• Journal of Korean Society of Steel Construction
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• v.28 no.5
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• pp.325-335
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• 2016

To predict service life of coating systems registered in Korean specifications for steel bridge coatings, field deterioration evaluation and accelerated weatherproof test were carried out, and deterioration models were drawn through regression analysis for evaluation results. For the coating systems that have not been used in field, regression analyses were carried out for the virtual evaluation results drawn by applying coordination factor to the field evaluation results for chlorinated rubber and urethane topcoat system. Service life prediction results showed that application of thermal sprayed coating (TSC) could extend service life of coatings to more than twice of general coatings.

• International Journal of Highway Engineering
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• v.12 no.2
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• pp.9-16
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• 2010

Roughness is the most important factor to maintain the road performance, and affects greatly on the design life in Jointed Plain Concrete pavements. Also, the factors the evaluate pavement‘s commonality is the three method such as functionality, safety and structural performance. In evaluating function of road, representative factors is the roughness, which has been used to determine maintenance time as key standard. As research for roughness is absence in pavement design. Applied roughness-model had a low-reliability in Korea. Therefore, it is needed to develop reliable model in road roughness. In this research, uniform specific is applied to distribute them after selecting the concrete pavements. Concrete pavement is divided by sections of 238. Total length of this sections has 281km and account for 16% of total road length in korean concrete pavements for selected sections. Considering the korean roughness-model, the evaluation of roughness is performed for the freezing index, average annual rainfall, condition for the base, the amount of traffic as well as spalling(%), cracking(%), age(year) at the selected section at the selected section. Also, additional sections is selected to evaluate various age which affects on the roughness. As a result of the analysis, it showed that spalling(%), cracking(%), age(year), and the condition of the base affected road roughness. When the correlation with the road roughness was analyzed, the reliable model for road roughness was proposed, and the ratio that can explain road roughness was R2-68.8% and P value-0 which is statistically meaningful.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.4
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• pp.53-60
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• 2019

In this study, a method for updating the finite element model of bridges with genetic algorithm using static displacement were presented, and verified this method using field test data for PSC girder bridge. As a field test, static load test and pseudo-static load test were conducted, and updated the finite element model of test bridge using each test data. Finally, evaluated the bridge load carrying capacity with updated model using pseudo-static load test's displacement data. To evaluate the bridge load carrying capacity, KHBDC-LSD, KHBDC and AASHTO LRFD's live load model were used, and compared the each results.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.3
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• pp.39-43
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• 2010

The main objective of this research is to develop a mechanistic performance predictive model for fatigue cracking of asphalt-aggregate mixtures. Controlled-stress diametral fatigue tests were performed to characterize fatigue cracking of asphalt-aggregate mixtures. Performance prediction model for fatigue cracking was developed using the internal damage ratio (IDR) growth method. In the IDR growth method, the general concepts of the dissipated energy, the reference tensile strain, the threshold tensile strain, and the strain shift factor were introduced. The source of the dissipated energy in the fatigue test is from the intrinsic viscoelastic material property of an asphalt concrete mixture and the damage growth within the asphalt concrete specimen. In controlled-stress mode test, the dissipated energy is gradually increased with an increasing number of load applications.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.05c
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• pp.1805-1808
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• 2003

웹 서비스의 위치와 컨텐츠에 대한 정보를 제공하는 UDDI 는 현재 IBM, Microsoft 그리고 SAP 에서 공용 UDDI 레지스트리 서비스 형태로 제공되고 있다. 기존의 UDDI 레지스트리 서비스는 웹 서비스 사용자 입장에서 볼 때 이용이 어렵고, 제공업체에 따른 UDDI 의 검색 도구와 방식의 차이로 인한 혼란이 있으며, 검색 효율도 만족스럽지 못한 실정이다. 이에 본 논문에서는 이러한 문제를 보완하기 위해 UDDI 레지스트리 서비스 중 검색을 위한 에이전트 개념 모델을 제안한다 UDDI 검색 에이전트는 사용자와 공용 UDDI 레지스트리 서비스 사이에 위치하며, UDDI 서비스 이용의 편의를 위해 사용자의 정보를 관리하는 UDDI 브라우저, 필요 시 검색 과정의 각 상황에 대한 정보 및 해결책을 제공하는 Help Desk, 사용자의 UDDI 레지스트리 검색 성향을 분석하는 패턴 분석기, 그리고 이전에 검색한 웹 서비스 정보 검색 경로를 기록한 Search Map으로 구성된다. 이 외에 Search Map을 작성하기 위한 Search Map Building Engine 또한 사용된다 이러한 UDDI 검색 에이전트 개념 모델의 구성요소들은 서로 메시지를 주고받으며 유기적으로 협력해, 사용자 입장에서 보다 더 쉽고, 용이하고, 효율적인 UDDI 검색 서비스를 제공한다.

• 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.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.5
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• pp.20-29
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• 2024

The chloride diffusion coefficient is a critical indicator for assessing the durability of concrete marine substructures. This study develops a prediction model for the chloride diffusion coefficient using data from concrete bridges located in marine exposure zones (atmospheric, splash, tidal), an aspect that has not been considered in previous studies. Chloride profile data obtained from these bridge substructures were utilized. After data preprocessing, machine learning models, including Random Forest (RF), Gradient Boosting Machine (GBM), and K-Nearest Neighbors (KNN), were optimized through hyperparameter tuning. The performance of these models was developed and compared under three different variable sets. The first model uses six variables: water-to-binder (W/B) ratio, cement type, coarse aggregate volume ratio, service life, strength, and exposure environment. The second model excludes the exposure environment, using only the remaining five variables. The third model relies on just three variables: service life, strength, and exposure environment factors that can be obtained from precision safety diagnostics. The results indicate that including the exposure environment significantly enhances model performance for predicting the chloride diffusion coefficient in concrete bridges in marine environments. Additionally, the three variable model demonstrates that effective predictions can be made using only data from precision safety diagnostics.

• International Journal of Highway Engineering
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• v.10 no.4
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• pp.79-89
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• 2008

The pavement performance model is the most important factor to determine the pavement life in the mechanistic-empirical pavement design guide (MEPDG). As part of Korean Pavement Research Program (KPRP), the Korean Pavement Design Guide (KPDG) is currently being developed based on mechanistic-empirical principle. In this paper, the rutting prediction model of asphalt mixtures, one of the pavement performance model, has been developed using triaxial repeated loading testing data. This test was conducted on various types of asphalt mixtures for investigating the rutting characteristics by varying with the temperature and air void. The calibration process was made for the coefficients of rutting prediction model using the accelerated pavement testing data. The accuracy of prediction model can be increased when by considering the effect of individual rutting properties of materials rather than shear stresses with depths.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.21-30
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• 2020

In this paper, we predicted the propagation loss for the air-to-ground (A2G) channel between the ground control system and the unmanned aerial vehicle (UAV) using the prediction model for the aircraft recommended by the International Telecommunication Union (ITU). We analyzed the network coverage of the aerial relay system based on the medium altitude UAVs by expanding it into the air-to-air (A2A) channel. Climate and geographic factors in Korea were used to predict propagation loss due to atmospheres. We used the measured data published by the Telecommunication Technology Association (TTA) for regional rainfall-rate and effective earth radius factors to increase accuracy. In addition, the aerial relay communication system used the key parameter of the common data link (CDL) system developed in Korea recently. Prediction results show that the network coverage of the aerial relay system broadens at higher altitude.

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2007.11a
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• pp.133-134
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• 2007