Many school buildings are vulnerable to earthquakes because they were built before mandatory seismic design was applied. This study uses machine learning to develop an algorithm that rapidly constructs an optimal reinforcement scheme with simple information for non-ductile reinforced concrete school buildings built according to standard design drawings in the 1980s. We utilize a decision tree (DT) model that can conservatively predict the failure type of reinforced concrete columns through machine learning that rapidly determines the failure type of reinforced concrete columns with simple information, and through this, a methodology is developed to construct an optimal reinforcement scheme for the confinement ratio (CR) for ductility enhancement and the stiffness ratio (SR) for stiffness enhancement. By examining the failure types of columns according to changes in confinement ratio and stiffness ratio, we propose a retrofit scheme for school buildings with masonry walls and present the maximum applicable stiffness ratio and the allowable range of stiffness ratio increase for the minimum and maximum values of confinement ratio. This retrofit scheme construction methodology allows for faster construction than existing analysis methods.

Due to seismically deficient details, existing reinforced concrete structures have low lateral resistance capacities. Since these building structures suffer an increase in axial loads to the main structural element due to the green retrofit (e.g., energy equipment/device, roof garden) for CO2 reduction and vertical extension, building capacities are reduced. This paper proposes a machine-learning-based methodology for allowable ranges of axial loading ratio to reinforced concrete columns using simple structural details. The methodology consists of a two-step procedure: (1) a machine-learning-based failure detection model and (2) column damage limits proposed by previous researchers. To demonstrate this proposed method, the existing building structure built in the 1990s was selected, and the allowable range for the target structure was computed for exterior and interior columns.

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.

최근 늘어나고 있는 이상 기상 현상으로 산사태 위험이 점차 증가하고 있다. 산사태는 막대한 인명 피해와 재산 피해를 초래할 수 있기에 이러한 위험을 사전에 평가함은 매우 중요하다. 최근 기술 발전으로 인해 능동형 원격탐사 방법을 사용하여 더 정확하고 상세한 지표 변위 및 강수 데이터를 얻을 수 있게 되었다. 그러나 이러한 데이터를 활용하여 산사태 예측 모델을 개발하는 연구는 찾기 힘들다. 따라서 본 연구에서는 합성개구레이더 간섭법(InSAR)을 사용한 지표 변위 자료와 하이브리드 고도면 강우(HSR) 추정 기법을 통한 강수 정보를 활용하여 산사태 민감도를 예측하는 기계학습 모델을 제시하고 있다. 나아가 기계학습의 블랙박스 문제를 극복할 수 있는 해석가능한 기계학습 방법인 SHAP을 이용하여 산사태 민감도의 영향 변수에 대한 중요도를 체계적으로 평가하였다. 경상북도 울진군을 대상으로 사례 연구를 수행한 결과, XGBoost가 가장 좋은 예측 성능을 보이며, 도로로부터의 거리, 지표 고도, 일 최대 강우 강도, 48시간 선행 누적 강우량, 사면 경사, 지형습윤지수, 단층으로 부터의 거리, 경사도, 지표 변위, 하천으로부터의 거리가 산사태 예측에 영향을 미치는 주요 변수로 밝혀졌다. 특히, 능동형 원격탐사를 통해 얻은 자료인 강우 강도와 지표 변위의 절댓값이 높을수록 산사태 발생 확률이 높음을 확인하였다. 본 연구는 능동형 원격탐사 자료의 산사태 민감도 연구에서의 활용 가능성을 실증적으로 보여주고 있으며, 해당 자료를 바탕으로 시공간적 으로 변하는 산사태 민감도를 도출함으로써 향후 산사태 민감도 모니터링에 효과적으로 활용될 수 있을 것으로 기대된다.

