A Symmetry−Informed Learning Framework for Robust Detection of Pavement Cracks in GPR Data Across Antenna Orientations and Material Conditions

Ground penetrating radar (GPR) is widely used for non−destructive evaluation of pavement structures, yet the automatic detection of internal cracks remains challenging due to variations in crack geometry, infilling materials, and antenna configurations that significantly alter signal responses. Most existing machine learning approaches focus on improving detection accuracy but pay limited attention to the inherent symmetries and invariances present in GPR data. This study proposes a symmetry−informed learning framework for robust pavement crack detection across different antenna orientations and material conditions. Laboratory concrete slabs containing cracks with varying widths (2–30 mm) and depths (10–110 mm) were constructed and tested under five representative crack states: air−filled, dry sand, fresh water, saturated sand, and bitumen−filled. GPR data were collected using a 2.3 GHz system under perpendicular and parallel broadside antenna orientations to capture rotational variability. A deep learning model was developed with symmetry−aware training strategies that exploit rotational consistency and material−invariant feature learning. Comparative experiments were conducted to evaluate detection performance and cross−condition generalization. Results demonstrate that incorporating symmetry improves model robustness and generalization across unseen orientations and filling conditions. The proposed framework highlights the importance of symmetry−informed learning for reliable AI−driven GPR inspection of pavement infrastructure.