Detection of steel rebar corrosion in bridge piers using 1.6GHz ground penetrating radar

Abstract

Corrosion of steel reinforcement in concrete highway bridges due to carbonation and chloride attack is an ongoing and prevalent problem. If left undetected in time, late-stage corrosion can lead to section loss of steel rebars, an uneven distribution of internal stresses, and ultimately lead to surface cracks and spalling of the concrete. Nondestructive testing and evaluation (NDT/E) sensors like ground penetrating radar (GPR) are commonly used to detect subsurface objects and anomalies (e.g., concrete cracking, and rebars). In this paper, the feasibility of a 1.6GHz GPR device for detection of steel rebar corrosion is tested in both laboratory reinforced concrete (RC) specimens and in-situ RC structures. For this purpose, three RC specimens (12 x 12 x 5 in3 ) were cast with a No.5 steel rebar (5/8” diameter) at the center of each specimen. Two of the RC panels were corroded using the accelerated corrosion test (ACT) to achieve different levels of corrosion, while the third one was left un-corroded to serve as an intact baseline in our measurements. The RC specimens were kept in a temperature-controlled environment (73 ∼ 77◦F) for six years (2017-2023). In addition, a bridge column of a RC highway bridge underpass (Chelmsford, MA), which exhibits the signs of steel rebar corrosion, was selected for collecting in-situ GPR scans. A known intact RC bridge column was chosen and scanned to serve as the baseline for comparison. In both scenarios, B-scan images were developed from the lab RC specimens and the damaged bridge column. The changes in the reflection amplitudes of the hyperbolic-shaped rebar reflections in GPR B-scan images due to corrosion were studied in both the time and the frequency domains, and cross-validated with the results from previous research. From our experiments, it was found that a 1.6GHz GPR sensor can successfully detect and distinguish corrosion level in three RC specimens and one bridge column by the combined use of 1D and 2D analytic methods.