• Journal of Korean Tunnelling and Underground Space Association
• /
• v.9 no.1
• /
• pp.75-82
• /
• 2007

As one of the main support systems, rock bolts play a crucial role in the reinforcement of tunnels. Numerical and experimental studies using a transmission method of ultrasonic guided waves are performed to evaluate the integrity of rock bolts encapsulated by grouting paste. Numerical simulations using "DISPERSE" are carried out for the selection of the optimal experimental setup, i.e. non-destructive testing (NDT) system of the rock bolt. Based on results of the numerical simulation, the calculated frequency range for NDT testing is between 20kHz and 70kHz with the first longitudinal L(1) mode. Laboratory transmission tests are performed by attaching the piezo electric sensor at the tip of the rock bolt before embedding. Both of analytical and experimental results show that the amplitude of signals as well as the wave velocity increases with increase in the defect ratio of grouting paste. The defect in grouting paste means that the space around the rock bolt is not fully filled with the grouting paste. Experimental results also show that the increase of the wave velocity is more sensitive to the defect ratio increase than that of the amplitude. This study demonstrates that the transmission technique of ultrasonic guided waves may be a valuable tool in the evaluation of the rock bolt integrity.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.21 no.1
• /
• pp.80-90
• /
• 2001

The aim of this study is to develop the methodology which enables to identify the mechanical properties of element such as stress intensity factor by using the AE parameters. Considering the multivariate and nonlinear properties of AE parameters such as ringdown count, rise time, energy, event duration and peak amplitude from fatigue cracks of machine element the principal component regression(PCR) and artificial neural network(ANN) models for the estimation of stress intensity factor were developed and validated. The AE parameters were found to be very significant to estimate the stress intensity factor. Since the statistical values including correlation coefficients, standard mr of calibration, standard error of prediction and bias were stable, the PCR and ANN models for stress intensity factor were very robust. The performance of ANN model for unknown data of stress intensity factor was better than that of PCR model.