This study aims to estimate the scope of damage impact with a real-life explosion case and a damage prediction program (ALOHA) and suggest measures to reduce risk by comparing and analyzing the results using a Probit model. After applying it to the ALOHA program, the toxicity, overpressure, and radiant heat damage of 5 tons of storage scopes between 66 to 413 meters, and the real-life case also demonstrated that most of the damage took place within 300 meters of the LPG gas station. In the Probit analysis, the damages due to radiant heat were estimated as first-degree burns (13-50%), while structural damage (0-75%) and glass window breakage (94-100%) were expected from overpressure, depending on the storage volume. After comparing the real-life case and the damage prediction program, this study concluded that the ALOHA program could be used as the scope of damage impacts is nearly the same as the actual case; it also concluded that the analysis using the Probit model could reduce risks by applying calculated results and predicting the probability of human casualties and structural damages.

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.

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.

이 연구는 1세대 스마트 온실의 재배환경 데이터와 장미 절 화의 품질 특성 데이터를 수집하고 그 요인들 간의 상관 관계 를 분석하여 절화수명 예측 및 최적 환경 조성의 기초 자료를 얻고자 수행되었다. 이를 위해, 토경재배(SC) 및 암면배지경 양액재배(RWH) 하우스 각 1개소를 선정하여 1년간 기온, 상 대습도(RH) 및 수증기압차(VPD), 일적산광량(DLI), 근권온도 등의 환경 데이터와 매월 말 수확된 장미 ‘Miss Holland’ 절 화의 품질 특성 데이터를 수집하였으며, 이 데이터와 절화수 명과의 상관관계를 분석하였다. 절화수명은 10월과 11월을 제외하고는 SC 하우스에서 RWH 하우스보다 더 길었다. 절 화수명과 환경 및 생육 특성 간의 상관관계 분석에서 SC 하우 스의 상관계수는 RWH 하우스보다 조금 더 높았으며, 절화수 명 예측을 위한 요소들도 두 하우스 간에 차이가 있었다. SC 하우스의 절화수명 Y=0.848X1+0.366X2-0.591X3+2.224X4- 0.171X5+0.47X6+0.321X7+9.836X8-110.219(X1-X8: 최고 RH, RH 일교차, DLI, pH, Hunter’s b value, EC, 절화 장, 잎 두께; R2=0.544)로 예측되었고, RWH 하우스의 절화수명 Y=-1.291X1+52.026X2-0.094X3+0.448X4-3.84X5+0.624X6 - 8.528X7+28.45(X1-X7: 경경, 야간 VPD, 최고 근권온도, 최 저 근권온도, 기온 일교차, RH 일교차, 최고 VPD; R2=0.5243) 로 예측되었다. 이 두 모델식으로부터 SC 하우스에서는 RH, EC 및 pH가, 그리고 RWH 하우스에서는 근권 온도가 절화수명에 더 큰 영향을 미친다는 것을 추론할 수 있다. 따라서 각 재배 방법에 따라 장미의 절화수명에 더 큰 영향을 미치는 환경적 요인을 효율적으로 관리할 필요가 있다.

The high-level nuclear waste (HLW) repository is a 500-1,000 m deep underground structure to dispose high-level nuclear waste. The waste has a very long half-time and is exposed to a number of stresses, including high temperatures, high humidity, high pressure These stresses cause the structure to deteriorate and create cracks. Therefore, structural health monitoring with monitoring sensors is required for safety. However, sensors could also fail due to the stresses, especially high temperature. Given that the sensors are installed in the bentonite buffer and the backfill tunnel, it is impossible to replace them if they fail. That’s why it is necessary to assess the sensors’ durability under the repository’s environmental conditions before installing them. Accelerated life test (ALT) can be used to assess durability or life of the sensors, and it is important to obtain the same failure mode for reliability tests including ALT. Before conducting the test, the proper stress level must be designed first to get reliable data in a short time. After that, acceleration of life reduction with increasing temperature and temperature-life model should be determined with some statistical methods. In this study, a methodology for designing stress levels and predicting the life of the sensor were described.

