Journal of the Korean Society for Railway (한국철도학회논문집)

The Korean Society for Railway (한국철도학회)
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A Condition Rating Method of Bridges using an Artificial Neural Network Model

인공신경망모델을 이용한 교량의 상태평가

  • Published : 2010.02.26
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Abstract

It is increasing annually that the cost for bridge Maintenance Repair & Rehabilitation (MR&R) in developed countries. Based on Intelligent Technology, Bridge Management System (BMS) is developed for optimization of Life Cycle Cost (LCC) and reliability to predict long-term bridge deteriorations. However, such data are very limited amongst all the known bridge agencies, making it difficult to reliably predict future structural performances. To alleviate this problem, an Artificial Neural Network (ANN) based Backward Prediction Model (BPM) for generating missing historical condition ratings has been developed. Its reliability has been verified using existing condition ratings from the Maryland Department of Transportation, USA. The function of the BPM is to establish the correlations between the known condition ratings and such non-bridge factors as climate and traffic volumes, which can then be used to obtain the bridge condition ratings of the missing years. Since the non-bridge factors used in the BPM can influence the variation of the bridge condition ratings, well-selected non-bridge factors are critical for the BPM to function effectively based on the minimized discrepancy rate between the BPM prediction result and existing data (deck; 6.68%, superstructure; 6.61%, substructure; 7.52%). This research is on the generation of usable historical data using Artificial Intelligence techniques to reliably predict future bridge deterioration. The outcomes (Long-term Bridge deterioration Prediction) will help bridge authorities to effectively plan maintenance strategies for obtaining the maximum benefit with limited funds.

대부분의 선진국에서 교량의 유지보수 및 보강(Maintenance Repair & Rehabilitation-MR&R)으로 인한 비용은 해마다 증가하고 있다. 전산화된 교량유지관리 및 의사결정시스템(Bridge Management System-BMS)은 가능한 최저의 생애주기비용(Life Cycle Cost - LCC)에 최적의 안정성를 확보하기 위해 개발되었다. 본 논문에서는 제한된 현존하는 교량진단기록을 이용하여 현존하지 않는 과거의 교량상태등급 데이타를 생성하기 위해 Backward Prediction Model(BPM)이라 불리는 인공신경망(Artificial Neural Network-ANN)에 기초한 예측모델을 제시한다. 제안된 BPM은 한정된 교량 정기점검기록으로부터 현존하는 교량진단기록과 연관성을 확립하기 위해 교통량과 인구, 그리고 기후 등과 같은 비구조적 요소를 이용하며, 제한된 교량진단기록과 비구조적 요소 사이에 맺어진 연관성을 통해 현존하지 않는 과거의 교량상태등급 데이타를 생성할 수 있다. BPM의 신뢰도를 측정하기 위하여 Maryland DOT로 부터 얻어진 National Bridge Inventory(NBI)와 BMS 교량진단자료를 이용하였다. 이중 NBI자료를 이용한 Backward comparison 에 있어서 실제 NBI기록과 BPM으로 생성된 교량상태등급과의 차이(상판: 6.68%, 상부구조부: 6.61%, 하부구조부: 7.52%)는 BPM으로 생성된 결과의 높은 신뢰도를 보여준다. 이 연구의 결과는 제한된 정기점검 기록으로 야기되는 BMS의 장기 교량손상 예측에 관련된 사용상의 문제를 최소화하고 전반적인 BMS 결과의 신뢰도를 높이는데 기여 할 수 있다.

Keywords

References

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