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.

In this paper, machine learning models were applied to predict the seismic response of steel frame structures. Both geometric and material nonlinearities were considered in the structural analysis, and nonlinear inelastic dynamic analysis was performed. The ground acceleration response of the El Centro earthquake was applied to obtain the displacement of the top floor, which was used as the dataset for the machine learning methods. Learning was performed using two methods: Decision Tree and Random Forest, and their efficiency was demonstrated through application to 2-story and 6-story 3-D steel frame structure examples.

Machine learning is widely applied to various engineering fields. In structural engineering area, machine learning is generally used to predict structural responses of building structures. The aging deterioration of reinforced concrete structure affects its structural behavior. Therefore, the aging deterioration of R.C. structure should be consider to exactly predict seismic responses of the structure. In this study, the machine learning based seismic response prediction model was developed. To this end, four machine learning algorithms were employed and prediction performance of each algorithm was compared. A 3-story coupled shear wall structure was selected as an example structure for numerical simulation. Artificial ground motions were generated based on domestic site characteristics. Elastic modulus, damping ratio and density were changed to considering concrete degradation due to chloride penetration and carbonation, etc. Various intensity measures 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 and extreme gradient boosting algorithms present good prediction performance.

여러 센서를 이용한 구조물의 구조 응답을 모니터링하는 사례가 증가하고 있다. 그러나 비용과 관리 문제로 인해 제한된 센서만이 구조물에 설치되어 일부의 구조 응답만을 수집하는 경우가 대부분이다. 이는 구조물의 전체 거동을 분석하는데 장애요소로 작용하게 된다. 따라서 제한된 센서를 이용해 센서가 설치되지 않은 위치에서의 응답을 신뢰할 수 있는 수준으로 예측하는 기술이 필요하다. 본 연구에서는 제한된 정보를 이용해 저층 건물 구조물의 지진 응답을 예측하는 해석적 연구를 수행한다. 활용 가능한 응답 정보는 1층과 최상층의 가속도 응답만을 사용할 수 있다고 가정한다. 두 정보를 이용하면 구조물의 1차 고유진동수를 얻을 수 있다. 1층 가속도 정보는 구조물의 가력 정보로 활용한다. 최상층의 가속도이력응답에 대한 오차와 대상 구조물의 1차 고유진동수 오차를 최소화하는 구조물의 질량과 강성 정보를 유전자알고리즘을 이용해 예측하는 기법을 제시한다. 제약조건은 고려하지 않는다. 탐색공간을 의미하는 설계변수의 범위를 결정하기 위해 인공신경망 기반의 파라미터 예측기법을 제시한다. 또한 유전자알고리즘을 통해 얻게 되는 해를 개선시키기 위해 앞서 언급한 인공신경망을 활용한다. 제시한 기법을 검증하기 위해 5층 구조물 예제를 사용한다.

Recently, deep learning that is the most popular and effective class of machine learning algorithms is widely applied to various industrial areas. A number of research on various topics about structural engineering was performed by using artificial neural networks, such as structural design optimization, vibration control and system identification etc. When nonlinear semi-active structural control devices are applied to building structure, a lot of computational effort is required to predict dynamic structural responses of finite element method (FEM) model for development of control algorithm. To solve this problem, an artificial neural network model was developed in this study. Among various deep learning algorithms, a recurrent neural network (RNN) was used to make the time history response prediction model. An RNN can retain state from one iteration to the next by using its own output as input for the next step. An eleven-story building structure with semi-active tuned mass damper (TMD) was used as an example structure. The semi-active TMD was composed of magnetorheological damper. Five historical earthquakes and five artificial ground motions were used as ground excitations for training of an RNN model. Another artificial ground motion that was not used for training was used for verification of the developed RNN model. Parametric studies on various hyper-parameters including number of hidden layers, sequence length, number of LSTM cells, etc. After appropriate training iteration of the RNN model with proper hyper-parameters, the RNN model for prediction of seismic responses of the building structure with semi-active TMD was developed. The developed RNN model can effectively provide very accurate seismic responses compared to the FEM model.

