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Analysis and Prediction of Highway Bridge Deck Slab Deterioration

고속도로 교량 바닥판 열화분석 및 추정

  • 이일근 (한국도로공사 도로교통연구원) ;
  • 김우석 (충남대학교 토목공학과) ;
  • 강형택 (한국도로공사 도로교통연구원) ;
  • 서정욱 (한국도로공사 구조물처)
  • Received : 2014.10.08
  • Accepted : 2014.11.11
  • Published : 2015.03.30

Abstract

This study established key factors causing deck slab deterioration based on GPR database of 747 highway bridges, and predicted deck slab damage rates with respect to bridge service life. To minimize the influence of bridge service life on deck slab deterioration, the deck slab damage rate database was corrected based on a linear regression model of bridge service life vs. deck slab damage rate. The corrected deck slab damage rates were analyzed to determine correlation considering the number of snowy days, the amount of snowfalls, the number of freeze-thaw days, average winter temperature, altitude, the amount of deicing chemicals and equivalent traffic volume, and then both the number of freeze-thaw days and the amount of deicing chemicals were determined to be key factors causing deck slab deterioration. The complex deterioration considering both key factors was represented deck slab damage rate charts, and the average deck slab life was derived. The results of this study will be used as a guideline for highway bridge maintenance to identify the progress of deck slab deterioration for a given bridge and predict the time required deck slab rehabilitation.

본 연구에서는 고속도로 교량 바닥판 747개소의 GPR 데이터를 분석하여 주요 열화인자를 도출하고, 이들을 활용하여 교량의 공용연수에 따른 교량의 손상률을 추정하였다. 바닥판의 손상률 데이터를 공용연수에 따른 영향을 최소화하기 위해 공용연수에 따른 손상률 데이터를 선형회귀분석 모형을 사용하여 보정하였다. 보정된 바닥판 손상률과 강설일수, 강설량, 동결융해일수, 동절기평균기온, 고도, 제설제살포량, 환상교통량의 상관관계를 분석하였으며, 동결융해일수와 제설제살포량이 바닥판의 손상률에 지배적인 영향이 있음을 도출하였다. 동결융해일수와 제설제살포량을 동시에 고려하여 복합열화 상황 하에서 바닥판의 손상률 차트를 도출하고, 이를 기반으로 평균적인 예상 바닥판 수명을 도출하였다. 본 연구의 결과는 고속도로 교량의 유지관리 시에 현 교량상황별로 바닥판의 열화진전 상태와 전단면 개량이 필요한 시점을 예상할 수 있는 참고자료로 활용될 수 있을 것이다.

Keywords

References

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  3. Evaluation of the Depth of Deteriorations in Concrete Bridge Decks with Asphalt Overlays Using Air-Coupled GPR: A Case Study from a Pilot Bridge on Korean Expressway vol.13, pp.1, 2019, https://doi.org/10.1186/s40069-018-0327-7
  4. Bridge Infrastructure Asset Management System: Comparative Computational Machine Learning Approach for Evaluating and Predicting Deck Deterioration Conditions vol.26, pp.3, 2015, https://doi.org/10.1061/(asce)is.1943-555x.0000572
  5. 교량 안전등급 분석을 통한 유지관리전략 개선 방안 연구 vol.22, pp.3, 2015, https://doi.org/10.5762/kais.2021.22.3.36