• International Journal of Highway Engineering
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• v.4 no.2 s.12
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• pp.9-18
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• 2002

The main distresses that influence pavement performance are rutting, fatigue cracking, and longitudinal roughness. Thus, it is important to analyze the factors that affect these three distresses, and to develop prediction models. In this paper, three distress prediction models were developed using DataPave program which stores data from a wide variety of pavement sections In the United States. Also, sensitivity studies were conducted to evaluate how the input variables impact on the distresses. The result of sensitivity study for the prediction model of rutting showed that asphalt content, air void, and optimum moisture content of subgrade were the major factors that affect rutting. The output of sensitivity study for the prediction model of fatigue cracking revealed that asphalt consistency, asphalt content, and air void were the most influential variables. The prediction model of longitudinal roughness indicated asphalt consistency, #200 passing percent of subgrade aggregate, and asphalt content were the factors that affect longitudinal roughness.

• Korean Journal of Materials Research
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• v.3 no.2
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• pp.197-204
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• 1993

Abstract The effects of the hole size and the specimen width on the fracture behavior of several fabric composite plates are experimentally investigated in tension. Tests are performed on plain woven glass/ epoxy, plain woven carbon/epoxy and satin woven glass/polyester specimens with a circular hole. It is shown in this paper that the characteristic length according to the point stress criterion depends on the hole size and the specimen width. An excellent agreement is found between the experimental results and the analytical predictions of the modified failure criterion. The notched strength increase with an increase in the damage ratio, which is explained by a stress relaxation due to the formation of damage zone. When the unstable fracture occurred, the critical crack length equivalent for the damage zone is about twice the characteristic length. The critical energy release rate $G_c$ is independent of hole size for the same specimen width. The variation of $G_c$ according to the material system, fiber volume fraction and specimen width relates to the notch sensitivity factor. $G_c$ increases with a decrease in the notch sensitivity factor, which can be explained by a stress relaxation due to the increase of damage zone.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.15 no.3
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• pp.77-81
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• 2009

There has been an increasing demand on measurement of the vibration levels in commercial truck shipments, where all packaged products are exposed to some levels of random vibration and shock. In this study, bear glass bottles loaded at the front, middle, and rear positions of 11 tonne truck bed was shipped from Kwangju to Waegouan. Vertical direction vibration levels were analysed and matching laboratory random vibration test was performed using power spectral density (PSD) profiles derived from truck transit records. Also, the effects of drop hight on glass bottles were evaluated. As expected, the maximum vibration levels were recorded at the rear of truck bed. No breakage of bottles were observed during truck transit and laboratory random vibration testing set at 0.52 $G_{rms}$. In drop test, glass bottles were not damaged by bottom side impact, while short side drop impact caused about twice higher bottle breakage rate than that of long side drop impact at 30 cm and 40 cm drop hight.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4D
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• pp.593-600
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• 2006

Joint opening of jointed concrete pavement is caused by change in temperature and humidity of adjoined slab. The magnitude of joint opening influences on the load-transfer-efficiency and the behavior of sealant. If temperature or humidity decreases, joint opening increases. Generally maximum joint opening of a given joint is predicted by using AASHTO equation. While different magnitudes of joint opening at the individual joints have been observed in a given pavement section, AASHTO equation is limited to predict average joint opening in a given pavement section. Therefore the AASHTO equation may underestimate maximum joint for the half of joint in a given pavement section. Joints showing larger opening than the designed may experience early joint sealant failure, early faulting. Also unexpected spalling may be followed due to invasion of fine aggregate into the joints after sealant pop-off. In this study, the variation of the joint opening in a given pavement section was investigated based on the LTPP SMP data. Factors affecting on the variation are explored. Finally a probabilistic joint opening model is developed. This model can account for the reliability of the magnitude of joint opening so that the designer can select the ratio of underestimated joint opening.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.2
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• pp.223-230
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• 2012

The strength of brittle material has commonly been characterized by a normal distribution or Weibull distribution, but it may fit the gamma distribution for some material. The use of an extreme value distribution is proper when the largest values of a set of stresses dominate the failure of the material. This paper presents a formula for reliability estimation based on stress-strength interference theory that is applicable when the strength of material is distributed like a gamma distribution and the stress is distributed like an extreme value distribution. We verified the validity of the equation for the reliability estimation by examining the relationships among the factor of safety, the coefficient of variation, and the reliability. The required minimum factor of safety and the highest allowable coefficient of variation of stress can be estimated by choosing an objective reliability and estimating the reliabilities obtained for various factors of safety and coefficients of variation.