PURPOSES : With the recent enactment of the 「Framework Act on Sustainable Infrastructure Management」 in Korea, the establishment of mid- to long-term management plans for social infrastructure and the feasibility evaluation of maintenance projects have become mandatory. To this end, the life cycle cost analysis is essential. However, owing to the absence of a deterioration model, trials and errors are in progress. METHODS : In this study, a deterioration model was established for bridges, which are the representative social infrastructures of roads, particularly for expansion joints that can cause enormous damage to not only the superstructure but also the substructure. The deterioration model was classified into rubber and steel, based on the material of the expansion joint. The analysis used the inspection and climate data conducted in Korea over the last 12 years. The Bayesian Markov Hazard model was applied as the analysis technique. RESULTS : The average life expectancy by type of expansion joint was analyzed to be 8.9 and 6.6 years for rubber and steel, respectively. For probabilistic life cycle cost analysis, the probability distribution of the life expectancy, validity range by confidence level, and Markov transition probability matrix were presented. CONCLUSIONS : In this study, the basis for deterministic and probabilistic life cycle cost analysis of expansion joints was laid. In future studies, it will be necessary to establish a standardized deterioration model for all types of infrastructure, including all bridge elements.

Predicting remaining useful life (RUL) becomes significant to implement prognostics and health management of industrial systems. The relevant studies have contributed to creating RUL prediction models and validating their acceptable performance; however, they are confined to drive reasonable preventive maintenance strategies derived from and connected with such predictive models. This paper proposes a data-driven preventive maintenance method that predicts RUL of industrial systems and determines the optimal replacement time intervals to lead to cost minimization in preventive maintenance. The proposed method comprises: (1) generating RUL prediction models through learning historical process data by using machine learning techniques including random forest and extreme gradient boosting, and (2) applying the system failure time derived from the RUL prediction models to the Weibull distribution-based minimum-repair block replacement model for finding the cost-optimal block replacement time. The paper includes a case study to demonstrate the feasibility of the proposed method using an open dataset, wherein sensor data are generated and recorded from turbofan engine systems.

Most of automobile steering parts are manufactured through the multi-stage cold forging process using round-bar drawn materials. The same process is applied to the ball stud parts of the outer ball joint, and various research activities are being carried out to reduce the extreme manufacturing cost in order to survive in the limitless competition. In this paper, we present a quantitative prediction method for the limiting life of the die as a method for cost reduction in the multi-stage cold forging process. The load on the die was minimized by distributing the forming load based on process optimization through finite element analysis. In addition, based on the quantitative prediction algorithm of the limiting life of the die, the application of the split die and the optimization of the phosphate treatment of the material surface are presented as a conclusion as a method to improve the limiting life of the die.

Recently, the importance of preventive maintenance has been emerging since failures in a complex system are automatically detected due to the development of artificial intelligence techniques and sensor technology. Therefore, prognostic and health management (PHM) is being actively studied, and prediction of the remaining useful life (RUL) of the system is being one of the most important tasks. A lot of researches has been conducted to predict the RUL. Deep learning models have been developed to improve prediction performance, but studies on identifying the importance of features are not carried out. It is very meaningful to extract and interpret features that affect failures while improving the predictive accuracy of RUL is important. In this paper, a total of six popular deep learning models were employed to predict the RUL, and identified important variables for each model through SHAP (Shapley Additive explanations) that one of the explainable artificial intelligence (XAI). Moreover, the fluctuations and trends of prediction performance according to the number of variables were identified. This paper can suggest the possibility of explainability of various deep learning models, and the application of XAI can be demonstrated. Also, through this proposed method, it is expected that the possibility of utilizing SHAP as a feature selection method.

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.

PURPOSES : The study aims to predict the service life of national highway asphalt pavements through deep learning methods by using maintenance history data of the National Highway Pavement Management System. METHODS: For the configuration of a deep learning network, this study used Tensorflow 1.5, an open source program which has excellent usability among deep learning frameworks. For the analysis, nine variables of cumulative annual average daily traffic, cumulative equivalent single axle loads, maintenance layer, surface, base, subbase, anti-frost layer, structural number of pavement, and region were selected as input data, while service life was chosen to construct the input layer and output layers as output data. Additionally, for scenario analysis, in this study, a model was formed with four different numbers of 1, 2, 4, and 8 hidden layers and a simulation analysis was performed according to the applicability of the over fitting resolution algorithm. RESULTS: The results of the analysis have shown that regardless of the number of hidden layers, when an over fitting resolution algorithm, such as dropout, is applied, the prediction capability is improved as the coefficient of determination (R2) of the test data increases. Furthermore, the result of the sensitivity analysis of the applicability of region variables demonstrates that estimating service life requires sufficient consideration of regional characteristics as R2 had a maximum of between 0.73 and 0.84, when regional variables where taken into consideration. CONCLUSIONS : As a result, this study proposes that it is possible to precisely predict the service life of national highway pavement sections with the consideration of traffic, pavement thickness, and regional factors and concludes that the use of the prediction of service life is fundamental data in decision making within pavement management systems.