This study develops an empirical prediction equation of spectral acceleration responses of earthquakes which can induce structural damages. Ground motion records representing hazards of low-to-moderate seismic regions were selected and organized with several influential factors affecting the response spectra. The empirical equation and estimator coefficients for acceleration response spectra were then proposed using a robust nonlinear optimization coupled with a regression analysis. For analytical verification of the prediction equation, response spectra used for low-to-moderate seismic regions were estimated and the predicted results were comparatively evaluated with measured response spectra. As a result, the predicted shapes of response spectra can simulate the graphical shapes of measured data with high accuracy and most of predicted results are distributed inside range of correlation of variation (COV) of 30% from perfectly correlated lines.

This study suggests a prediction model of ground motion spectral shape considering characteristics of earthquake records in Korea. Based on the Graizer and Kalkan’s prediction procedure, a spectral shape model is defined as a continuous function of period in order to improve the complex problems of the conventional models. The approximate spectral shape function is then developed with parameters such as moment magnitude, fault distance, and average shear velocity of independent variables. This paper finally determines estimator coefficients of subfunctions which explain the corelation among the independent variables using the nonlinear optimization. As a result of generating the prediction model of ground motion spectral shape, the ground motion spectral shape well estimates the response spectrum of earthquake recordings in Korea.

원자력발전소에 설치되는 주요 전기기기들의 내부 부품을 내진검증하기 위해서는 캐비닛내부응답스펙트럼이 필요하고, 이는 캐비닛의 각 위치에서 정확한 지진응답을 구한 후에 생성이 가능하다. 반면에 대부분의 전기기기는 질량과 강성 분포가 복잡하기 때문에 해석적 방법에 의해 동적 분석을 수행하는 것이 어렵다. 이러한 여건을 감안하여 이 연구에서는 해석과 시험을 조합하여 기기의 지진응답을 예측하는 간편한 절차를 제안하였다. 제안된 절차는 먼저 충격시험을 통하여 규명된 실험모드특성을 이용하여 독립된 모드방정식을 구성하고, 이로부터 모드응답을 계산한 다음, 각 모드응답을 중첩함으로써 구조물의 지진응답을 예측한다. 제안된 절차의 신뢰성을 검증하기 위해서, 별도로 제작된 단순 강재 프레임 시편에 제안된 절차를 적용하여 지진응답을 예측하고, 이를 실제 진동대시험을 통하여 계측한 결과와 비교하였다. 이 연구를 통하여 충격시험에 의해 얻어진 실험모드특성을 이용하여 구조물의 지진응답을 비교적 정확하게 예측할 수 있음을 확인하였다.

본 연구는 원자력발전소에 설치되는 캐비닛형 전기기기의 동적 진동시험 자료를 이용하여 캐비닛의 지진응답을 예측할 수 있는 기법을 제안하였다. 제안된 기법은 1) 절점질량 이상화 모델에 기반한 등가 지진하중 산정, 2) 진동시험자료에 기반한 캐비닛 구조의 입출력 상태방정식 규명, 3) 산정된 등가지진하중과 규명된 입출력 상태방정식을 사용한 지진응답산정의 과정으로 구성된다. 제안된 기법은 유한요소기법(FEM) 모델 개선(Model Updating)에 기반한 지진응답예측기법에 비하여 모델링 오차가 개입 되지 않는 장점을 가진다. 캐비넷 구조를 이상화한 2차원 프레임 모델과 3차원 상세 모델에 대한 수치검증을 통하여 제안된 기법이 지진응답을 매우 정확하게 예측을 함을 관찰하였고, 측정 노이즈에 대해서도 강인함을 관찰하였다. 추후연구로 실험검증이 요구된다.

지진 시 필댐에 손상이 발생하는 경우, 심각한 인명 및 경제적 피해를 유발할 수 있으므로 이의 안정성 평가는 중요하다. 필댐의 손상은 일반적으로 수치해석으로 계산된 침하량으로 평가된다. 하지만 수치해석모델을 내진성능평가에 활용하기 위해서는 선제적으로 수치모델의 검증이 필요한다. 본 연구에서는 필댐의 계측기록과 동적 해석결과를 비교하여 수치모델의 정확성을 평가하였다. 비교 결과, 수치해석은 필댐의 동적응답을 정확하게 예측하는 것으로 나타났다. 따라서 수치해석은 필댐의 내진성능평가에 활용 가능한 것으로 평가된다.