• Journal of the Korean Geotechnical Society
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• v.38 no.12
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• pp.7-17
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• 2022

The ratios of water and controlled low-strength materials (CLSM) were selected as 1:0.4, 1:0.6, 1:0.8, 1:1.0, and 1:1.2 to minimize the construction and long-term decrease in uniaxial compressive strength due to dry shrinkage through the combination of water, CLSM, and expansion agent. Approximately 2% and 5% of the expansion agent were added for each blending condition. As a result, it was found that the compressive strength decreased and the expandability increased as the specific gravity of the water increased. In addition, it was confirmed that the compressive strength increased rapidly up to 15 days of age compared to the CLSM used in the field. However, the compressive strength decreased compared to the 15 days of the age as of the 28 days of the age. It showed engineering characteristics similar to CLSM generally used in the field. Therefore, the water and the CLSM were mixed at a ratio of 1:0.8, and the field test was performed by adding 5% of an expansion agent. As a result, 28 days after age, the cavity waveform was observed using the handy GPR exploration system, and it was found that cavity waveform was relaxed or disappeared.

• Journal of the Korean Geotechnical Society
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• v.39 no.7
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• pp.31-37
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• 2023

Since tunnels are built underground, it is impossible to check visually the location and degree of deterioration of steel ribs. Therefore, in tunnel maintenance, GPR images are generally used to detect steel ribs. While research on GPR image analysis employing artificial neural networks has primarily focused on detecting underground pipes and road damage, there have been limited applications for analyzing tunnel GPR data, specifically for steel rib detection, both internationally and domestically. In this study, a one-step object detection algorithm called YOLO, based on a convolutional neural network, was utilized to automate the localization of steel ribs using GPR data. The performance of the algorithm is then analyzed. Two datasets were employed for the analysis. A dataset comprising 512 original images and another dataset consisting of 2,048 augmented images. The omission rate, which represents the ratio of undetected steel ribs to the total number of steel ribs, was 0.38% for the model using the augmented data, whereas the omission rate for the model using only the original data was 7.18%. Thus, from an automation standpoint, it is more practical to employ an augmented dataset.