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.

This study aims to develop a regression model using data from the Ammunition Stockpile Reliability Program (ASRP) to predict the shelf life of 81mm mortar high-explosive shells. Ammunition is a single-use item that is discarded after use, and its quality is managed through sampling inspections. In particular, shelf life is closely related to the performance of the propellant. This research seeks to predict the shelf life of ammunition using a regression model. The experiment was conducted using 107 ASRP data points. The dependent variable was 'Storage Period', while the independent variables were 'Mean Ammunition Velocity,' 'Standard Deviation of Mean Ammunition Velocity,' and 'Stabilizer'. The explanatory power of the regression model was an R-squared value of 0.662. The results indicated that it takes approximately 55 years for the storage grade to change from A to C and about 62 years to change from C to D. The proposed model enhances the reliability of ammunition management, prevents unnecessary disposal, and contributes to the efficient use of defense resources. However, the model's explanatory power is somewhat limited due to the small dataset. Future research is expected to improve the model with additional data collection. Expanding the research to other types of ammunition may further aid in improving the military's ammunition management system.

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.

본 논문에서는 다목적 구조물인 다중연결 해양부유체를 대상으로 변형 기반 모드 차수축소법을 적용하고 차수축소모델의 구조응 답 예측 성능을 향상시키기 위해 유전 알고리즘 기반의 센서 배치 최적화를 수행하였다. 다중연결 해양부유체의 차수축소모델 생성 에 필요한 변형 기반 모드 데이터를 얻기 위해 다양한 규칙파랑하중조건에 대한 유체-구조 연성 수치해석을 수행하고 변형 기반 모드 의 직교성, 자기상관계수를 이용하여 주요 변형 기반 모드를 선정하였다. 다중연결 해양부유체의 경우 차수축소모델의 구조응답 예 측 성능이 계측 및 예측 구조응답 위치에 따라 민감하기 때문에 유전 알고리즘 기반의 최적화를 수행하여 최적의 센서 배치를 도출하 였다. 최적화 결과, 모든 센서 배치 조합에 대한 차수축소모델 생성 및 예측 성능 평가 대비 약 8배의 계산 비용을 절감하였으며, 예측 성능 평가 지표인 평균 제곱근 오차가 초기 센서 배치보다 84% 감소하였다. 또한, 다중연결 해양부유체 모형시험 결과를 이용하여 불 규칙파랑하중에 대한 최적화된 센서 배치의 차수축소모델의 구조응답 예측 성능을 평가 및 검증하였다.

콩과 같은 밭작물은 주로 토양으로부터 수분을 공급받으며 토양 수분 조건에 따라 생육 반응이 민감하게 반응한다. 작물의 생육과 재배 지역의 토양 조건, 기상 등에 따라 적정 토양 수분을 유지하는 것은 작물 생산량의 증가를 위해 중요하다. 따라서, 본 연구에서는 머신러닝 기법을 이용하여 토양 수분 함량 예측 모델을 개발하였다. 깊이에 따른 토양 수분과 외기, 강수량 등 기상 변수와의 상관 관계를 구명하고, 깊이별 토양 수분예측을 위한 부분최소제곱회귀(PLSR) 모델을 알고리즘을 개발하였다. 콩 재배포장의 10cm, 20cm, 30cm 깊이의 토양수분은 FDR 방식의 센서로 측정하였 고, 콩 작물 주변 환경인자(재배환경의 기온, 상대습도, 풍속, 일사량, 일조시간)는 주변의 기상관측소에서 측정된 데이터를 이용하였다. 이를 이용하여 깊이별 미래의 토양수분함량 예측 모델을 개발한 결과, 10cm와 20cm깊이에서 주요 인자는 현재 토양수분함량과 기온이었으며, 30cm 깊이에서의 주요 인자는 현재 토양수분함량과 기온, 풍속으로 나타났다. 토양 깊이가 깊어짐에 따라 토양수분함량 예측 정확도가 향상되었으며, 이는 표면에 가까울수록 토양수분함량이 변화가 크기 때문으로 예상된다. 또한 미래의 토양 수분함량예측시 1시간 후 예측 정확도가 가장 우수하였으며, 이때의 Rv 2와 RMSEV가 10cm 깊이에서 0.993와 1.069%, 20cm 깊이에서 0.994와 0.821% 였으며, 30cm 깊이에서 0.999와 0.149% 였다. 본 연구 결과는 콩 생육환경 진단을 위해 재배 포장의 토양수분함량을 토양층별로 미래의 토양수분함량도 예측이 가능함을 보여준다.

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.

