• 국제학술발표논문집
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• The 3th International Conference on Construction Engineering and Project Management
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• pp.700-706
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• 2009

Bridges are vital components of any road network which demand crucial and timely decision-making for Maintenance, Repair and Rehabilitation (MR&R) activities. Bridge Management Systems (BMSs) as a decision support system (DSS), have been developed since the early 1990's to assist in the management of a large bridge network. Historical condition ratings obtained from biennial bridge inspections are major resources for predicting future bridge deteriorations via BMSs. Available historical condition ratings in most bridge agencies, however, are very limited, and thus posing a major barrier for obtaining reliable future structural performances. To alleviate this problem, the verified Backward Prediction Model (BPM) technique has been developed to help generate missing historical condition ratings. This is achieved through establishing the correlation between known condition ratings and such non-bridge factors as climate and environmental conditions, traffic volumes and population growth. Such correlations can then be used to obtain the bridge condition ratings of the missing years. With the help of these generated datasets, the currently available bridge deterioration model can be utilized to more reliably forecast future bridge conditions. In this paper, the prediction accuracy based on 4 and 9 BPM-generated historical condition ratings as input data are compared, using deterministic and stochastic bridge deterioration models. The comparison outcomes indicate that the prediction error decreases as more historical condition ratings obtained. This implies that the BPM can be utilised to generate unavailable historical data, which is crucial for bridge deterioration models to achieve more accurate prediction results. Nevertheless, there are considerable limitations in the existing bridge deterioration models. Thus, further research is essential to improve the prediction accuracy of bridge deterioration models.

• Corrosion Science and Technology
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• 제3권4호
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• pp.166-171
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• 2004

Recent developments in Bridge Management Systems (BMS) and in Life-Cycle Cost (LCC) of bridges, have raised the need for evaluation procedure of future condition (Deterioration) of a bridge. Predicting future deterioration is not an easy task due to limited past data to extrapolate from and also due to difficulty in measuring actual deterioration such as section loss of steel on an actual steel bridge. Also, increase in live load and reduction of resistance are random variables, thus a probabilistic approach should be adopted for determining the future deterioration. Due to difficulties in evaluation of future deterioration on steel bridges, accepting uncertainties within a reasonable error, a deterministic procedure using bridge condition rating can be a useful tool for projection of future condition of bridges to identify repair and maintenance needs. The object of this paper is to determine applicability of evaluating deterioration of steel bridge components based on Bridge condition ratings. Bridge condition ratings of bridge components show wide variation for bridges of same age and does not directly correlate well with the age of the bridge and/or deterioration of the bridge. High uncertainty can be reduced by breaking down the rating and by sensitivity analysis. From refined condition rating data, generalized deterioration profile of structures based on age can be derived. Examples are shown for sample bridges in USA. Approximately, 3,000 short to medium span steel bridges were listed in the inventory database. Results show wide variation of rating factors but by subdividing the Bridge condition ratings for various categories general deterioration profiles of steel bridges can be determined.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2000년도 가을 학술발표회 논문집(II)
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• pp.1291-1296
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• 2000

A basis for the direct use of data from nondestructive evaluation methods in bridge management systems is presented. Bridge management systems use integer-valued condition ratings to recognize conditions of bridge elements, to model progression of deterioration, and to determine repair needs. Data from nondestructive evaluation methods can inform management systems on the extent of damage, on the initiation of deterioration processes, and on the exposure of bridge elements to aggressive agents. In addition, data obtained through nondestructive evaluation methods allow the formation of models of specific deterioration process. The use of these data in bridge management systems requires redefinition of condition ratings together with the creation of procedures for automated interpretation of data. By these action, nondestructive evaluation methods are directly used to assign condition ratings, and condition ratings are made into terse form of NDE data that are compatible with present day bridge management systems. This paper reports work in progress to strategic use of nondestructive evaluation methods in bridge management system.

• 한국철도학회논문집
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• 제13권1호
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• pp.71-77
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• 2010

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.

