Deep Learning–Based Framework for Bridge Deck Condition Assessment Using Ground Penetrating Radar

Abstract

Ground penetrating radar (GPR) is an advanced nondestructive testing (NDT) technique extensively used for assessing the structural integrity of concrete bridge decks. Despite its effectiveness, the manual interpretation required for GPR data hampers its broader application in bridge deck evaluations. This study presents a novel automated workflow that utilizes GPR scans to produce detailed deterioration maps of bridge decks, significantly enhancing the efficiency and precision of inspections. The automated process involves detecting rebar regions within the GPR data based on their unique electromagnetic signatures and employing hyperbola clustering to accurately localize each rebar by identifying the peaks of hyperbolic patterns. This crucial step aids in assessing the structural integrity of the bridge. The automated workflow culminates with the creation of deterioration maps that highlight potential structural weaknesses, enabling targeted maintenance and repairs. Field experiments conducted on a bridge deck confirmed the method’s effectiveness, showcasing its potential to expedite the bridge inspection process significantly. The development of this automated processing pipeline marks a substantial advancement in the application of GPR technology in civil engineering, paving the way for more reliable and streamlined bridge maintenance practices.