Automated concrete damage detection using GPR: A universal solver based on AI-assisted relative permittivity estimation

Abstract

Ground-penetrating radar (GPR) has recently been adopted for detecting damage in concrete based on relative permittivity variations. However, its practical applicability remains limited due to the need for pre-known parameters such as wave velocity or rebar depth. This paper proposes an automated algorithm that back-calculates relative permittivity from electromagnetic responses without requiring any prior structural information. Leveraging AI-assisted analysis, a YOLO-based model detects rebar-induced reflections and estimates permittivity. The algorithm was validated in three phases: (1) testing on a mock-up slab with artificial voids; (2) application to a deteriorated bridge deck, benchmarked against impact-echo results; and (3) deployment on an in-service reinforced concrete bridge. Results demonstrate high detection accuracy and significantly enhanced efficiency, enabling robust performance across varying GPR datasets. The proposed algorithm also functions as a universal solver compatible with diverse structures and equipment, advancing the automation of GPR interpretation and its broader application in civil infrastructure assessment.