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Journal of Sensors
Volume 2018, Article ID 3095427, 10 pages
Research Article

Inversion Model of GPR Imaging Characteristics of Point Objects and Fracture Detection of Heritage Building

1Institute of Water Resources and Hydro-Electric Engineering, Xi’an University of Technology, Xi’an 710048, China
2Institute of Geotechnical Engineering, Xi’an University of Technology, Xi’an 710048, China
3State Key Laboratory Base of Eco-Hydraulic Engineering in Arid Area, Xi’an University of Technology, Xi’an, Shaanxi 710048, China
4Geochemical Exploration Team of Shaanxi Geological Mineral Survey Group, Xi’an 710023, China

Correspondence should be addressed to Ning Li; nc.ude.tuax@ilgnin

Received 29 December 2017; Accepted 20 February 2018; Published 7 May 2018

Academic Editor: Hyung-Sup Jung

Copyright © 2018 Gao Lv et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


There are often many hidden structural defects in heritage buildings. As a convenient and effective nondestructive detecting method, ground-penetrating radar (GPR) has a technical advantage in detecting and protecting heritage buildings depending on the advanced image interpretation. The analytic relationship between buried depth and radius of point object and long and short axis of hyperbolic equation was established according to derivations of formulas. The image characteristics of hyperbolic curves with different depth and radius were studied by finite-difference time-domain method (FDTD). And then, inversion models of buried depth and radius of point object were established. The buried depth and radius can be accurately deduced by long and short axis of hyperbolic image. This result was applied in the detection of pedestal defects of the heritage building, and the depth and distribution range of hidden fracture can be accurately interpreted. It provides an effective and fast method to detect hidden defects in civil engineering.