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.

PURPOSES : This study aimed to develop a quantitative structure property relationships (QSPR) model to predict the density from the molecular structure information of the asphalt binder AAA1, a non-full connected structure mixed with a total of 12 molecules. METHODS : The partial least squares regression (PLSR) model, which models the relationship between predictions and responses and the structure of these variables, was applied to predict the density of a binder with molecule descriptors. The PLSR model could also analyze data with collinear, noisy, and multiple dimensional independent variables. The density and additive-free AAA1 binder’s molecule systems generated by an asphalt binder’s molecules-related study were used to fit the PLSR model with the molecular descriptors produced using alvaDesc software. In addition to developing the relationship, a systematic feature selection framework (i.e., the V-WSP- and PLSR-modelbased genetic algorithm (GA)) was applied to explore sets of predictors which contributed to predicting the physical property. RESULTS : The PLSR model accurately predicted the density for the AAA1 binder’s molecules using the condition of the temperature and aging level (R2 was 0.9537, RMSE was 0.00424, and MAP was 0.00323 for the test data) and provided a set of features which correlated well to the property. CONCLUSIONS : Through the establishment of the physical property prediction model, it was possible to evaluate the physical properties of construction materials without limited experiments or simulations, and it could be used to comprehensively design the modified material composition.

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.

PURPOSES : This study aims to provide quantitative profile values for the objective evaluation of concrete surface profile (CSP) grades in concrete structures. The main aims are to quantify the CSP grade required for concrete surface pretreatment and proposing a more suitable CSP grade for structural maintenance. METHODS : Initially, the challenges in measuring concrete surface profiles were outlined by analyzing pretreatment work and profile samples of concrete pavements. Theoretical foundations for quantifying concrete surface roughness were established, and regression models including linear regression, cubic regression, and log regression were selected. Additionally, the interquartile range anomaly removal technique was employed to preprocess the data for regression modeling. RESULTS : Concrete CSP profiles were measured through indoor tests, and the measured data were quantified. Linear regression, cubic regression, and log regression models were applied to each CSP grade for comparative analysis of the results. Furthermore, comparative studies were conducted through adhesion strength tests based on the CSP grade. CONCLUSIONS : Our results are expected to establish objective standards for the pretreatment stage of concrete repair and reinforcement. The derived reference values can inform standards for the restoration and reinforcement of concrete structures, thereby contributing to performance improvement. Moreover, our results may serve as primary data for the repair and reinforcement of various concrete structures such as airports, bridges, highways, and buildings.

PURPOSES : Under the Traffic Safety Act, the installation and management of transportation facilities (facilities and attachments necessary for the operation of transportation, such as roads, railways, and terminals) must take necessary measures to ensure traffic safety, such as enhancing safety facilities. Recently, railway operators have graded the congestion level inside railway stations and vehicles, addressing safety and convenience issues arising from congestion and providing this information to users. However, for bus-related transportation facilities (such as bus stops, terminals, and transfer facilities), criteria and related research for assessing traffic congestion are lacking. Therefore, this study developed a model for the congestion risk factors of four bus-related transportation facilities and proposed criteria for classifying congestion risk levels. METHODS : This study involved selecting congestion risk influence variables for each traffic facility through field surveys, calculating congestion risk index values through evacuation and pedestrian simulations, and constructing a congestion risk influence model based on the ridge model. RESULTS : The factors influencing congestion were selected to include the number of people waiting, effective sidewalk width, and number of bus stops. As a result of developing congestion risk grades, the central bus stops were determined to be in a severe stage if the Average Waiting Time (AWT) was 2.7 or above. Roadside bus stops were considered severe at 4.2, underground metropolitan transit centers at 3.7, and bus terminals at 5.9 or above. CONCLUSIONS : This study can help establish a foundation for a safety management system for congested areas in transportation facilities. When the congestion risk prediction results correspond to cautionary or severe levels, measures that can reduce congestion risk must be applied to ensure the safety of road users.

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.

PURPOSES : This study aims to conduct a sensitivity analysis to determine the major factors affecting traffic accidents involving elderly pedestrians. METHODS : In this study, a regression tree model was built based on a non-parametric statistical model using data on traffic accidents involving elderly pedestrians. Using this model, we analyzed the degree of change in the probability of pedestrian fatalities. RESULTS : Results of the model analysis show that the first major factor combination affecting traffic accidents involving elderly pedestrians is speeding, night time, and road markers. The second combination is night time and arterial roads (national and local highways). The last combination that may lead to such accidents is heavy vehicles and federally funded local highways. CONCLUSIONS : Preventive measures, such as speed control, proper lighting, median strips, designation of pedestrian protection zones, and guidance of detours, are necessary to manage high-risk combinations causing accidents of the elderly.