PURPOSES : Pavement surface friction depends significantly on pavement surface texture characteristics. The mean texture depth (MTD), which is an index representing pavement surface texture characteristics, is typically used to predict pavement surface friction. However, the MTD may not be sufficient to represent the texture characteristics to predict friction. To enhance the prediction of pavement surface friction, one must select additional variables that can explain complex pavement surface textures. METHODS : In this study, pavement surface texture characteristics that affect pavement surface friction were analyzed based on the friction mechanism. The wavelength, pavement surface texture shape, and pavement texture depth were hypothesized to significantly affect the surface friction of pavement. To verify this, the effects of the three abovementioned pavement surface texture characteristics on pavement surface friction must be investigated. However, because the surface texture of actual pavements is irregular, examining the individual effects of these characteristics is difficult. To achieve this goal, the selected pavement surface texture characteristics were formed quantitatively, and the irregularities of the actual pavement surface texture were improved by artificially forming the pavement surface texture using threedimensionally printed specimens. To reflect the pavement surface texture characteristics in the specimen, the MTD was set as the pavement surface texture depth, and the exposed aggregate number (EAN) was set as a variable. Additionally, the aggregate shape was controlled to reflect the characteristics of the pavement surface texture of the specimen. Subsequently, a shape index was proposed and implemented in a statistical analysis to investigate its effect on pavement friction. The pavement surface friction was measured via the British pendulum test, which enables measurement to be performed in narrow areas, considering the limited size of the three-dimensionally printed specimens. On wet pavement surfaces, the pavement surface friction reduced significantly because of the water film, which intensified the effect of the pavement surface texture. Therefore, the pavement surface friction was measured under wet conditions. Accordingly, a BPN (wet) prediction model was proposed by statistically analyzing the relationship among the MTD, EAN, aggregate shape, and BPN (wet). RESULTS : Pavement surface friction is affected by adhesion and hysteresis, with hysteresis being the predominant factor under wet conditions. Because hysteresis is caused by the deformation of rubber, pavement surface friction can be secured through the formation of a pavement surface texture that causes rubber deformation. Hysteresis occurs through the function of macro-textures among pavement surface textures, and the effects of macro-texture factors such as the EAN, MTD, and aggregate shape on the BPN (wet) are as follows: 1) The MTD ranges set in this study are 0.8, 1.0, and 1.2, and under the experimental conditions, the BPN (wet) increases linearly with the MTD. 2) An optimum EAN is indicated when the BPN (wet) is the maximum, and the BPN decreases after its maximum value is attained. This may be because when the EAN increases excessively, the space for the rubber to penetrate decreases, thereby reducing the hysteresis. 3) The shape of the aggregate is closely related to the EAN; meanwhile, the maximum value of the pavement surface friction and the optimum EAN change depending on the aggregate shape. This is believed to be due to changes in the rubber penetration volume based on the aggregate shape. Based on the results above, a statistical prediction model for the BPN (wet) is proposed using the MTD, EAN, and shape index as variables. CONCLUSIONS : The EAN, MTD, and aggregate shape are crucial factors in predicting skid resistance. Notably, the EAN and aggregate shape, which are not incorporated into existing pavement surface friction prediction models, affect the pavement surface friction. However, the texture of the specimen created via three-dimensional printing differs significantly from the actual pavement surface texture. Therefore, the pavement surface friction prediction model proposed in this study should be supplemented with comparisons with actual pavement surface data in the future.

고자리꽃파리는 양파 및 마늘 등 백합과 Allium 속에 속하는 농작물에 중요한 해충으로 전 세계적으로 온대지역에 서 경제적 해충으로 취급하고 있다. 본 연구에서는 기존 자료를 바탕으로 월동번데기의 성충으로 우화모델를 작성하 고 포장 실측자료와 비교하여 평가하였다. 월동번데기 발육모형으로 선형과 비선형모형을 작성하고 발육기간 분포 모형과 결합하여 예찰모형을 작성하였다. 비선형발육모형 작성시 3-매개변수 락틴모형 적용뿐만 아니라 4-매개변 수 모형의 마지막 변수 값을 선형모형의 절편값으로 대체하여 저온에서 선형성이 강화도록 변형시켰다. 성충우화 50% 예측에서 일일평균온도를 이용하는 경우 적산온도 모형을 비롯하여 발육률 적산모형(선형식 및 비선형식) 모두 실측치와 큰 차이가 있었다. 시간별온도를 입력값으로 한 경우 3-매개변수 모형을 제외한 사인곡선 적산온도 모형, 선형 발육률 적산모형, 4-매개변수 비선형 발육률 적산모형의 평균편차는 3일과 차이가 없었다. 최종적으로 선형모형 및 4-매개변수 비선형모형을 바탕으로 시간별온도자료를 이용한 발육률 적산모형은 선발하였다. 그 결과 선형 발육률 적산모형이 두 포장적합 집단(1984, 1987)에서 실측일과 편차가 3일과 차이가 없었다. 비선형 발육률 적산모형은 1984년 적합은 0.8일 편차로 정확하였으나 1987년 집단에서 평균편차가 6.5일로 다소 증가하였다.

