PURPOSES : This study aimed to predict the number of future COVID-19 confirmed cases more accurately using public and transportation big data and suggested priorities for introducing major policies by region. METHODS : Prediction analysis was performed using a long short-term memory (LSTM) model with excellent prediction accuracy for time-series data. Random forest (RF) classification analysis was used to derive regional priorities and major influencing factors. RESULTS : Based on the daily number of COVID-19 confirmed cases from January 26 to December 12, 2020, as well as the daily number of confirmed cases in Gyeonggi Province, which was expected to occur on December 24 and 25, depending on social distancing, the accuracy of the LSTM artificial neural network was approximately 95.8%. In addition, as a result of deriving the major influencing factors of COVID-19 through random forest classification analysis, according to the number of people, social distancing stages, and masks worn, Bucheon, Yongin, and Pyeongtaek were identified as regions expected to be at high risk in the future. CONCLUSIONS : The results of this study can help predict pandemics such as COVID-19.

본 연구에서는 구조물의 재료, 구조물의 단면, 지진 하중등의 불확실성을 고려한 저형 전단벽의 최대 전단력를 예측하는 뉴 런-네트워크 모델을 개발하였다. 이를 위해 실험 데이터를 통해 검증된 박스타입 저형 전단벽 수치해석 모델을 구축하였고, 가정된 분 포를 통해 200개의 구조물의 재료, 단면변수를 라틴 하이퍼 큐브 샘플링을 통해 추출하였다. 또한 이전 연구에서 사용된 인공지진파를 데이터를 기반으로 10개의 다른 PGA 레벨별 총 200개의 인공지진파 데이터를 구축하였다. 뉴런-네트워크 모델의 Training 및 testing을 위해 200개의 데이터셋에 상응 수치해석 모델을 구축하고 최대 전단력을 산출하였다. 이렇게 구축된 데이터셋을 이용하여 최종적으로 뉴런-네트워크 모델을 확정하였다. 마지막으로 구축된 모델로부터 얻어진 취약도와 기존에 사용되는 방법들로부터 얻은 취약도를 비교, 분석하여 본 연구에서 구축된 모델의 정확도를 보여주었다.

PURPOSES : High temperatures induce excessive expansion in pavements, thus causing the closure of contraction joints between expansion joints. This results in the integration of slabs within the expansion joints into a unified slab. Compressive forces are generated owing to the friction that ensues between the unified slab and lower base layer. As the integrated slab expands and exceeds the allowable width of the expansion joint, the end restraint generates an additional compressive force. The escalating force, which reaches a critical threshold, induces buckling, thus compromising stability and causing blow-up incidents, which poses a significant hazard to road users. The unpredictable nature of blow-up incidents render their accurate prediction challenging because the compressive force within the slab must be predicted and the threshold for blow-up occurrence must be determined. METHODS : In this study, a GWNU blow-up model was developed to predict both the compressive force and period of blow-up incidents in jointed concrete pavements. The climate conditions, pavement structure, materials, and expansion joints were considered in this model. In the first stage of the model, the time at which the integrated slab expanded and surpassed the allowable width of the expansion joint was determined, and the compressive force was calculated. Subsequently, the compressive force within the integrated slab, considering both the end restraints and friction, was predicted. A large-scale blow-up test was performed to measure the blow-up force based on changes in the geometric imperfections. The measured blow-up force was adopted as the blow-up occurrence threshold, and the point at which the predicted compressive force within the slab exceeded the blow-up force was identified as the blow-up occurrence time. RESULTS : Using the GWNU blow-up model, the blow-up occurrence on the Seohean Expressway in Korea is predicted in the presence or absence of the alkali-silica reaction (ASR). Analysis is conducted using the expansion joint spacing and width as variables. As the expansion joint spacing increases, blow-up occurs sooner, and as the width increases, only the expansion joint life decreases. When applying an expansion joint spacing of 300 m and a width of 100 mm under an ASR with 99.9% TTPG reliability, the sum of the expansion joint life and blow-up occurrence time is 16 years. CONCLUSIONS : In the case of jointed concrete pavements where ASR occurred, installing an expansion joint spacing of 300 m and a width of 100 mm does not satisfy the design life of 20 years, and the expansion joint width minimally affect the blow-up occurrence time. To prevent blow-up incidents, a spacing of less than 300 m for the expansion joint is recommended. Based on the analysis results, the blow-up occurrence time and location can be predicted from the characteristics of the installed expansion joint, through which blow-up incidents can be prevented via preliminary maintenance.

