PURPOSES: The intensiveness of highway management has increased owing to the growth in the number of vehicles and the rapid climate change. The disadvantages produced by these factors can affect management time and cost. Serious traffic accidents and traffic jam may be experienced when snow fall accumulates on highway surfaces and the friction between tires and pavements is lower than that in the general state, in a non-management condition. Such conditions need intensive management. In this regard, one of the spread methods used for the melting material is pre-wetted salt (PWS), which is the frequently used method in South Korea. In the PWS method, the solid material with CaCl2 is mixed with water in 30% concentration and then finally mixed with NaCl before application to pavements. The chloride-type melting material not only is cheaper, but also has a high melting property than the others. It can shorten the pavement or structure life by deterioration and corrosion. This melting material can affect the flora near the highways; hence, an eco-friendly de-icing agent must be utilized considering the environmental effect. METHODS : The Kalman filter algorithm (KFA) was utilized herein to develop optimization models using the performed test data. The KFA, which was developed from recursive filter algorithms, such as the low- and high-pass filters, applies a weighting filter to the Kalman filter. The algorithm has the property of utilizing the filter and updated estimations. In this regard, melting tests were performed for the real applicative utilization of de-icing agents. The KFA was also applied to reduce the error rates and optimize the relationships between the test data and the predictions. RESULTS: Comparing the measurements performed, the error was reduced by 1.69 g when the KFA was applied. Moreover, the error can be optimized to approximately 91.4% compared to the test errors. The prediction data had over 85% tendency in the test measurement, showing that the KFA application can reduce the error and increase the tendency. By comparison, the agent with CaCl2 showed the best ice melting performance within 10 min without surface temperature. However, the PWS with a 25% concentration indicated the best water melting performance from start to end of the test time, implying that this is a powerful agent in terms of performance. CONCLUSIONS : The melting test is an artificial test method; therefore, it can generate a huge error from the test. The error and the tendency can be controlled by tracking the measurement error and the white noise matrix using the KFA. A further research will be performed to track the measurement error and the white noise matrix. Other optimization methods will also be applied to reduce the experimental error.

PURPOSES: This study is to predict the Sound Pressure Level(SPL) obtained from the Noble Close ProXimity(NCPX) method by using the Extended Kalman Filter Algorithm employing the taylor series and Linear Regression Analysis based on the least square method. The objective of utilizing EKF Algorithm is to consider stochastically the effect of error because the Regression analysis is not the method for the statical approach. METHODS: For measuring the friction noise between the surface and vehicle’s tire, NCPX method was used. With NCPX method, SPL can be obtained using the frequency analysis such as Discrete Fourier Transform(DFT), Fast Fourier Transform(FFT) and Constant Percentage Bandwidth(CPB) Analysis. In this research, CPB analysis was only conducted for deriving A-weighted SPL from the sound power level in terms of frequencies. EKF Algorithm and Regression analysis were performed for estimating the SPL regarding the vehicle velocities. RESULTS : The study has shown that the results related to the coefficient of determination and RMSE from EKF Algorithm have been improved by comparing to Regression analysis. CONCLUSIONS : The more the vehicle is fast, the more the SPL must be high. But in the results of EKF Algorithm, SPLs are irregular. The reason of that is the EKF algorithm can be reflected by the error covariance from the measurements.

Recently, as the awareness of safety has become more important, studies on damage assessment techniques for building structures have been actively conducted. The damage of the building structure is caused by the decrease of the stiffness which is inherent dynamic characteristic of the structural system, and the decrease of stiffness acts as a direct variable connected to the collapse of the structure. there have been developed techniques for estimating the inherent dynamics of a structure to identify and evaluate damage to the structure. In this study, we estimate the layer mass due to the modeling error through the optimization algorithm, Genetic Algorithm, and use the optimization algorithm GA to optimize the error covariance matrix, system noise and measured noise covariance matrix We propose an optimal state estimation algorithm. The objective function of the GA algorithm is obtained by the residual which is the difference between the measured values obtained from the EKF calculation and the values obtained from the system model. We verified the feasibility of the algorithm through a 4-DOF system.

본 연구에서는 자료동화기법의 하나인 확장 칼만 필터를 이용하여 유량자료의 실시간 품질향상을 수행하였다. 확장 칼만 필터의 상태-공간모형은 강우-유출모형과 관측유량자료를 이용하여 구성하였다. 본 연구에서는 실시간 품질향상 목적을 댐 유입량의 비정상적 고변동성 억제 및 관측유량의 결‧오측 보완으로 구분하였으며, 각각의 경우에 적절한 확장 칼만 필터 모형을 제시하였다. 이들 모형의 차이는 칼만이득 계산에 필요한 공분산 함수의 추정에 변동성만을 고려하는냐 또는 편의까지를 포함하느냐로 나타난다. 본 연구는 충주댐 유역을 대상으로 적용하였으며, 그 결과 제시된 모형들이 댐 유입량자료나 결‧오측이 포함된 유량자료의 실시간 품질향상에 효과적으로 작동함을 확인하였다.