Existing reinforced concrete buildings with seismically deficient column details affect the overall behavior depending on the failure type of column. This study aims to develop and validate a machine learning-based prediction model for the column failure modes (shear, flexure-shear, and flexure failure modes). For this purpose, artificial neural network (ANN), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) models were used, considering previously collected experimental data. Using four machine learning methodologies, we developed a classification learning model that can predict the column failure modes in terms of the input variables using concrete compressive strength, steel yield strength, axial load ratio, height-to-dept aspect ratio, longitudinal reinforcement ratio, and transverse reinforcement ratio. The performance of each machine learning model was compared and verified by calculating accuracy, precision, recall, F1-Score, and ROC. Based on the performance measurements of the classification model, the RF model represents the highest average value of the classification model performance measurements among the considered learning methods, and it can conservatively predict the shear failure mode. Thus, the RF model can rapidly predict the column failure modes with simple column details.

기후변화 영향으로 이상고수온, 태풍, 홍수, 가뭄 등 재난 및 안전 관리기술은 지속적으로 고도화를 요구받고 있으며, 특히 해 수면 온도는 한반도 주변에서 발생되는 여름철 적조 발생과 동해안 냉수대 출현, 소멸 등에 영향을 신속하게 분석할 수 있는 중요한 인자 이다. 따라서, 본 연구에서는 해수면 온도 자료를 해양 이상현상 및 연구에 적극 활용되기 위해 통계적 방법과 딥러닝 알고리즘을 적용하 여 예측성능을 평가하였다. 예측에 사용된 해수면 수온자료는 흑산도 조위관측소의 2018년부터 2022년까지 자료이며, 기존 통계적 ARIMA 방법과 Long Short-Term Memory(LSTM), Gated Recurrent Unit(GRU)을 사용하였고, LSTM의 성능을 더욱 향상할 수 있는 Sequence-to-Sequence(s2s) 구조에 Attention 기법을 추가한 Attention Long Short-Term Memory (LSTM)기법을 사용하여 예측 성능 평가를 진행하 였다. 평가 결과 Attention LSTM 모델이 타 모델과 비교하여 더 좋은 성능을 보였으며, Hyper parameter 튜닝을 통해 해수면 수온 성능을 개 선할 수 있었다.

Aluminum alloy-based additive manufacturing (AM) has emerged as a popular manufacturing process for the fabrication of complex parts in the automotive and aerospace industries. The addition of an inoculant to aluminum alloy powder has been demonstrated to effectively reduce cracking by promoting the formation of equiaxed grains. However, the optimization of the AM process parameters remains challenging owing to their variability. In this study, the response surface methodology (RSM) was used to predict the crack density of AM-processed Al alloy samples. RSM was performed by setting the process parameters and equiaxed grain ratio, which influence crack propagation, as independent variables and designating crack density as a response variable. The RSM-based quadratic polynomial models for crack-density prediction were found to be highly accurate. The relationship among the process parameters, crack density, and equiaxed grain fraction was also investigated using RSM. The findings of this study highlight the efficacy of RSM as a reliable approach for optimizing the properties of AM-processed parts with limited experimental data. These results can contribute to the development of robust AM processing strategies for the fabrication of highquality Al alloy components for various applications.

In the event of a radioactive release, it is essential to quickly detect and locate the source of the release, as well as track the movement of the plume to assess the potential impact on public health and safety. Fixed monitoring posts are limited in their ability to provide a complete picture of the radiation distribution, and the information they provide may not be available in real-time. This is why other types of monitoring systems, such as mobile monitoring, aerial monitoring, and personal dosimeters, are also used in emergency situations to complement the information provided by fixed monitoring posts. Also, the monitoring system can be improved by using the Kriging technique, which is one of the interpolation methods, to predict the radiation dose in the relevant districts. This can be achieved by utilizing both the GPS information and the radiation dose measured at a particular point. The Kriging method involves estimating the value between different measurement points by considering the distance between them. The model used GPS and radiation data that were measured around the Hanbit NPP. The data were collected using a radiation measuring detector on a bus that traveled around the NPP area at 2-second intervals for one day. From the collected data, 200 data points were randomly selected for analysis, excluding the data measured at the bus garage out of a total of 16,550 data points. The average dose of the daily measurement data was 117.94 nSv/h, and the average dose of the 200 randomly extracted data was 119.17 nSv/h. The GPS and radiation dose data were utilized to predict the radiation dose around the Yeonggwang area where the Hanbit NPP is located. In the event of an abnormal release of radioactive material, it can be difficult to accurately determine the dose unless a monitoring measurement point is present. This can delay the rapid evacuation of residents during an emergency situation. By utilizing the Kriging model to make predictions, it is anticipated that more accurate dose predictions can be generated, particularly during accident scenarios. This can aid in the development of appropriate resident protection measures.

