Open AccessAutomatic Evaluation of Pavement Thickness in GPR Data with Artificial Neural Networks
Author(s) -
Y.A. Sukhobok,
Leonid Verkhovtsev,
Yu. V. Ponomarchuk
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/272/2/022202
Subject(s) - ground penetrating radar , artificial neural network , computer science , artificial intelligence , multilayer perceptron , data processing , radar , machine learning , pattern recognition (psychology) , telecommunications , operating system
The ground penetrating radar (GPR) is one of the most frequently recommended non-destructive methods for the pavement thickness measurement. Due to the rapid growth of GPR data in the recent years, the development of automatic data processing techniques is required. In this paper we propose to use one type of artificial neural network, the multilayer perceptron (MLP), for automatic selection of the pavement boundaries. The experimental results indicate that machine learning techniques can be used for robust road structure evaluation.