The primary objectives of this study are to investigate the interference of ground penetrating radar (GPR) signals with air-cavities behind concrete linings subjected to three experimental parameters (GPR antenna frequency, thickness of concrete lining, and thickness of air-cavities), and their effects on the evaluation of important engineering parameters (thickness of concrete, and length and thickness of air-cavities) for tunnel structure inspection using GPR. For these purposes, first, a concrete model specimen is fabricated in the laboratory, which simulates air-cavity defects with various thicknesses (0 mm, 30 mm, 100 mm, 200 mm, and 300 mm) behind concrete with four different thicknesses (100 mm, 200 mm, 300 mm, and 400 mm) in tunnel structures. Second, a series of GPR survey is performed on the fabricated concrete model specimen using three ground-coupled GPR antennas with nominal center frequencies of 400 MHz, 900 MHz, and 1600 MHz. Third, a practical signal interpretation method is proposed to evaluate air-cavities in concrete tunnel linings subjected to the three experimental parameters. It is demonstrated that the cross-correlation analysis is effective for identifying air-cavities behind concrete, and for characterizing the length of the air-cavities. In addition, the thicknesses of air-cavities are estimated by a new approach in this study, measuring the apparent time delay of partially overlapped electromagnetic wave reflections within air-cavities. The results in this study provide a fundamental basis to improve GPR signal interpretation techniques for the evaluation of air-cavities behind concrete tunnel linings.

Crack inspection is important to monitor the structural health of pavement structures and make the repair process easier. Currently, pavement crack inspection is conducted manually, which is inefficient and costly at the same time. To solve the problem, this work has developed a robotic system for automated data collection and analysis in real-time. The robotic system navigates the pavement and collects visual images from the surface. A deep-learning-based semantic segmentation framework named RCDNet was proposed. The RCDNet was implemented on the onboard computer of the robot to identify cracks from the visual images. The encoder-decoder architecture was utilized as the base framework of the proposed RCDNet. The RCDNet comprises a dual-channel encoder and a decoder module. The encoder and decoder parts contain a context-embedded channel attention (CECA) module and a global attention module (GAM), respectively. Simulation results show that the deep learning model obtained 96.29% accuracy for predicting the images. The proposed robotic system was tested in both indoor and outdoor environments. The robot was observed to complete the inspection of a 3 m × 2 m grid within 10 min and a 2.5 m × 1 m grid within 6 min. This outcome shows that the proposed robotic method can drastically reduce the time of manual inspection. Furthermore, a severity map was generated using the visual image results. This map highlights areas that require greater attention for repair in the test grid.

This study aimed to evaluate 2D magnetic flux leakage (MFL) signals (B_x, B_y) in D19-size reinforcing steel with several defect conditions. The magnetic flux leakage data were collected from the defected and new specimens using an economically designed test setup incorporating permanent magnets. A two-dimensional finite element model was numerically simulated using COMSOL Multiphysics to validate the experimental tests. Based on the MFL signals (B_x, B_y), this study also intended to improve the ability to analyze defect features such as width, depth, and area. Both the numerical and experimental results indicated a high cross-correlation with a median coefficient of 0.920 and a mean coefficient of 0.860. Using signal information to evaluate defect width, the x-component (B_x) bandwidth was found to increase with increasing defect width and the y-component (B_y) amplitude rise with increasing depth. In this two-dimensional MFL signal study, both parameters of the two-dimensional defects (width and depth) affected each other and could not be evaluated individually. The defect area was estimated from the overall variation in the signal amplitude of the magnetic flux leakage signals with the x-component (B_x). The defect areas showed a higher regression coefficient (R² = 0.9079) for the x-component (B_x) amplitude from the 3-axis sensor signal. It was determined that defect features are positively correlated with sensor signals.

Crack inspection is important to monitor the structural health of pavement structures and to facilitate an easier rehabilitation process. Currently, pavement crack inspection is conducted manually, which is inefficient and costly at the same time. For solving the problem, this work has developed a robotic system for automated data collection and analysis in real-time. The robotic system navigates on the pavement and collects visual images from the surface. A deep learning-based semantic segmentation framework named RCDNet was proposed and implemented on the onboard computer of the robot to identify cracks from the visual images. Simulation results show that the deep learning model obtained 96.29% accuracy for predicting the images. The proposed robotic system was tested on both indoor and outdoor environments and was observed that it can complete inspecting a 3m × 2m grid within 10 minutes and a 2.5m × 1m grid within 6 minutes. This outcome shows that the proposed robotic method can drastically reduce the time of manual inspection. Furthermore, a severity map based on the results from visual images was also generated to provide an idea of which locations should be paid more attention to repair in a test grid.