여름철은 타 계절에 비해 장마와 불안정한 대기 등으로 인하여 빗길 교통사고의 위험성이 크게 증대될 수 있으며, 최근 5년 (2018~2022)간 여름철 빗길 교통사고는 전체 빗길 교통사고의 39%를 차지할 정도로 높은 수준이다. 이러한 빗길 운전은 노면의 배수 불량 및 미끄럼 저항 감소 등으로 인하여 수막현상을 발생시키게 된다. 이에 본 연구에서는 우천 시 도로의 안전성 강화 및 사고 위 험을 최소화하기 위한 요소인 수막두께를 산정하기 위하여 Manning의 평균 유속식을 기반으로 콘크리트 노면의 조도계수 예측 모델을 개발하는 것을 목표로 하였다. 조도계수의 영향인자를 고려하기 위하여 실외 강우 모의 장비를 이용하여 콘크리트를 타설한 뒤 실험 인자로 포장 경사, 배수거리, 강우강도, 노면 조직 특성을 고려하였으며, 이 중 노면 조직 특성은 타이닝 처리를 하지 않은 구간만 고 려한 타 연구의 기존 예측 모델 단점을 보완하기 위하여 16, 25mm 간격의 타이닝 표면 처리한 구간을 추가로 고려하였다. 수막두께 측정은 측정 범위 0.3~5mm의 수막두께 측정 게이지를 제작하여 강우가 모사된 조건에서 배수 거리 1~5m 이내 지점의 노면 조직 상 단과 수면이 접하는 수직 높이를 총 3회 측정하여 평균값을 사용하였다. 실측된 수막두께 데이터베이스를 기반으로 Manning 공식을 이용하여 조도계수를 역산한 결과, 강우강도가 증가함에 따라 조도계수는 감소하였으며, 이는 강우의 증가로 인해 물의 흐름과 콘크리 트 노면 사이의 마찰 저항 감소에 기인한 것으로 판단되었다. 또한 포장 경사가 높고 배수 거리가 짧을수록 배수성이 증가하여 마찰 저항의 지표인 조도계수가 증가하는 것으로 확인되었다. 평균 조직 깊이에 따른 조도계수 영향의 경우, 평균 조직 깊이가 증가할수록 콘크리트 표면에 노출되는 표면적이 증가하여 수막두께가 얕게 생성되고, 얕은 수심으로 인해 물의 흐름 저항이 감소하여 조도계수는 감소하는 것으로 산정되었다. 이후 135개의 데이터를 종합하여 조도계수를 종속변수로 하고 강우강도, 포장경사, 배수거리, 평균 조직 깊이, 수막두께를 독립변수로 하는 회귀분석을 수행하여 조도계수 산정식을 개발하였다.

This study deals with the application of an artificial neural network (ANN) model to predict power consumption for utilizing seawater source heat pumps of recirculating aquaculture system. An integrated dynamic simulation model was constructed using the TRNSYS program to obtain input and output data for the ANN model to predict the power consumption of the recirculating aquaculture system with a heat pump system. Data obtained from the TRNSYS program were analyzed using linear regression, and converted into optimal data necessary for the ANN model through normalization. To optimize the ANN-based power consumption prediction model, the hyper parameters of ANN were determined using the Bayesian optimization. ANN simulation results showed that ANN models with optimized hyper parameters exhibited acceptably high predictive accuracy conforming to ASHRAE standards.

현대 해양 산업은 기술적 발전을 통해 신속한 발전을 이루고 있다. 이러한 발전을 주도하는 주요 기술 중 하나는 데이터 처리 기술이며, 이 중 자연어 처리 기법은 사람의 언어를 기계가 이해하고 처리할 수 있도록 하는 기술이다. 본 연구는 자연어 처리 기법을 통해 해양안전심판원의 재결서를 분석하여 이미 재결이 이루어진 선박 충돌사고의 원인 제공 비율을 학습한 후, 새로운 재결서를 입력 하면 원인 제공 비율을 예측하는 모델을 개발하고자 하였다. 이 모델은 사고 당시 적용되는 항법과 원인 제공 비율에 영향을 주는 핵심 키워드의 가중치를 이용하여 사고의 원인 제공 비율을 계산하는 방식으로 구성하였다. 이 연구는 이러한 방식을 통해 제작한 모델의 정 확도를 분석하고, 모델의 실무 적용 가능성을 검토함과 동시에 충돌사고 재발 방지 및 해양사고 당사자들의 분쟁 해결에 기여할 것으로 기대한다.