• Journal of Korean Society of Water and Wastewater
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• v.32 no.1
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• pp.37-45
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• 2018

This study develops a model to estimate the economic life of the large-diameter water supply pipeline in Korea by supplementing existing methods used to perform similar calculations. To evaluate the developed methodology, the model was applied to the actual target area with the conveyance pipe in P waterworks. The application yielded an economic life computation of 39.7 years, considering the cost of damages, maintenance, and renewal of the pipeline. Based on a sensitivity analysis of the derived results, the most important factor influencing the economic life expectancy was the predicted failure rate. The methodology for estimating the economic life of the water supply pipeline proposed in this study is one of the core processes of basic waterworks facility management planning. Therefore, the methods and results proposed in this study may be applied to asset management planning for water service providers.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.357-357
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• 2019

최근 한반도에서 예측하기 어려운 지진현상 및 가뭄, 폭우 등 이상기후현상이 지속적으로 발생하고 있다. 댐과 같은 수공구조물의 치수능력부족은 구조물의 파손이나 붕괴로 직결되며, 대규모 재산피해와 인명피해를 야기한다. 이에 댐의 안전성 위험요인들로부터 구조적 안전성을 평가하기 위한 방법에 대한 관심이 높아지고 있다. 비상방류시설은 여수로와 함께 저수지의 저류수를 안전하게 배제시킬 수 있는 구조물로서, 댐 비상상황 발생 시 가능한 빠른 시간 내 댐 수위를 낮춰 댐 손상을 최소화 시킨다. 이러한 점에서 비상방류시설의 적정성 평가하기 위한 방법의 도입이 필요할 것으로 판단되며, 본 연구에서는 비상방류시설의 수위강하율을 산정하여 댐 축조재에 따른 적합성을 평가하는 방법을 제시하였다. 필댐의 경우 5가지, 콘크리트, 석괴댐의 경우 3가지 사항에 대한 수위강하율의 적합성을 고려하였으며, 각각의 고려사항에 대한 결과의 점수화를 통해 비상방류시설의 적정성을 평가하였다. 최종적으로 경과연수가 50년 이상된 필댐을 선정하여 비상방류시설 수위강하율의 고려사항에 대한 적합성 평가를 수행하였다. 본 연구방법은 댐의 안정성 개선을 위한 노후화된 댐의 현황 파악 및 비상방류시설의 적정 운영방안을 수립하는데 있어 보다 합리적인 기준을 제시할 수 있을 것으로 판단된다.

• Composites Research
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• v.18 no.2
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• pp.20-29
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• 2005

To evaluate the flexural deformation and strength of composite motor case above the glass transition temperature$(T_g),\;170^{\circ}C$, of resin material, a finite element analysis(FEA) model in which material non-linearity and progressive failure mode were considered was proposed. The laminated flexural specimens which have the same lay-up and thickness as the composite motor case were tested by 4-point bending test to verify the validity of FEA model. Also. mechanical properties in high temperature were evaluated to obtain the input values for FEA. Because the material properties related to resin material were highly deteriorated in the temperature range beyond $T_g$, the flexural stiffness and strength of laminated flexural specimen in $200^{\circ}C$ were degraded by also $70\%\;and\;80\%$ in comparison with normal temperature results. Above $T_g$, the failure mode was changed from progressive failure mode initiated by matrix cracking at $90^{\circ}$ ply in bottom side and terminated by delamination at the center line of specimen to fiber compressive breakage mode at top side. From stress analysis, the progressive failure mechanism was well verified and the predicted bending stiffness and strength showed a good agreement with the test results.

• International Journal of Highway Engineering
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• v.7 no.4 s.26
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• pp.69-77
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• 2005

Joint spacing is a potent influence in increasing the long term performance of jointed concrete pavement slabs through the control of tensile stress, sealant failure and Load Transfer Efficiency (LTE). Internal Joint Spacing is an empirical and fixed method therefore this study will present the optimum joint spacing considerations depending on various climactic conditions. Calculating the optimum joint spacing eliminates random cracking due to the effect of the environmental loads such as the early behavior of drying shrinkage and heat hydration. Optimum joint spacing is calculated so as not to cause pavement distress by the deterioration of LTE by long term pavement movement. This study shows that the provisional joint spacing is 6-8m. Pavement Distress Prediction Models show that pavement distress has no effect on joint spacing of 8m.

• Journal of the Korea Convergence Society
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• v.13 no.2
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• pp.249-255
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• 2022

This study proposed an analysis framework for real-time prediction of CNC processing defects using machine learning-based models that are recently attracting attention as processing defect prediction methods, and applied it to CNC machines. Analysis shows that the XGBoost, CatBoost, and LightGBM models have the same best accuracy, precision, recall, F1 score, and AUC, of which the LightGBM model took the shortest execution time. This short run time has practical advantages such as reducing actual system deployment costs, reducing the probability of CNC machine damage due to rapid prediction of defects, and increasing overall CNC machine utilization, confirming that the LightGBM model is the most effective machine learning model for CNC machines with only basic sensors installed. In addition, it was confirmed that classification performance was maximized when an ensemble model consisting of LightGBM, ExtraTrees, k-Nearest Neighbors, and logistic regression models was applied in situations where there are no restrictions on execution time and computing power.