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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.

No abstract

Segmental posttensioned bridges are major structures that carry significant traffic. Recent investigations of these bridges identified voids in their ducts. The exposed strands at these void locations can undergo corrosion. The corrosion of strands may lead to the failure of tendons. As such, an effective inspection process for identifying these voids is needed. From a literature review, several nondestructive testing methods are compared for applicability to void inspection in external tendons. The impact echo, ultrasonic pulse velocity, and sounding inspection methods are then selected and assessed for further preliminary testing. The sounding inspection method is further assessed for its effectiveness in identifying voids in a full-scale, external tendon system. The results indicate that sounding inspection slightly underestimates the size of the voids. However, the inspected size and locations of voids have a close correlation with actual voids in ducts. Thus, sounding inspection can be an effective tool for identifying voids because of its easy application in the field.

Recent inspections of segmental posttensioned (PT) bridges reported the presence of large voids in ducts that contain PT strands. Corrosion of the strands was observed in these voids, which are believed to be a result of poor grouting. Corrosion of the PT strands can result in structural failure. Controversy exists on how best to protect PT tendons from corrosion; filling these voids with grout is one option. An optimized grouting procedure for repairing these voids is needed to protect the strands from corrosive environments. This research investigates three grouting methods for efficiently repairing the voids in PT duct systems: vacuum grouting (VG), pressure grouting (PG), and pressure–vacuum grouting (PVG). Each method is evaluated for filling capability, filling performance, and economic feasibility. Also, three prepackaged grouts for repair are assessed in this research to propose the most suitable material for repairing voids in the PT ducts. The results indicate that the PG and PVG methods are more constructible and likely to be more economical than the VG method. However, the PVG and VG methods seem to be more effective than the PG method in filling the voids. As a result of these tests, the PVG method is recommended for filling voids in tendons. The results also show that Classes C-1 and C-2 grouts have better filling capability than Class C-3 grout.