PURPOSES : In this study, an optimal model for compressive strength prediction was derived by learning and directly comparing several machine learning models based on the same data. METHODS : Approximately 478 pieces of concrete compressive strength data were obtained to compare the performance of the machine learning models. In addition, five machine learning models were trained based on the obtained data. The performance of the learned model was compared using three performance indicators. Finally, the performance of the model trained using additional data was reviewed. RESULTS : As a result of comparing the performance of machine learning models, the XGB(eXtra Gradient Boost) model showed the best performance. In addition, as a result of the verification based on additional data, highly reliable results can be obtained if the XGB model is used to predict the compressive strength of concrete. CONCLUSIONS : If a concrete strength prediction model is derived based on a machine learning model, a highly reliable model can be derived.

PURPOSES : In this study, the applicability of the water content, suction, and suction stress in a resilient modulus prediction model for a subbase was reviewed. METHODS : To compare the applicability of water content, suction, and suction stress models for resilient modulus prediction, the suction stress was determined based on the soil water characteristic curve. The model parameters for each approach were derived from the measured resilient moduli. Finally, the relationships between the degree of saturation and resilient modulus were analyzed using the calculated model parameters. RESULTS : Prediction models of the resilient modulus based on water content and suction demonstrated high correlation with measured values, but overestimated the resilient modulus at saturation levels beyond the laboratory testing range. In contrast, the model accounting for suction stress effectively reduced this overestimation, likely owing to a decrease in suction stress as the suction increased. CONCLUSIONS : Based on the above results, the resilient modulus of subbase materials could be estimated through the change in the degree of saturation and the stress-dependent resilient modulus model using the suction stress proposed in this study.

Occurrence of process environment changes, such as influent load variances and process condition changes, can reduce treatment efficiency, increasing effluent water quality. In order to prevent exceeding effluent standards, it is necessary to manage effluent water quality based on process operation data including influent and process condition before exceeding occur. Accordingly, the development of the effluent water quality prediction system and the application of technology to wastewater treatment processes are getting attention. Therefore, in this study, through the multi-channel measuring instruments in the bio-reactor and smart multi-item water quality sensors (location in bio-reactor influent/effluent) were installed in The Seonam water recycling center #2 treatment plant series 3, it was collected water quality data centering around COD, T-N. Using the collected data, the artificial intelligence-based effluent quality prediction model was developed, and relative errors were compared with effluent TMS measurement data. Through relative error comparison, the applicability of the artificial intelligence-based effluent water quality prediction model in wastewater treatment process was reviewed.

The conventional multi-scale modelling approach that predicts carbon nanotube (CNT) growth region in heterogeneous flame environment is computationally exhaustive. Thus, the present study is the first attempt to develop a zero-dimensional model based on existing multi-scale model where mixture fraction z and the stoichiometric mixture fraction zst are employed to correlate burner operating conditions and CNT growth region for diffusion flames. Baseline flame models for inverse and normal diffusion flames are first established with satisfactory validation of the flame temperature and growth region prediction at various operating conditions. Prior to developing the correlation, investigation on the effects of zst on CNT growth region is carried out for 17 flame conditions with zst of 0.05 to 0.31. The developed correlation indicates linear ( zlb=1.54zst +0.11) and quadratic ( zhb=zst(7-13zst )) models for the zlb and zhb corresponding to the low and high boundaries of mixture fraction, respectively, where both parameters dictate the range of CNT growth rate (GR) in the mixture fraction space. Based on the developed correlations, the CNT growth in mixture fraction space is optimum in the flame with medium-range zst conditions between 0.15 and 0.25. The stronger relationship between growth-region mixture-fraction (GRMF) and zst at the near field region close to the flame sheet compared to that of the far field region away from the flame sheet is due to the higher temperature gradient at the former region compared to that of the latter region. The developed models also reveal three distinct regions that are early expansion, optimum, and reduction of GRMF at varying zst.

Approximately 40,000 elevators are installed every year in Korea, and they are used as a convenient means of transportation in daily life. However, the continuous increase in elevators has a social problem of increased safety accidents behind the functional aspect of convenience. There is an emerging need to induce preemptive and active elevator safety management by elevator management entities by strengthening the management of poorly managed elevators. Therefore, this study examines domestic research cases related to the evaluation items of the elevator safety quality rating system conducted in previous studies, and develops a statistical model that can examine the effect of elevator maintenance quality as a result of the safety management of the elevator management entity. We review two types: odds ratio analysis and logistic regression analysis models.

Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms—specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms—to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on 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 analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.