Dynamic responses of nuclear power plant structure subjected to earthquake loads should be carefully investigated for safety. Because nuclear power plant structure are usually constructed by material of reinforced concrete, the aging deterioration of R.C. have no small effect on structural behavior of nuclear power plant structure. Therefore, aging deterioration of R.C. nuclear power plant structure should be considered for exact prediction of seismic responses of the structure. In this study, a machine learning model for seismic response prediction of nuclear power plant structure was developed by considering aging deterioration. The OPR-1000 was selected as an example structure for numerical simulation. The OPR-1000 was originally designated as the Korean Standard Nuclear Power Plant (KSNP), and was re-designated as the OPR-1000 in 2005 for foreign sales. 500 artificial ground motions were generated based on site characteristics of Korea. Elastic modulus, damping ratio, poisson’s ratio and density were selected to consider material property variation due to aging deterioration. Six machine learning algorithms such as, Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN), eXtreme Gradient Boosting (XGBoost), were used t o construct seispic response prediction model. 13 intensity measures and 4 material properties were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks present good prediction performance considering aging deterioration.

For the OPR1000, a standard power plant in Korea, an analytical model of the containment building considering voids and deterioration was built with multilayer shell elements. Voids were placed in the vulnerable parts of the analysis model, and the deterioration effects of concrete and rebar were reflected in the material model. To check the impact of voids and deterioration on the seismic performance of the containment building, iterative push-over analysis was performed on four cases of the analytical model with and without voids and deterioration. It was found that the effect of voids with a volume ratio of 0.6% on the seismic performance of the containment building was insignificant. The effect of strength reduction and cross-sectional area loss of reinforcement due to deterioration and the impact of strength increase of concrete due to long-term hardening offset each other, resulting in a slight increase in the lateral resistance of the containment building. To determine the limit state that adequately represents the seismic performance of the containment building considering voids and deterioration, the Ogaki shear strength equation, ASCE 43-05 low shear wall allowable lateral displacement ratio, and JEAC 4601 shear strain limit were compared and examined with the analytically derived failure point (ultimate point) in this study.

Pipe Deterioration Prediction (PDP) and Pipe Failure Risk Prediction (PFRP) models were developed in an attempt to predict the deterioration and failure risk in water mains using fuzzy technique and the markov process. These two models were used to determine the priority in repair and replacement, by predicting the deterioration degree, deterioration rate, failure possibility and remaining life in a study sample comprising 32 water mains. From an analysis approach based on conservative risk with a medium policy risk, the remaining life for 30 of the 32 water mains was less than 5 years for 2 mains (7%), 5-10 years for 8 (27%), 10-15 years for 7 (23%), 15-20 years for 5 (17%), 20-25 years for 5 (17%), and 25 years or more for 2 (7%).

본 연구는 공급자(supplier), 중간공급자(distributor) 그리고 고객(customer)으로 구성된 2 단계 공급사슬에서 퇴화성 제품(deteriorating products)에 대한 중간공급자의 재고모형을 분석하였다. 문제 분석을 위해 공급자는 중간공급자의 수요 증대를 목적으로 중간공급자의 주문 크기에 따라 차별적으로 외상 기간을 허용하고, 최종 고객의 수요는 중간공급자의 재고 수준에 따라 선형적(linearly)으로 증가한다는 가정 하에 모형을 분석하였다. 중간공급자의 이익을 최대화하는 경제적 주문량 결정 방법을 제시하였고, 예제를 통하여 그 해법의 타당성을 보였으며, 민감도 분석을 통하여 퇴화율이 재고정책에 미치는 영향을 분석하였다.

본 연구(II)는 연구(I)에서 제안한 상수관로의 노후도 예측에 근거한 최적 개량 모형을 A시를 대상으로 이를 적용하였다. 노후도 예측 모형은 굴착 및 실험이 필요한 14개 항목과 굴착 및 실험이 필요하지 않은 9개 항목을 구분하여 각각 관의 노후도 등급을 산정하였다. 노후도 예측 모형 적용 결과 항목개수에 따른 등급의 차는 l~2% 이내로 굴착 및 실험을 하지 않고도 노후도 예측이 가능한 것으로 나타났다. 최적 개량 모형은 노후도 항목별 최대 잔존수명

본 연구에서는 상수관의 개량사업을 보다 효율적으로 실시 할 수 있는 방법으로 국내 실정에 적합한 상수관로의 노후도 조사방법을 이용하여 매설된 관의 노후도를 예측 근거한 최적 개량 모형을 제시하였다. 노후도 예측 모형은 확률론적 신경망 이론을 바탕으로 관별 노후도 정도를 5개 등급으로 구분하며 산정된 관별 노후도 등급 및 관경을 바탕으로 최대 잔존수명을 산정하였다. 최적 개량 모형은 관의 유지보수, 갱생, 교체의 시기 및 비용을 산정하는 것으로 최단경로흐