Time-delay estimation (TDE) using ground penetrating radar (GPR) is of great importance in roadway surveys. The conventional GPR methods apply uniform sampling strategy for TDE, which require numerous frequency sampling points, leading to lengthy data acquisition time and large data storage, especially for ultra-wideband (UWB) radar. Moreover, detecting the overlapped backscattered echoes from thin layer of roadways remains a challenge in TDE, due to the limited resolution of GPR and the characteristics of GPR signals. To address these issues, we derive a co-prime sampling strategy based TDE for thin layers in roadway survey by exploiting off-grid sparse Bayesian learning (OGSBL), referred as co-prime-OGSBL. In our scheme, the sampling rate of GPR signals with co-prime sampling strategy is greatly reduced compared with the uniform sampling, which therefore reduces the data acquisition burden and computational complexity. The estimation performance of time-delays and thickness is also enhanced with OGSBL by utilizing radar pulse, co-prime sampling, and non-circularity of GPR signals. Both simulation and experimental results demonstrate the efficiency and accuracy of the proposed method in the estimation of time-delays and thickness.