This study attempts a comparison between AHP(Analytic Hierarchy Process) in which the importance weight is structured by individual subjective values and regression model with importance weight based on statistical theory in determining the importance weight of casual model. The casual model is designed by for students’ satisfaction with university, and SERVQUAL modeling methodology is applied to derive factors affecting students’ satisfaction with university. By comparison of importance weights for regression model and AHP, the following characteristics are observed. 1) the lower the degree of satisfaction of the factor, the higher the importance weight of AHP, 2) the importance weight of AHP has tendency to decrease as the standard deviation(or p-value) increases. degree of decreases. the second sampling is conducted to double-check the above observations. This study empirically checks that the importance weight of AHP has a relationship with the mean and standard deviation(or p-value) of independence variables, but can not reveal how exactly the relationship is. Further research is needed to clarify the relationship with long-term perspective.

PURPOSES : To efficiently manage pavements, a systematic pavement management system must be established based on regional characteristics. Suppose that the future conditions of a pavement section can be predicted based on data obtained at present. In this case, a more reasonable road maintenance strategy should be established. Hence, a prediction model of the annual surface distress (SD) change for national highway pavements in Gangwon-do, Korea is developed based on influencing factors. METHODS : To develop the model, pavement performance data and influencing factors were obtained. Exploratory data analysis was performed to analyze the data acquired, and the results show that the data were preprocessed. The variables used for model development were selected via correlation analysis, where variables such as surface distress, international roughness index, daily temperature range, and heat wave days were used. Best subset regression was performed, where the candidate model was selected from all possible subsets based on certain criteria. The final model was selected based on an algorithm developed for rational model selection. The sensitivity of the annual SD change was analyzed based on the variables of the final model. RESULTS : The result of the sensitivity analysis shows that the annual SD change is affected by the variables in the following order: surface distress ˃ heat wave days ˃ daily temperature range ˃ international roughness index. CONCLUSIONS : An annual SD change prediction model is developed by considering the present performance, traffic volume, and climatic conditions. The model can facilitate the establishment of a reasonable road maintenance strategy. The prediction accuracy can be improved by obtaining additional data, such as the construction quality, material properties, and pavement thickness.

일반적으로 콘크리트는 골재, 모래, 시멘트, 담수, 혼합재 등 다양한 재료로 구성되어있으며 재령에 따라서 강도가 증 가한다. 콘크리트에 필요한 각 재료의 비율은 혼합 설계를 통해 결정되지만, 콘크리트의 강도는 실험적으로 측정되기 전까지는 알 수 없다. 이러한 한계를 극복하기 위해 실험을 통해 얻은 데이터를 이용하여 콘크리트의 압축 강도를 예측하기 위해 통계수 학과 기계학습 알고리즘을 이용한 많은 연구가 시도되었다. 이전의 연구는 콘크리트 압축 강도 예측에 신경망 기법이 가장 적 합하다고 제안하였다. 그러나 신경망 기법은 다른 기계학습과 비교하여 모델 학습에 계산 비용이 많이 들어 실제로 적용하기 어려운 문제점이 있다. 최근 몇 년 동안 다양한 회귀 분석 모델이 개발되었으므로 본 연구에서는 신경망 대신 최신 회귀 분석 모델을 이용하여 콘크리트 강도 예측모델을 제시하였다. 이를 위해 최근 개발된 회귀 분석 모델에 대한 교차검증을 시행하여 최적의 모델을 선정하였다. 그리드 검색을 통하여 선정된 각 모델의 하이퍼 파라미터를 최적화하고, 국내외 데이터를 활용하여 기계학습 모델을 훈련하고 검증하였다. 이들 중 CatBoost, LGBMR, RFR, XGBoost 회귀모델이 높은 성능을 보여주었다. 특히 그 중에서 XGBoost 회귀 분석 모델이 가장 작은 오차와 높은 정확도를 보여주었다. 이들 중 오류가 가장 큰 LGBMR 모델도 이전 연구에서 제안된 신경망 및 앙상블 모델보다 성능이 우수하였다. 현장 레미콘 콘크리트에 대한 압축 강도 예측을 시행하여 학 습된 모델의 현장 적용 가능성을 확인하였다.

