• 한국방재학회:학술대회논문집
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• 2011.02a
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• pp.165-165
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• 2011

내진 설계규정이 적용되기 이전에 시공되어 사용 중인 교량의 경우 지진 발생시 교각의 파괴 또는 구조적 피해는 교량 전체 시스템의 붕괴를 초래하므로 지진하중에 대하여 피해를 최소화해야 한다. 이를 위해 내진설계규정이 적용되기 이전의 교량 또는 지진취약지역으로 분류된 곳의 교량, 사회적 중요도가 높은 교량에 대해 교각의 내진성능보강을 실시하고 있다. 2007년 말 국토해양부가 관리하고 있는 11,940개 교량 중 지진 발생시 피해가 우려되는 1,342개(일반국도 682개, 고속국도 600개) 교량에 대해 2006년부터 내진보강이 착수되었고 2009년에는 확대 추진하여 일반국도 80개교, 고속국도 100개교에 대한 보강을 실시하였다. 이와 같이 확대 추진되고 있는 정책에 반해, 내진보강 기술 및 제품이 부족하고 새로운 내진보강재 개발이 불가피해지고 있는 것이 현실이다. 소성영역에서의 횡방향 철근은 지진 시 종방향 철근의 좌굴과 콘크리트의 압축강도저하를 방지하며, 전단보강철근으로도 중요한 역할을 하여 교각의 전단강도를 증가시킨다. 그러나 이러한 횡방향 철근은 초기 설계에 의한 시공이 종료된 후 기존의 성능을 증가시키기 위하여 철근량을 증가하거나 단면의 변화를 주기에는 매우 어려운 일이다. 따라서 내진성능을 위한 단면력 증가를 위하여 다양한 재료의 보강재와 형식이 사용되고 있다. 본 연구에서는 원형교각 모델의 구조해석을 이용해 내진성능평가를 선행한 후 실험체를 제작, Helical Bar를 보강하여 준정적 실험을 통해 내진보강성능을 평가하였다. 압축설계강도 $f_{ck}=240kgf/cm^2$를 기준으로 교량등급 2등교인 일반적인 도로교의 1/4축소모형을 설계, 기초부는 $1,200{\times}600{\times}600$ (mm)으로 철근과 콘크리트로 구성하였으며, 기둥부는 직경 400mm, 높이 1,250mm 크기의 철근콘크리트 원형 교각 실험체를 제작하였다. 제작된 실험체는 총 3개로, 분류는 무보강 일반 실험체, Helical Bar 직경에 따른 분류, 보강간격에 따른 분류로 나누어진다.

• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.6
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• pp.93-101
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• 2012

Bridge deck has a role in a comfortable and safe passage of vehicles. At the same time, it preserves upper structure against the abrasion and shearing due to impact of traffic loads in bridges or has a role to protect the plate from off adverse effect of climatic process as rain, chemicals. Currently, the total number of inspected bridges is 6,248 in the general national road and to maintain effectively, Introduction of GPR system mounted in the vehicle has been considered. In this research, the comparison and analysis of bridge deck condition on general national road has been performed with major variations of superstructure type, span lengths, located region and ages by using 'the current status of road bridge and tunnel' that is provided by MLTM(Ministry of Land, Transport and Maritime Affairs). As a result, Condition assessment grade, superstructure type, age and length were derived as a major factor to determine priority for the condition assessment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.36-43
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• 2021

Because bridges are major national infrastructure, regular safety inspections or diagnoses for bridges have been conducted in accordance with the "Special Act on the Safety and Maintenance of Facilities." Accordingly, the condition and safety assessments of the bridge are conducted to derive the condition and safety rating, respectively. A lower result is determined to be the safety grade of the bridge. In this study, the relationship between the condition rating and safety rating, which are the core of the bridge safety grade, was analyzed by the representative superstructure types of bridges, such as RC slab, PSCI girder, Steel box girder, Rhamen, and Preflex girder, to identify the correlation status and range between each rating. A reasonable improvement direction for establishing existing maintenance policies was suggested by proposing an alternative plan to change the proper implementation cycle of the inspection and diagnosis of bridge superstructure types. As a result of the research, it is necessary to adjust the inspection and diagnosis cycle according to the superstructure type and safety grade. In addition, maintenance policies need to be improved through detailed research on more diverse bridge types in the future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.3
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• pp.397-411
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• 2023

The number of deteriorated bridges with a service period of more than 30 years has been rapidly increasing in Korea. Accordingly, the importance of advanced maintenance technologies through the predictions of age-induced deterioration degree, condition, and performance of bridges is more and more noticed. The prediction method of the safety grade of bridges was proposed in this study using the classification models of the Decision Tree and the Random Forest based on machine learning. As a result of analyzing these models for the 8,850 bridges located in national roads with various evaluation indexes such as confusion matrix, balanced accuracy, recall, ROC curve, and AUC, the Random Forest largely showed better predictive performance than that of the Decision Tree. In particular, random under-sampling in the Random Forest showed higher predictive performance than that of other sampling techniques for the C and D grade bridges, with the recall of 83.4%, which need more attention to maintenance because of the significant deterioration degree. The proposed model can be usefully applied to rapidly identify the safety grade and to establish an efficient and economical maintenance plan of bridges that have not recently been inspected.