• Journal of the Korean GEO-environmental Society
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• v.14 no.7
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• pp.51-56
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• 2013

Recently, a top record of hourly-based rainfall has been changed annually and flood damages of road have increased. To solve this problem, pavements for drainage were developed and practically constructed but there was no considerations on sub-base. In this research, we proposed standard for distribution of particle size of sub-base to consider strength characteristic and drainage property. We focused to compare coefficients strength and permeability by laboratory tests. Prior to tests, 4 samples were selected under the consideration on the international or domestic design guideline. In the tests, strength characteristics were compared with resilient modulus. Also, permeability characteristics were compared with coefficient of upward and downward permeability. Resilient modulus was determined with MR test using cyclic triaxial testing system. Two permeability tests were carried out. One is variable head permeability test for downward drainage and the other is Rowe Cell test for upward drainage. In the case of Rowe Cell test, middle-sized sampler with 150mm diameter was used for this study. Consequentially, we tried to find the optimum distribution of particle size to satisfy both of strength and permeability characteristics for sub-base.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1131-1141
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• 2021

In this study, the artificial neural network model is applied for real-time dam inflow prediction and then evaluated for the prediction lead times (1, 3, 6 hr) in dam basins in Korea. For the training and testing the model, hourly precipitation and inflow are used as input data according to average annual inflow. The results show that the model performance for up to 6 hour is acceptable because the NSE is 0.57 to 0.79 or higher. Totally, the predictive performance of the model in dry seasons is weaker than the performance in wet seasons, and this difference in performance increases in the larger basin. For the 6 hour prediction lead time, the model performance changes as the sequence length increases. These changes are significant for the dry season with increasing sequence length compared to the wet season. Also, with increasing the sequence length, the prediction performance of the model improved during the dry season. Comparison of observed and predicted hydrographs for flood events showed that although the shape of the prediction hydrograph is similar to the observed hydrograph, the peak flow tends to be underestimated and the peak time is delayed depending on the prediction lead time.