Identifying the water circulation status is one of the indispensable processes for watershed management in an urban area. Recently, various water circulation models have been developed to simulate the water circulation, but it takes a lot of time and cost to make a water circulation model that could adapt the characteristics of the watershed. This paper aims to develop a water circulation state estimation model that could easily calculate the status of water circulation in an urban watershed by using multiple linear regression analysis. The study watershed is a watershed in Seoul that applied the impermeable area ratio in 1962 and 2000. And, It was divided into 73 watersheds in order to consider changes in water circulation status according to the urban characteristic factors. The input data of the SHER(Similar Hydrologic Element Response) model, a water circulation model, were used as data for the urban characteristic factors of each watershed. A total of seven factors were considered as urban characteristic factors. Those factors included annual precipitation, watershed area, average land-surface slope, impervious surface ratio, coefficient of saturated permeability, hydraulic gradient of groundwater surface, and length of contact line with downstream block. With significance probabilities (or p-values) of 0.05 and below, all five models showed significant results in estimating the water circulation status such as the surface runoff rate and the evapotranspiration rate. The model that was applied all seven urban characteristics factors, can calculate the most similar results such as the existing water circulation model. The water circulation estimation model developed in this study is not only useful to simply estimate the water circulation status of ungauged watersheds but can also provide data for parameter calibration and validation.

본 연구는 대륜 국화 ‘백마’의 생육 특성인 생체중, 건물중, 엽면적을 조사하여, 생장 및 기후요소에 따른 생장 예측모델 개발을 위하여 수행되었다. 정식후 일수 및 누적온도에 따른 국화의 건물중 및 엽면적 분석에 기반한 ‘백마’의 생장예측을 위한 시그모이드 회귀모델을 개발하였다. ‘백마’의 건물중 상대 생장률(RGR)은 재배기간 평균 0.084 g·g-1·d-1이었다. 정식 후 재배 기간에 따른 건물중에 대한 상대 생장률은 정식 초기부터 단일처리 전까지 높았으며 최고 0.133 g·g-1·d-1까지 증가하였고, 63일째 단일처리가 시작된 후 수확 시기에서는 0.030 g·g-1·d-1으로 감소하는 경향을 보였다. 누적온도에 따른 국화의 건물중, 엽면적에 대한 생장 모델(sigmoid 곡선)을 개발하였다. 정식 후 일수와 누적온도에 따른 ‘백마’의 건물중 및 엽면적은 지수함수적으로 증가하였으며, 건물중의 경우 63일(누적온도 1601℃)까지 평균 39.1%씩 증가하였고, 이후 평균 7.4%씩 증가하였다. 엽면적의 경우 정식 후 28일차 까지 평균 63.3%씩 증가하였고, 화아분화가 발생하기 전인 84일차까지 평균 6.5%씩 증가하였으며 화아 분화가 발생하 기 전 84일까지 평균 6.5%로 증가했고, 이후 수확 전까지 평균 10.6%씩 증가하는 경향을 보였다. 본 실험은 충남지역에 서 대륜 국화 ‘백마’의 재배관리 체계와 계획적 연중 생산 체계를 구축하는데 유용한 자료로 활용될 수 있다. 보다 정밀한 생육 예측 모델을 만들기 위해서는 누적 일사량을 포함한 다양한 기상자료를 바탕으로 하여 교정 및 검증이 필요하다.

This study examined the spatial autocorrelation of the 2016 foot and mouth disease (FMD) outbreaks in South Chungcheong to determine the association between the disease epidemics and pig farm vehicle movements. Two spatial autocorrelation testing methods were used: Moran’s I and Getis-Ord G statistics. The Moran’s I statistic for the FMD outbreak areas was -0.239, and its p-value was less than 0.007. The median Getis-Ord G statistic for the FMD outbreak areas was -0.323. The results indicated that the geographical distribution of the FMD outbreak areas was not spatially homogeneous. The spatial autocorrelation of the 2016 FMD epidemics was considered by applying a geographical weighted Poisson regression (GWPR) model in the analysis, in which pig farm vehicle movements were used as risk factors for the 2016 FMD epidemics. The number of FMD-infected farms per second-level administrative province (si or gun) was used as a dependent variable. The number of farm vehicle movements within the province (within variable), from one province to other provinces (outbound variable), or from other provinces to one province (inbound variable), were included as independent variables in the GWPR model. The results of the GWPR model were as follows. The estimated median coefficient of the log-transformed within variable, the log-transformed outbound variable, and log-transformed inbound variable were -0.000, 0.010, and -0.009, respectively. The optimal bandwidth for the GWPR model was 80.49, and the AIC score was 89.35. The results showed that the GWPR model would provide an understanding of the relationship between the 2016 FMD epidemics and pig farm vehicle movements

본 논문에서는 고리 1호기 해체 비용 추정을 위해 외국 원자력발전소 해체 비용 데이터를 현가화한 후 원자력발전소 해체 비용 추정 회귀 분석모델을 개발하였다. 이 모델 개발에 사용된 데이터는 해체 또는 진행 중인 BWR 13기, PWR 16기의 해체 비용 데이터이다. 회귀 분석모델 도출을 위해, 해체 비용을 종속변수로 정하고, 해체 원전의 운전 특성을 반영할 수 있게 고 안된 Contamination factor와 해체 기간을 독립변수로 선정하였다. 빅데이터 분석 도구인 R language의 통계패키지를 이용 하여 회귀 분석모델을 도출하였다. 이 회귀 분석 모델을 적용하여 고리 1호기 해체 비용을 예측한 결과, 미화 663.40~928.32 백만 달러, 한화 약 7,828.12억~1조 954.18억 원이 소요될 것으로 예측되었다.