본 연구는 돼지 간 거리(PD), 돈사 내 상대 습도(RRH), 돈사 내 이산화탄소(RCO2) 세 가지 변수를 사용하여, 네 개의 데이터 세트를 구성하고, 이를 다중 선형 회귀(MLR), 서포트 벡터 회귀(SVR) 및 랜덤 포레스트 회귀(RFR) 세 가지 모델 기계학습(ML)에 적용하여, 돈사 내 온도(RT)를 예측하고자 한다. 2022년 10월 5일부터 11월 19일까지 실험을 진행하였다. Hik-vision 2D카메라를 사용하여, 돈사 내 영상을 기록하였다. 이후 ArcMap 프로그램을 사용하여, 돈사 내 영상에서 추출한 이미지 안 돼지의 PD를 계산하였다. 축산환경관리시스템(LEMS) 센서를 사용하여, RT, RRH 및 RCO2를 측정하였다. 연구 결과 각 변수 간 상관분석 시 RT와 PD 간의 강한 양의 상관관계가 나타났다(r > 0.75). 네 가지 데이터 세트 중 데이터 세트 3을 사용한 ML 모델이 높은 정확도가 나타났으며, 세 가지 회귀 모델 중에서 RFR 모델이 가장 우수한 성능을 보였다.

In this study, the magnetocaloric effect and transition temperature of bulk metallic glass, an amorphous material, were predicted through machine learning based on the composition features. From the Python module ‘Matminer’, 174 compositional features were obtained, and prediction performance was compared while reducing the composition features to prevent overfitting. After optimization using RandomForest, an ensemble model, changes in prediction performance were analyzed according to the number of compositional features. The R2 score was used as a performance metric in the regression prediction, and the best prediction performance was found using only 90 features predicting transition temperature, and 20 features predicting magnetocaloric effects. The most important feature when predicting magnetocaloric effects was the ‘Fe’ compositional ratio. The feature importance method provided by ‘scikit-learn’ was applied to sort compositional features. The feature importance method was found to be appropriate by comparing the prediction performance of the Fe-contained dataset with the full dataset.

Existing reinforced concrete (RC) building frames constructed before the seismic design was applied have seismically deficient structural details, and buildings with such structural details show brittle behavior that is destroyed early due to low shear performance. Various reinforcement systems, such as fiber-reinforced polymer (FRP) jacketing systems, are being studied to reinforce the seismically deficient RC frames. Due to the step-by-step modeling and interpretation process, existing seismic performance assessment and reinforcement design of buildings consume an enormous amount of workforce and time. Various machine learning (ML) models were developed using input and output datasets for seismic loads and reinforcement details built through the finite element (FE) model developed in previous studies to overcome these shortcomings. To assess the performance of the seismic performance prediction models developed in this study, the mean squared error (MSE), R-square (R2), and residual of each model were compared. Overall, the applied ML was found to rapidly and effectively predict the seismic performance of buildings according to changes in load and reinforcement details without overfitting. In addition, the best-fit model for each seismic performance class was selected by analyzing the performance by class of the ML models.

본 논문은 1980년대 초 도미 이후 지속적으로 기하학적 드로잉의 형태로 작업을 이어 가는 이상남의 도상에 대해 분석하고 있다. 그가 뉴욕이라는 배경에서 과거의 국내 또는 뉴 욕 현지에서의 주된 미술의 조류에 동화되는 일 없이 독특한 기하학적이고 기계적인 이미지 로만 작업을 이어가는 과정을 마르셀 뒤샹이 1911년 이후 기계적 이미지를 화면에 도입하 고 그 이후로는 완전한 기하학적 추상, 또는 기계적 구성으로만 작품을 구성하는 점과 비교, 연구하고 있다. 이들 화풍에 공통되는 점은 첫째, 직전 세대에 통용되는 화법으로부터의 명 백한 단절을 꾀함으로써 전통회화적 기법에서 해방되고자 했다는 점, 둘째, 이를 위한 방법 으로 고질적 회화의 기술, 즉, 손이 익힌 화법을 차단하고자 몰개성(de-personalize)적인 기 하학 또는 기계 이미지만을 그리거나 제작했다는 점, 마지막으로 그렇기 때문에 이들에게 있 어 기하학은 20세기 초의 추상화로의 움직임에서 전형적인 순수한 형식상의 필요에 의한 변 화로만 파악하기는 어렵다는 점 등을 들 수 있다.