Commercial operation of KORI Unit 1 ended in 2017, and the final decommissioning plan is currently under approval from the KINS. In order for the dismantling waste to go to the repository, it is judged that the radioactive waste generated during the commercial operation should be treated and disposed in advance. Among these radioactive wastes, spent filters contain various radionuclides. The radiation dose rate from the radiation coming out of the filters ranges from a low dose rate to high dose rate. Therefore, in order to handle the spent filters, a remote processing system is required to reduce the radiation exposure of workers. This paper evaluates the radioactive inventory of filters that are stored in the filter room at the KORI unit #1. For this purpose, a method for predicting the radioactivity of each nuclide in the filter, based on the radiation dose rate, has been described using the MicroShield code, which is a commercial shielding code. The information on the filters in the field has only the creation date, type, size, and surface dose rate. In order to evaluate the radioactivity inventory using such limited data, it is possible to know the nuclide radioactivity ratio in the filter. We took out some of the filters stored on site and measured from using the ISCOS system, a gamma nuclide analyzer. The radioactivity of each nuclide in the filter was inferred by modeling with the MicroShield code, based on the radiation dose rate and the radioactivity value of each nuclide measured in the field.

고성능 콘크리트(HPC) 압축강도는 추가적인 시멘트질 재료의 사용으로 인해 예측하기 어렵고, 개선된 예측 모델의 개발이 필수적 이다. 따라서, 본 연구의 목적은 배깅과 스태킹을 결합한 앙상블 기법을 사용하여 HPC 압축강도 예측 모델을 개발하는 것이다. 이 논 문의 핵심적 기여는 기존 앙상블 기법인 배깅과 스태킹을 통합하여 새로운 앙상블 기법을 제시하고, 단일 기계학습 모델의 문제점을 해결하여 모델 예측 성능을 높이고자 한다. 단일 기계학습법으로 비선형 회귀분석, 서포트 벡터 머신, 인공신경망, 가우시안 프로세스 회귀를 사용하고, 앙상블 기법으로 배깅, 스태킹을 이용하였다. 결과적으로 본 연구에서 제안된 모델이 단일 기계학습 모델, 배깅 및 스태킹 모델보다 높은 정확도를 보였다. 이는 대표적인 4가지 성능 지표 비교를 통해 확인하였고, 제안된 방법의 유효성을 검증하였다.

본 논문은 FRCM 공법으로 보강된 철근콘크리트 보의 휨 성능을 예측하기 위한 해석적 연구결과를 제시한다. FRCM 공법으로 보강된 철근콘크리트 보의 휨 성능 예측을 위해 상용구조해석 프로그램인 LS-DYNA를 이용하여 유한요소해석이 수행되었다. 유한요소해석시 콘크리트와 모르타르는 Solid 요소로 모델링 되었으며, 철근과 FRP 그리드는 각각 Beam 요소 및 Shell 요소로 모델링되었다. 또한, 콘크리트와 철근은 완전부착하는 것으로 가정되었으며, 콘크리트와 모르타르 경계면의 부착파괴를 모사하기 위하여 Contact_Tiebreak_Surface_to_Surface 요소가 사용되었다. 이후, 국내⋅외 여러 연구자들에 의해 수행된 실험의 재현해석을 통해 제안된 유한요소해석 모델의 신뢰성이 검증되었다. 실험결과와 해석결과의 파괴양상을 분석하였을 때, 본 연구에서 제안된 유한요소해석 모델은 실험체의 부착파괴를 적절히 모사할 수 있는 것으로 나타났다. 또한, 해석을 통해 예측된 극한 강도에 대한 실험결과의 비는 평균 1.04, 표준편차 0.064로 제안된 해석모델은 FRCM 공법으로 보강된 철근콘크리트 보의 휨 성능을 비교적 잘 예측할 수 있는 것으로 나타났다.

