학술논문

A Linear Prediction and Support Vector Regression-Based Debonding Detection Method Using Step-Frequency Ground Penetrating Radar
Document Type
Periodical
Source
IEEE Geoscience and Remote Sensing Letters IEEE Geosci. Remote Sensing Lett. Geoscience and Remote Sensing Letters, IEEE. 16(3):367-371 Mar, 2019
Subject
Geoscience
Power, Energy and Industry Applications
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Signal Processing and Analysis
Ground penetrating radar
Support vector machines
Telecommunications
Delay effects
Time-frequency analysis
Debondings
detection
linear prediction (LP)
pavements
step-frequency ground penetrating radar (SF-GPR)
support vector regression (SVR)
Language
ISSN
1545-598X
1558-0571
Abstract
In the field of civil engineering, ground penetrating radar (GPR) is a highly efficient nondestructive testing tool for sustainable management of pavement infrastructures. GPR allows to evaluate the structure of the roadway over large distances (with contactless configurations) and to detect significant subsurface defects. This letter presents a new method to detect thin debondings within pavement structures with the step-frequency GPR. The proposed method enables us to carry out the detection with only a small number of frequency samples and A-scans. It is based on the linear prediction and support vector regression theories. Two experimental results show its effectiveness.