아스팔트 혼합물의 동탄성계수는 시험온도 하중주파수의 조합에 따라 각각의 동탄성계수값을 평가한다. 실험에서 얻어진 각각의 동탄성계수를 하중시간과 온도중첩원리를 이용하여 마스터곡선(Master Curve)을 결정한다. 본 연구의 주목적은 마스터곡선을 만들기 위해 필요한 3개의 다른 전이함수(Shift Factor)에 -즉, Arrhenius, 2002 AASHTO Guide, Experimental method- 따른 마스터곡선의 변화정도를 평가하는 것이다. 평가를 위해 사용된 골재는 화강암이고, 아스팔트(AP-3 및 AP-5)를 이용하여 표층용 및 기층용 아스팔트 혼합물의 동탄성계수를 평가하였다. 배합설계는 Superpave Level 1 기준을 준용하였고, 다짐은 선회다짐기를 이용하였다. UTM시험기를 이용한 동탄성계수 시험은 5개의 온도(-10, 5, 20, 40, 55도) 및 5개의 하중주파수(0.05, 0.1, 1, 10, 25 Hz)를 이용하였고, 각각의 아스팔트 혼합물의 위상각 및 동탄성계수를 평가하였다. 측정된 값을 이용하여 Sigmoidal Function방정식을 만족하는 입력변수를 결정하기 위해 전이함수 및 활성에너지 (activation energy)를 결정하였다.

In this paper, time series of soil moisture were measured for a steep forest hillslope to model and understand distinct hydrological behaviours along two different transects. The transfer function analysis was presented to characterize temporal response patterns of soil moisture for rainfall events. The rainfall is a main driver of soil moisture variation, and its stochastic characteristic was properly treated prior to the transfer function delineation between rainfall and soil moisture measurements. Using field measurements for two transects during the rainy season in 2007 obtained from the Bumrunsa hillslope located in the Sulmachun watershed, a systematic transfer functional modeling was performed to configure the relationships between rainfall and soil moisture responses. The analysis indicated the spatial variation pattern of hillslope hydrological processes, which can be explained by the relative contribution of vertical, lateral and return flows and the impact of transect topography.

The transfer function was introduced to establish the prediction method for the DO concentration at the intaking point of Kongju Water Works System. In the most cases we analyze a single time series without explicitly using information contained in the related time series. In many forecasting situations, other events will systematically influence the series to be forecasted(the dependent variables), and therefore, there is need to go beyond a univariate forecasting model. Thus, we must build a forecasting model that incorporates more than one time series and introduces explicitly the dynamic characteristics of the system. Such a model is called a multiple time series model or transfer function model. The purpose of this study is to develop the stochastic stream water quality model for the intaking station of Kongju city waterworks in Keum river system. The performance of the multiplicative ARIMA model and the transfer function noise model were examined through comparisons between the historical and generated monthly dissolved oxygen series. The result reveal that the transfer function noise model lead to the improved accuracy.