The global trend is the application of heat-treated omission materials to reduce the manufacturing cost of automobile steering parts. Attempts have been made to apply heat-treated omission materials in domestic, but they are delayed due to concerns over rising cold forging process costs. For quantitative prediction of cold forging process cost, fatigue properties of forging die materials were evaluated. Based on this, the die life and cost were predicted quantitatively, and the manufacturing cost reduction of automobile steering parts using heat-treated material was found to be about 11%. Also, various methods to improve die life were additionally presented.

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.

본 논문에서는 해석결과를 보정하여 고층 건축물의 기둥축소 예측값과 실제값 간의 오차를 최소화하기 위한 해석보정법이 제안되었다. 이를 위하여 41층 규모의 철근 콘크리트 건물에 대한 시공단계해석이 수행되었으며, 해석결과는 기둥과 코어로 나뉘어 네 가지의 가정된 계측결과들과 비교되었다. 해석보정은 기둥에서는 오차 한계를 넘어서는 시공단계에서 코어에서는 모든 시공단계에 적용되었으며, 해석이 보정된 이후에도 지속적으로 오차가 발생하므로 해석보정이 자주 수행될수록 오차는 감소하였다. 이러한 과정을 통하여 제안된 해석보정 방법을 적용함으로써 장기적인 축소값이 실제값과 유사하게 예측될 수 있음을 확인하였다.

동해 연안지역의 고해상도 파랑예측을 위하여 통계적 규모축소화 방안을 적용하여 고해상도 동해 연안 파랑예측시스템을 구축하였다. 예측시스템을 구축하기 위하여 기상청 현업에서 예측된 동해 및 남해 연안파랑예측모델과 전구 파랑예측모델의 예측결과를 이용하였다. 3일까지는 연안파랑예측모델들의 결과를 그대로 활용하였고 3일 이후 7일까지는 전구파랑예측모델의 예측결과를 통계적 규모축소화 방안(역거리 가중 내삽방법과 조건부합성방법)을 적용하여 예측하였다. 예측된 고해상도 연안예측시스템을 이용하여 예측된 파고의 2차원 공간분포는 연안예측모델의 초기장(분석장)과 자기상관관계를 이용하여 검증하였고 부이 등 해양관측소 자료를 이용하여 파고 및 풍속 예측을 검증되었다. 수치모델의 예측성능과 유사하게 초기시간에는 예측성능이 높게 나타났으나 시간이 지남에 따라 예측성능이 점진적으로 감소되었다. 전체 기간의 파고 예측결과를 파고 관측자료를 이용하여 검증하였을 때 역거리 가중 내삽과 조건부합성방법 적용에 따른 상관계수와 평균 제곱근 오차는 0.46과 0.34 m에서 0.6과 0.28 m로 개선되었다.

Since 2010, old concrete pavement of expressway in Korea has approached the design life or exceeded it. As a result, the amount of maintenance for various types of distresses has also been increasing. In addition, there is increasing a demand for improvement in riding quality due to poor roughness. In this paper, when applying a diamond grinding method (DG) to improve the functionality of aged concrete pavement, a program to predict the 3D road surface profile was developed. Two field tests were conducted for program verification, and the comparison with the existing program and equipment was performed. As a result of the evaluation, the predicted result of the program was very similar to those actually measured using the existing roughness measuring equipment, and the error of the two results was about 5%. As a result of the roughness prediction using the 2D road surface profile measured before DG construction, it was similar to the existing program ProVAL. Using the data obtained by the 3D profile measuring equipment, it is confirmed that the prediction of how the IRI of the road will change in 3D is possible. In the future, through supplementary algorithms and economic analysis modules, it is possible to provide a tool for efficient budget execution of road maintenance agencies.