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.

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.

In order to prevent accidents via defective ammunition, this paper analyzes recent research on ammunition life prediction methodology. This workanalyzes current shelf-life prediction approaches by comparing the pros and cons of physical modeling, accelerated testing, and statistical analysis-based prediction techniques. Physical modeling-based prediction demonstrates its usefulness in understanding the physical properties and interactions of ammunition. Accelerated testing-based prediction is useful in quickly verifying the reliability and safety of ammunition. Additionally, statistical analysis-based prediction is emphasized for its ability to make decisions based on data. This paper aims to contribute to the early detection of defective ammunition by analyzing ammunition life prediction methodology hereby reducing defective ammunition accidents. In order to prepare not only Korean domestic war situation but also the international affairs from Eastern Europe and Mid East countries, it is very important to enhance the stability of organizations using ammunition and reduce costs of potential accidents.

콘크리트 포장의 조기 파손을 초래하는 콘크리트 혼합물의 품질 저하는 최근 종종 발생되고 있다. 이로 인한 유지보수 비용 또한 증 가하는 추세이다. 본 연구는 이러한 문제를 해결하고자 콘크리트 배합 시 효과적으로 유변학적 특성을 측정하여 콘크리트 품질을 예 측할 수 있는 시제품 개발을 연구 중이다. 현재 상용화되어 사용되고 있는 ICAR Plus Rheometer 장비의 이론을 변경 적용하여, 본 시제품 Twin Shaft Rheometer mixer를 개발하였다. 동시에 레오미터 장비를 활용해 유변학적 특성을 확인하고 측정하였다. 콘크리트 의 변형과 움직임을 분석하기 위해 수직, 수평 거동의 비교분석을 진행하였고, 흐름 저항성과 토크 점성을 이용하여 유변학적 특성을 기존 장비와 비교 분석하였다. 그 결과 절댓값의 차이는 존재하나 선형적 유사성을 가지는 것을 알 수 있었다. 높은 정확성을 위해 추 가연구는 진행하고 있다. 추가로 슬럼프 측정 센서 또한 개발 진행 중이며, 이 장비는 마이크로파를 통해 매질의 변화를 측정하여 슬 럼프를 유추하는 센서로 더욱 정밀한 결과값을 위해 추가연구 진행하고 있다.

Since the decrease of skid resistance of the road surface due to the effects of hydroplaning increases the ratio of vehicle crashes significantly, it is important to predict water film thickness (WFT). Tined is one of the widely used textures for concrete pavements. Since previous WFT models have been developed based on the asphalt pavement texture and broom concrete, it may not give reliable predictions for Water film thickness for tinned concrete. Furthermore, surface flow on tined texture may show hydraulically different characteristics due to the geometric characteristics of tined texture. This study aims to propose a reliable water film thickness prediction model for tined concrete. Three test slabs including a smooth surface, a tined surface with 16mm spacing, and a tined surface with 25mm spacing were prepared. WFTs of the test slab were measured for various conditions such as pavement slope (0-10%), rainfall intensity (0-130mm/h), and drainage path length (0-5m). A statistical model was proposed to predict water film thickness (WFT) as a function of pavement slope, rainfall intensity, drainage path length, and mean texture depth. This model exhibits strong agreement with the experimental test results. The GWNU prediction model consistently provides reliable predictions with the actual WFT for tined concrete pavement. Conversely, the previous equation consistently underestimated the water film thickness, notably on tined surfaces with 16 mm and 25 mm spacing, due to the occurrence of viscous flow along the tined lines.