, yielding an average measurement accuracy of 93%. Furthermore, the established method's findings and accuracy are validated by comparing them with both destructive and non-destructive conventional pavement thickness measurement methods. The outcome of this research is an innovative approach that overcomes the limitations of destructive conventional methods, providing a non-destructive solution for pavement thickness measurement. The study contributes to advancing the field of civil engineering by introducing a reliable and accurate technique for quality control and quality assurance in pavement construction and maintenance projects

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