This paper proposes a 3D ground penetrating radar (GPR) image-based underground cavity detection network (UcNet) for preventing sinkholes in complex urban roads. UcNet is developed based on convolutional neural network (CNN) incorporated with phase analysis of super-resolution (SR) GPR images. CNNs have been popularly used for automated GPR data classification, because expert-dependent data interpretation of massive GPR data obtained from urban roads is typically cumbersome and time consuming. However, the conventional CNNs often provide misclassification results due to similar GPR features automatically extracted from arbitrary underground objects such as cavities, manholes, gravels, subsoil backgrounds and so on. In particular, non-cavity features are often misclassified as real cavities, which degrades the CNNs’ performance and reliability. UcNet improves underground cavity detectability by generating SR GPR images of the cavities extracted from CNN and analyzing their phase information. The proposed UcNet is experimentally validated using in-situ GPR data collected from complex urban roads in Seoul, South Korea. The validation test results reveal that the underground cavity misclassification is remarkably decreased compared to the conventional CNN ones.

This study proposes a probability-based carbonation prediction approach for successful monitoring of deteriorating concrete structures. Over the last several decades, a number of researchers have studied the concrete carbonation prediction to estimate the long-term performance of carbonated concrete structures. Recently, probability-based durability analyses have been introduced to precisely estimate the carbonation of concrete structures. Since the carbonation of concrete structures, however, can be affected by material compositions as well as various environmental conditions, it is still a challenge to predict concrete carbonation in the field. In this study, the Fick’s first law and a Bayes’ theorem-based carbonation prediction approach is newly proposed using on-site data, which were obtained over 19 years. In particular, the effects of design parameters such as diffusion coefficient, concentration, absorption quantity of CO₂, and the degree of hydration have been thoroughly considered in this study. The proposed probabilistic approach has shown a reliable prediction of concrete carbonation and remaining service life.