A Linear Prediction and Support Vector Regression-Based Debonding Detection Method Using Step-Frequency Ground Penetrating Radar - Archive ouverte HAL Access content directly
Journal Articles IEEE Geoscience and Remote Sensing Letters Year : 2019

A Linear Prediction and Support Vector Regression-Based Debonding Detection Method Using Step-Frequency Ground Penetrating Radar

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.
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Dates and versions

hal-01907612 , version 1 (11-12-2018)

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Cédric Le Bastard, Jingjing Pan, Yide Wang, Meng Sun, Shreedhar Savant Todkar, et al.. A Linear Prediction and Support Vector Regression-Based Debonding Detection Method Using Step-Frequency Ground Penetrating Radar. IEEE Geoscience and Remote Sensing Letters, 2019, 16 (3), pp.367-371. ⟨10.1109/LGRS.2018.2873045⟩. ⟨hal-01907612⟩
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