본 연구는 3개의 운전변수(압력, 공기량, 운전시간)를 실험 설계하고 마이크로 버블의 종말부상 속도(Terminal rise velocity)를 반응 값으로 하여 예측식 모델과 최적 조건을 수립하였다. 다항식 회귀분석을 통해 펌프의 압력(X1) 4.5bar, 공기량(X2) 3.3L/min 그리고 운전시간(X3)이 2.2min에서 종말상승속도(Terminal rise velocity)에 대한 최적값인 5.14 cm/min (85.7㎛/sec)을 얻었다. 또한, 레이저 입자계수 측정장치를 이용하여 2~5㎛ 및 25~50㎛ 영역에서의 가장 높은 마이크로버블 직경크기 분포를 확인하였다.

The airport concrete pavement is gradually changing from the empirical design method proposed by the FAA to the mechanical design method. However, the complete mechanical design method has not yet been developed because it is difficult to consider designing due to various break types due to environmental load. FAAFIELD, a widely used finite element analysis program in existing airport pavement design, has yet to be considered for environment loads, although it has been continuously updated. Therefore, this study used ABAQUS instead of FAARFIELD as a finite element analysis program to consider the environmental load, and carried out the load quantification work of A380 aircraft and B777 aircraft corresponding to 3DT(3 Dual Tandem) gear. The traffic load information of 3DT gear aircraft was collected, and the environmental load due to temperature and humidity was converted into the equivalent linear temperature difference. Through the finite element analysis using ABAQUS, the prediction data of maximum tensile stress and location was collected when the two loads act to slab simultaneously. The factors affecting the maximum tensile stress in the finite element analysis are slab thickness, joint spacing, aircraft load, combined bearing capacity, and equivalent linear temperature difference, respectively. In order to examine the adequacy of the selected factors, sensitivity analysis for factors which affect maximum tensile stress was performed, and the stress regression model was developed. For this, a multiple regression analysis program SPSS was used and the stress regression equations for 3DT gear aircraft were calculated considering traffic and environmental loads. Through the developed stress regression model, it is possible perform the load quantification process of the 3DT gear aircraft for the mechanical design of the airport concrete pavement. In addition, the appropriateness of the regression model is verified by securing the high decision coefficient through SPSS. This study was supported by Incheon International Airport Corporation(BEX00625)

Conventional airport concrete pavement design uses empirical design method presented by FAA but it is not accurate because it does not consider environment load. In case of mechanistic - empirical design method used overseas, it needs to be modified according to domestic conditions. In this paper, a stress regression model considering environmental load and dual tandem gear load is developed as a mechanical - empirical design process and verified by fatigue model calibration. First of all, literature review was conducted on airports using DT gear as a design aircraft among domestic airports, and the physical properties of concrete pavement layers of each airport were identified. In addition, the environmental load is divided into the temperature load and the moisture load, and the temperature load is calculated from the climatic data of the region where the domestic airport is located, and the moisture load is calculated through the drying shrinkage prediction model developed through the previous study. The stresses occurring in concrete slabs under environmental loads and traffic loads were predicted using FEAFAA, which is a finite element analysis program, and factors predicted to affect concrete pavement were selected for the stress regression.As a result of the sensitivity analysis of the selected factors, the joint spacing, slab thickness, gear load, and road bearing capacity coefficient affected the stress behavior of the slab by more than 5% of the reference stress.According to the mechanical design, the finite element analysis was carried out for the consideration of only the traffic load and the environmental load and the traffic load based on the design factors. Based on the analysis results, multiple regression analysis was performed using the statistical analysis program SPSS and the stress regression equation was calculated. Then, the stress model for the environmental load and the traffic load was calculated and the final stress model with each stress model as the independent variable was derived. Using the calculated stress regression model, the stress was calculated and the bending strength of the concrete was divided to calculate the stress - to - strength ratio, and the appropriate fatigue model was selected and the correction was performed by the least squares method. This study was supported by Incheon International Airport Corporation(BEX00625)