최근 지구온난화로 인해 발생하는 폭우 및 강설과 같은 비정상적인 기상 패턴으로 인해 도로 표면 결빙(블랙 아이스)으로 인 한 사고와 인명 피해가 증가하고 있으며, 이는 주요 문제로 대두되고 있습니다. 이러한 문제를 완화하기 위해 본 연구에서는 열저장 능력을 갖춘 상변화 물질(PCM)을 시멘트 복합재료에 포함시켰습니다. PCM은 상변화 과정에서 열에너지를 흡수, 저장 및 방출할 수 있어 온도 변동으로 인한 결빙을 최소화할 수 있습니다. PCM은 먼저 미세 캡슐화된 후 시멘트 복합재료에 강화되어 기계적 및 열적 성능 검증 연구가 수행되었습니다. 또한, 열전달 효율과 기계적 특성을 향상시키기 위해 다중벽 탄소나노튜브(CNT)와 실리카 퓸이 추 가되었습니다. 미세 캡슐화된 PCM의 열 성능은 열 거동을 측정하기 위한 재료 실험을 통해 검증되었습니다. 이후, 제조된 시멘트 복 합재의 기계적 및 열적 성능 테스트가 그 효과를 평가하기 위해 수행되었습니다. 이러한 테스트 동안 일정 온도와 습도 챔버를 사용한 열 주기 테스트가 열 성능을 검증하기 위해 수행되었습니다. 기계적 성능 실험에서는 CNT와 실리카 퓸의 포함이 미세 캡슐화된 PCM 의 포함으로 인한 강도 저하를 완화하는 것을 확인하였습니다. 더욱이, 열 주기 테스트를 통해 고효율 열저장 시멘트 복합재가 결빙 조건에서도 영하의 온도를 유지할 수 있음을 보여주었으며, 이는 효율적인 열저장 성능을 입증하였습니다.

New motor development requires high-speed load testing using dynamo equipment to calculate the efficiency of the motor. Abnormal noise and vibration may occur in the test equipment rotating at high speed due to misalignment of the connecting shaft or looseness of the fixation, which may lead to safety accidents. In this study, three single-axis vibration sensors for X, Y, and Z axes were attached on the surface of the test motor to measure the vibration value of vibration. Analog data collected from these sensors was used in classification models for anomaly detection. Since the classification accuracy was around only 93%, commonly used hyperparameter optimization techniques such as Grid search, Random search, and Bayesian Optimization were applied to increase accuracy. In addition, Response Surface Method based on Design of Experiment was also used for hyperparameter optimization. However, it was found that there were limits to improving accuracy with these methods. The reason is that the sampling data from an analog signal does not reflect the patterns hidden in the signal. Therefore, in order to find pattern information of the sampling data, we obtained descriptive statistics such as mean, variance, skewness, kurtosis, and percentiles of the analog data, and applied them to the classification models. Classification models using descriptive statistics showed excellent performance improvement. The developed model can be used as a monitoring system that detects abnormal conditions of the motor test.

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.

In this study, the SBC system, a new mechanical joint method, was developed to improve the constructability of precast concrete (PC) beam-column connections. The reliability of the finite element analysis model was verified through the comparison of experimental results and FEM analysis results. Recently, the intermediate moment frame, a seismic force resistance system, has served as a ramen structure that resists seismic force through beams and columns and has few load-bearing walls, so it is increasingly being applied to PC warehouses and PC factories with high loads and long spans. However, looking at the existing PC beam-column anchorage details, the wire, strand, and lower main bar are overlapped with the anchorage rebar at the end, so they do not satisfy the joint and anchorage requirements for reinforcing bars (KDS 41 17 00 9.3). Therefore, a mechanical joint method (SBC) was developed to meet the relevant standards and improve constructability. Tensile and bending experiments were conducted to examine structural performance, and a finite element analysis model was created. The load-displacement curve and failure pattern confirmed that both the experimental and analysis results were similar, and it was verified that a reliable finite element analysis model was built. In addition, bending tests showed that the larger the thickness of the bolt joint surface of the SBC, the better its structural performance. It was also determined that the system could improve energy dissipation ability and ductility through buckling and yielding occurring in the SBC.