PURPOSES : The piezoelectric energy road analysis technology using a three-dimensional finite element method was developed to investigate pavement behaviors when piezoelectric energy harvesters and a new polyurethane surface layer were installed in field conditions. The main purpose of this study is to predict the long-term performance of the piezoelectric energy road through the proposed analytical steps. METHODS: To predict the stresses and strains of the piezoelectric energy road, the developed energy harvesters were embedded into the polyurethane surface layer (50 mm from the top surface). The typical type of triaxial dump truck loading was applied to the top of each energy harvester. In this paper, a general purpose finite element analysis program called ABAQUS was used and it was assumed that a harvester is installed in the cross section of a typical asphalt pavement structure. RESULTS : The maximum tensile stress of the polyurethane surface layer in the initial fatigue model occurred up to 0.035 MPa in the transverse direction when the truck tire load was loaded on the top of each harvester. The maximum tensile stresses were 0.025 MPa in the intermediate fatigue model and 0.013 MPa in the final fatigue model, which were 72% and 37% lower than that of the initial stage model, respectively. CONCLUSIONS : The main critical damage locations can be estimated between the base layer and the surface layer. If the crack propagates, bottom-up cracking from the base layer is the main cracking pattern where the tensile stress is higher than in other locations. It is also considered that the possibility of cracking in the top-down direction at the edge of energy harvester is more likely to occur because the material strength of the energy harvester is much higher and plays a role in the supporting points. In terms of long-term performance, all tensile stresses in the energy harvester and polyurethane layer are less than 1% of the maximum tensile strength and the possibility of fatigue damage was very low. Since the harvester is embedded in the surface layer of the polyurethane, which has higher tensile strength and toughness, it can assure a good, long-term performance.

The autoregressive method provides a univariate procedure to predict the future sunspot number (SSN) based on past record. The strength of this method lies in the possibility that from past data it yields the SSN in the future as a function of time. On the other hand, its major limitation comes from the intrinsic complexity of solar magnetic activity that may deviate from the linear stationary process assumption that is the basis of the autoregressive model. By analyzing the residual errors produced by the method, we have obtained the following conclusions: (1) the optimal duration of the past time for the forecast is found to be 8.5 years; (2) the standard error increases with prediction horizon and the errors are mostly systematic ones resulting from the incompleteness of the autoregressive model; (3) there is a tendency that the predicted value is underestimated in the activity rising phase, while it is overestimated in the declining phase; (5) the model prediction of a new Solar Cycle is fairly good when it is similar to the previous one, but is bad when the new cycle is much different from the previous one; (6) a reasonably good prediction of a new cycle can be made using the AR model 1.5 years after the start of the cycle. In addition, we predict the next cycle (Solar Cycle 25) will reach the peak in 2024 at the activity level similar to the current cycle.

OBJECTIVES : The objective of this research is to determine the integrity of pavement structures for areas where voids exist. Furthermore, we conducted the study of voided-area analysis and remaining life prediction for pavement structures using finite element method. METHODS : To determine the remaining life of the existing voided areas under asphalt concrete pavements, field and falling weight deflectometer (FWD) tests were conducted. Comparison methods were used to have better accuracy in the finite element method (FEM) analysis compared to the measured surface displacements due to the loaded trucks. In addition, the modeled FEM used in this study was compared with well-known software programs. RESULTS : The results show that a good agreement on the analyzed and measured displacements can be obtained through comparisons of the surface displacement due to loaded trucks. Furthermore, the modeled FEM program was compared with the available pavement-structure software programs, resulting in the same values of tensile strains in terms of the thickness of asphalt concrete layers. CONCLUSIONS: The study, which is related to voided-area analysis and remaining life prediction using FEM for pavement structures, was successfully conducted based on the comparison between our methods and the sinkhole grade used in Japan.