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

作者：Le Bastard, Cedric; Pan, Jingjing^*; Wang, Yide; Sun, Meng; Todkar, Shreedhar Savant; Baltazart, Vincent; Pinel, Nicolas; Ihamouten, Amine; Derobert, Xavier; Bourlier, Christophe

来源：IEEE Geoscience and Remote Sensing Letters, 2019, 16(3): 367-371.

DOI：10.1109/LGRS.2018.2873045

• Debondings
• detection
• linear prediction (LP)
• pavements
• step-frequency ground penetrating radar (SF-GPR)
• support vector regression (SVR)

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.

• 出版日期2019-3
• 单位上海海事大学; 中国人民解放军信息工程大学