Automated Rebar Recognition and Evaluation Through Hyperbolic Fitting and Signal Analysis of Ground Penetrating Radar GPR Data

Abstract

Precise rebar spacing and depth determination are essential for evaluating the integrity and load-bearing capacity of reinforced and prestressed concrete structures. Traditional manual interpretation of ground penetrating radar (GPR) data can be timeconsuming and inconsistent, particularly when dealing with complex radargram signals. This study presents an automated rebar-picking method that employs hyperbolic fitting and positive peak signal analysis to enhance the accuracy of rebar detection. The developed algorithm processes GPR data by identifying hyperbolic reflections and extracting amplitude values from the peaks of these hyperbolas. It then compares these values to theoretical hyperbolas. A user-defined threshold is incorporated to enhance reliability, enabling users to adjust acceptable deviations. The algorithm constrains the difference between the detected hyperbolic amplitude patterns and the theoretical hyperbolas to 80 percent, accommodating a certain level of noise or signal variations. Once a rebar is identified, the algorithm automatically determines its location. Controlled experiments were conducted on a prestressed concrete beam to validate the method, during which GPR data was collected and analyzed. The results show that the algorithm achieves over 90 percent accuracy, successfully detecting and localizing most rebars. Additionally, field scans of prestressed concrete box beams confirm the efficacy of this method in determining rebar spacing and depth, even in challenging conditions involving signal noise or overlapping reflections. The findings demonstrate that this proposed method significantly enhances GPR non-destructive evaluation techniques by improving efficiency, precision, and repeatability while reducing the need for manual interpretation. Furthermore, the study highlights the potential of automated hyperbola based GPR analysis in structural health monitoring, offering a reliable and scalable solution for rebar detection in reinforced and prestressed concrete bridge infrastructure.