The buffer is a critical barrier component in an engineered barrier system, and its purpose is to prevent potential radionuclides from leaking out from a damaged canister by filling the void in the repository. No experimental parameters exist that can describe the buffer expansion phenomenon when Kyeongju bentonite, which is a buffer candidate material available in Korea, is exposed to groundwater. As conventional experiments to determine these parameters are time consuming and complicated, simple swelling pressure tests, numerical modeling, and machine learning are used in this study to obtain the parameters required to establish a numerical model that can simulate swelling. Swelling tests conducted using Kyeongju bentonite are emulated using the COMSOL Multiphysics numerical analysis tool. Relationships between the swelling phenomenon and mechanical parameters are determined via an artificial neural network. Subsequently, by inputting the swelling tests results into the network, the values for the mechanical parameters of Kyeongju bentonite are obtained. Sensitivity analysis is performed to identify the influential parameters. Results of the numerical analysis based on the identified mechanical parameters are consistent with the experimental values.

Various models have been proposed to describe the swelling behavior of buffer in high level waster repository. One of the most notable models, the Barcelona Basic Model (BBM), is a mechanical model that simulates the behavior of unsaturated ground and is widely applied to soils that undergo large expansion due to water. Among the BBM parameters of Kyeongju bentonite, which is found in Korea, there are no experimental data for parameters that describe the unsaturated state. Such hydromechanical properties should be characterized through experimental programs. However, such experiments are highly complicated and require long periods of time to produce an unsaturated state through different methods according to the suction range. Although there are several studies in which geotechnical parameters were obtained through a back analysis instead of direct experiments, few studies have employed machine learning methods for the identification of geotechnical parameters. In this study, instead of direct experiments, the results of a relatively simple swelling pressure experiment was compared to the numerical analysis results to propose a method of determining some of BBM parameters. Influential factors were identified by a sensitivity analysis and the values of the factors were estimated using an artificial neural network and optimization method. The obtained parameters were applied to the numerical model to estimate the swelling pressure growth, which was subsequently compared to the experimental value. As a result, it was found that there was no significant difference between the two swelling values.

해외 자회사로부터 본국에 있는 모기업으로의 본국 송금은 다국적 기업에게는 글로벌전략을 추진 하는 데 있어서 중요한 문제이고 국가적으로는 해외직접투자의 효과에도 영향을 미칠 수 있는 문제 이다. 본 연구는 해외 자회사와 본국 모기업의 관계가 대리인과 주인 관계라는 것에 주목하여 대리 인인 해외 자회사의 관리자구성이 본국 송금에 어떠한 영향을 미치는지를 조사하였다. 해외에 진출 한 637개 한국다국적기업 자회사의 본국 송금과 관리자구성을 분석한 결과 본 연구는 자회사의 관리자급에서 현지인 관리자의 비율이 높을수록 본금 송금이 줄어든다는 것을 발견하였다. 또한, 이러한 현지인 관리자의 비율과 본국 송금의 관계는 자회사의 소유 형태와 자회사의 나이에 따라 달라진다는 것을 발견하였다. 기존의 연구들이 국가의 세금과 내부적 통제에 초점을 맞춰 본국 송금을 설명한 것에 비해 본 연구는 자회사의 인력구성이 본국 송금에 영향을 미칠 수도 있다는 것을 보여주어 본국 송금 연구에 새로운 방향을 제시할 뿐만 아니라 자회사가 모기업의 지시만을 따르는 수동적인 조직이 아니라는 최근의 시각에도 시사점을 주고 있다.