Autofocusing Techniques for GPR Data from RC Bridge Decks

Abstract

According to the Federal Highway Administration, more than a quarter of the bridge decks in the United States are obsolete or deficient and most of them are made of reinforced concrete (RC). Precise decisions for bridge maintenance are in high demand due to the limited budget. Among various nondestructive evaluation techniques, ground penetrating radar (GPR) has gained its popularity for reasons such as high speed and fine resolution. Migration is an important intermediate process for GPR data analysis and its accuracy depends on the velocity or dielectric permittivity estimation of the subsurface. Velocity analysis for commonly adopted bistatic GPR systems is based on an iterative trial-and-error process, which involves a human decision process to obtain the properly migrated data. Besides, for heavily polluted data, it can be hardly possible to perform the visual inspection. Autofocusing techniques are rarely discussed in the field of GPR. In this paper, potential autofocusing metrics are nominated and evaluated by both simulation and experimental data. The effects of noise, aperture width, and crossing rebar signals on the metric performance are investigated and the results demonstrate that the higher-order metrics are the most robust and sensitive autofocusing metrics for the migration of GPR data from RC bridge decks.

Subsurface Detection of Shallow Targets by Undersampled Multifrequency Data and a Non-Cooperative Source

“…which can be satisfied when N > 5. How must conditions in Equations (16) and (18) be interpreted and what do they guarantee about artifacts in the reconstruction?…”

“…Electromagnetic waves at the microwave spectrum allow to get high resolution. That is why over the years a great deal of research has been carried out to tackle the many challenges of subsurface imaging, both in terms of the system and the imaging algorithms [15][16][17][18][19][20][21].…”

Foreword to the Special Issue on Pattern Recognition in Remote Sensing

“…In [25], a sequential detection algorithm using simulated annealing (SA) to detect patterns in seismic data is proposed, and the proposed sequential detection is better than that of synchronous detection in detecting a large number of patterns. In [26], GPR is used for quality assessment of bridge decks due to its high speed and fine resolution. In this paper, potential autofocusing metrics are nominated and evaluated by both simulation and experimental data, and the results demonstrate that the higher-order metrics are the most robust and sensitive autofocusing metrics for the migration of GPR data from RC bridge decks.…”

Diagnosing Emerging Infectious Diseases of Trees Using Ground Penetrating Radar

“…For the current problem, the velocity structure of the trunk is not trivially available. Consequently, the velocity of the trunk is assumed to be homogeneous and it is estimated using auto-focusing criteria [56], [57], [58].…”