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.

This study was carried out to indirectly predict the storage time limit, hardness, and acidity of Fuji apples in controlled atmosphere (CA) storage. A sensor installed inside the CA storage measured temperature, relative humidity, and gas composition data in real time. The respiration rate from five tons of apples in CA storage was calculated to predict the weight loss rate. As a result, the predicted and actual weight loss rate induced a predictable residual storage time equation that showed a significantly high correlation. The apple storage period showed a high reliability (R2=0.9322) because the predicted equation using respiration rate and number of days stored was about nine months for five tons of apples. Furthermore, the hardness and acidity prediction equation were derived from the quality analysis. However, there was not enough analysis sample correlation (the coefficient was as low as 0.3506 and 0.3144, respectively), but the tendency could be confirmed by reduced hardness and acidity. As a result, these quality prediction equations could encourage CA container distribution, effective for agricultural shipment regulation and increasing the ease of operations.

본 연구에서는 월성 중₩저준위방사성폐기물 처분시설의 내구성 및 한계수명을 예측하였다. 처분시설은 6개의 사일로로 구 성되어 있으며 지하수 포화대에 위치하고 있어 주변 지하수와 화학적 침식 등에 의한 열화에 노출되어 있으며, 장시간이 흐 르면 수리적 방벽으로서의 역할을 상실할 것으로 예상된다. 각각의 인자에 대한 열화시간을 평가한 결과 황산염 및 마그네 슘에 의한 콘크리트 열화속도는 1.308×10-3 cm/yr로 48,000 년 이상인 것으로 나타났으며, 수산화칼슘 침출에 의한 영향은 1,000 년의 기간 경과에서 수산화칼슘 유출 깊이는 1.5 cm이하로 상당히 오랜 시간이 소요되는 것으로 나타났다. 마지막으 로 염해에 의한 철근 부식의 경우 철근 부식개시기간이 1,648 년으로, 최종적으로 구조물이 한계수명 상태에 도달하는 시간 은 2,288 년인 것으로 예측되어 가장 민감한 인자로 평가되었다.

Domestic 105㎜ HE (High Explosive) shell is composed of three parts that are Fuze, Projectile and Propellants. Among three parts, propelling charge of propellants part consists of single base propellants. It has been known that the lifespan of single base propellants is affected by a storage period. These are because Nitrocellulose (NC) which is the main component of propelling gunpowder can be naturally decomposed to unstable substances similar with other nitric acid ester. Even though it cannot be prevented fundamentally from being disassembled, a decomposition product (NO2, NO3, and HNO3) and tranquillizer DPA (Diphenylamine) having high reactivity are added into a propellant to restrain induction of automatic catalysis by a decomposition product. The decay rate of the tranquillizer is also affected by a production rate of the decomposition product of NC. Therefore, an accurate prediction of the Self-Life is required to ensure against risks such as explosion. Hereupon, this paper presents a new methodology to estimate the shelf-life of single base propellants using data of ASRP (Ammunition Stockpile Reliability Program) to domestic 105mm HE (propelling charge of propellants part). We selected four attributes that are inferred to have influence on distribution of the DPA amount in a propellant from the ASRP dataset through data mining processes. Then the selected attributes were used as independent variables in a regression analysis in order to estimate the shelf-life of single base propellants. 1)

Domestic 105㎜ HE (High Explosive) shell is divided into three parts (Fuze, Projectile and Propellants). Among three parts, propelling charge of propellants part consists of single base propellants. The lifespan of single base propellants is affected by a storage period. These are because Nitrocellulose (NC) which is the main component of propelling gunpowder can be naturally decomposed to unstable substances similar with other nitric acid ester. Even though it cannot be prevented fundamentally from being disassembled, a decomposition product (NO2, NO3, and HNO3) and tranquillizer DPA (Diphenylamine) having high reactivity are added into a propellant to restrain induction of automatic catalysis by a decomposition product. The decay rate of the tranquillizer is also affected by a production rate of the decomposition product of NC. Therefore, an accurate prediction of the Self-Life is required so that it is ensure against risks. Hereupon, this paper presents a new methodology to estimate the shelf-life of single base propellants using data of ASRP (Ammunition Stockpile Reliability Program) to domestic 105mm HE (propelling charge of propellants part). We selected four attributes that is inferred to have influence on distribution of the DPA amount in a propellant from the ASRP dataset through data mining processes. Then the selected attributes were used as independent variables in a regression analysis in order to estimate the shelf-life of single base propellants.