: Due to low annual average temperature, road tunnel lining in domestic cold region (Gangwon province) experiences durability problems. The financial and human damage due to cracks, breakout, exfoliation and water leakage increases every year. However, domestic research on effect of temperature on road tunnel lining damage is insufficient. Thus, this research has investigated 70 tunnels located in cold region (Gangwon-do) to analyze damage status. Furthermore, by contrasting damage on tunnels in relatively warm Gangneung area with those in relatively cold Hongcheon area, the effect of temperature on road tunnel lining damage was analyzed.

: Even if the various data analyzing methods were suggested to examine the measured slope behaviors, it is difficult to find methods or procedures for connecting the analyzed results of slope stability and measured slope data. This research suggests the analyzing methods combing the stability analysis and measured data based on progressive failure of slope. Slope failure analysis by time degradation were calculated by strength parameters composed of strength reduction coefficients, also which were compared to the measured data according to the variations of safety factor and displacement of slopes. The accumulated displacement curve were shown as 3rd degree polynomials by suggested procedures, which was the same as before researches. The reverse displacement velocity curves were shown as linear function for prediction of brittle slope failures, also they were shown as 3rd degree polynomials for ductile slope failures, which were the same as the suggested equation by Fukuzono (1985) and they were very similar behaviors to the in-situ failure cases.

Although shear wave velocity (V s) is an important design factor in seismic design, the measurement is not usually made in typical field investigation due to time and economic limitations. In the present study, an investigation was made to predict sand Vs based on the standard penetration test (SPT) results by using artificial neural network (ANN) model. A total of 650 dataset composed of SPT-N value (N 60), water content, fine content, specific gravity for input data and V s for output data was used to build and train the ANN model. The sensitivity analysis was then performed for the trained ANN to examine the effect of the input variables on the Vs. Also, the ANN model was compared with seven existing empirical models on the performance. The sensitivity analysis results revealed that the effect of the SPT-N value on V s is significantly greater compared to other input variables. Also, when compared with the empirical models using Nash-Sutcliffe Model Efficiency Coefficient (NSE) and Root Mean Square Error (RMSE), the ANN model was found to exhibit the highest prediction capability.

Slope stability is affected by duration of precipitation, probable rainfall intensity, unsaturated soil property, and soil strength. The recent analyses of slope stability tend to include unsaturated analysis based on infiltration properties of soil, while researches of unsaturated soil slope tend to include the analysis of deformation and stress distribution of soil over time. However, infiltration property of unsaturated soil slope depends not only on intensity or duration of precipitation, but also on relief and surface condition, which is not considered in status quo. This research uses hydrologic model parameters of soil in order to consider effects of inclination on filtration, and carries out analysis of unsaturated soil slope to confirm the effects according to slope inclination and surface condition. In conclusion, using slope stability analysis, the need to consider infiltration rate according to inclination and surface condition was confirmed even under the same precipitation conditions.

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