Abstract
This paper present the technique to compare the depth of pipe measured with Ground Penetrating Radar (GPR) to the actual value determine during pipe installation. In this study six different subsurface materials were used to bury the pipes meant to evaluate the impact of these materials toward the depths obtained from reflected wave. Estimation of depth for buried object required an understanding of ground-penetrating radar (GPR) functioning that include the concept of pulse, wavelength, strength of the energy (amplitude), travelling time as well as reflection of electromagnetic wave. However, there is a considerable lack of the study on the accuracy depth using GPR. This study is attempted to address the knowledge deficit by buring pipe at specific depth and measure it with GPR to estimate depth. Despite increasing application, little consideration has been given to the accuracy of the depth. This study examines the accuracy of the depth by comparing the actual depth with what measured by GPR. Corresponding GPR and manual measurement of the depth were carried out at the site specially prepared for this study. There are two metals and two plastic pipes running through the material at the depth 0.5m and 1m respectively where a few pieces of plywood are used as the separator between them GPR utilises propagating electromagnetic (EM) that travel through subsurface material from the transmitter in form of pulse. When the energy faces a different dielectric constant on the journey it will reflect back up to the receiver at a specific time window. For this study the 250 MHz frequency is used to capture image of different six different samples of soils and one of it is a ready mix concrete. The 250 MHz GPR data set gives a vertical resolution (0.125 m) with an approximate penetration depth of about 3 m.
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CITATION STYLE
Ghazali, R., Rasam, A. R. A., Razali, M. H., Razali, R., & Latif, Z. A. (2019). Estimation of different subsurface materials depth using ground penetrating radar. International Journal of Advanced Trends in Computer Science and Engineering, 8(1.3 S1), 363–370. https://doi.org/10.30534/ijatcse/2019/6581.32019
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