• Smart Structures and Systems
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• 제15권3호
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• pp.543-553
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• 2015

The condition of the vehicular bridge network in New York City, as represented by ratings obtained during biennial inspections is reviewed over a period of three decades. Concurrently, the bridges comprising the network are considered as networks of structural elements whose condition defines the overall bridge condition according to New York State assumptions. A knowledge-based matrix of assessments is used in order to determine each element's vulnerability and impact within the network of an individual structure and the network of City bridges. In both networks expansion deck joints emerge as the weak link. Typical joint failures are illustrated. Bridge management options for maintenance, preservation, rehabilitation and replacement are examined in the context of joint performance.

• 한국구조물진단유지관리공학회 논문집
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• 제7권3호
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• pp.251-262
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• 2003

교량의 건설계획단계에서 LCC을 고려한 의사결정이나 공용중인 교량의 체계적인 유지관리 전략을 수립하기 위해서도 최소한의 점검결과만으로 노후화를 예측할 수 있는 LCP가 필요하다. 본 연구에서는 국내외 연구결과를 토대로 무리함수 $y=\sqrt{y^2_0-at}$로 표현되는 LCP를 제안하여 국내외연구결과에서 적용한 D/B에 적용한 결과 상관계수가 0.99이상으로 노후화 경향을 표현할 수 있었으며, 전국에 분포되어 있는 슬래브교량을 대상으로 정밀점검 및 정밀안전진단의 BMS를 Fuzzy Logic을 이용하여 정량적 평가하여 회귀분석을 실시한 결과 0.81의 상관계수를 갖는 노후화 예측모델을 도출할 수 있었다.

• 국제학술발표논문집
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• The 1th International Conference on Construction Engineering and Project Management
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• pp.533-538
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• 2005

An outline of an Artificial Neural Network (ANN) model for bridge condition rating and the results of a pilot study are presented in this paper. Most BMS implementation systems involve an extensive range of data collection to operate accurately. It takes many years to effectively implement a BMS using existing methodologies. This is due to unmatched data requirements. Such problems can be overcome by adopting the ANN model presented in this paper. The objective of the proposed model is to predict bridge condition ratings using historical bridge inspection data for effective BMS operation.

• Smart Structures and Systems
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• 제13권6호
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• pp.901-925
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• 2014

The reliability of a Bridge management System depends on the quality of visual inspection and the reliable estimation of bridge condition rating. However, the current practices of visual inspection have been identified with several limitations, such as: they are time-consuming, provide incomplete information, and their reliance on inspectors' experience. To overcome such limitations, this paper presents an approach of automating the prediction of condition rating for bridges based on digital image analysis. The proposed methodology encompasses image acquisition, development of 3D visualization model, image processing, and condition rating model. Under this method, scaling defect in concrete bridge components is considered as a candidate defect and the guidelines in the Ontario Structure Inspection Manual (OSIM) have been adopted for developing and testing the proposed method. The automated algorithms for scaling depth prediction and mapping of condition ratings are based on training of back propagation neural networks. The result of developed models showed better prediction capability of condition rating over the existing methods such as, Naïve Bayes Classifiers and Bagged Decision Tree.

• Structural Monitoring and Maintenance
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• 제2권3호
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• pp.199-211
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• 2015

The safety and performance of bridges could be monitored and evaluated by Structural Health Monitoring (SHM) systems. These systems try to identify and locate the damages in a structure and estimate their severities. Current SHM systems are applied to a single bridge, and they have not been used to monitor the structural condition of a network of bridges. This paper propose a new method which will be used in Synthetic Rating Procedures (SRP) developed by the authors of this paper and utilizes SHM systems for monitoring and evaluating the condition of a network of bridges. Synthetic rating procedures are used to assess the condition of a network of bridges and identify their ratings. As an additional part of the SRP, the method proposed in this paper can continuously monitor the behaviour of a network of bridges and therefore it can assist to prevent the sudden collapses of bridges or the disruptions to their serviceability. The method could be an important part of a bridge management system (BMS) for managers and engineers who work on condition assessment of a network of bridges.

• 한국구조물진단유지관리공학회 논문집
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• 제25권5호
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• pp.114-126
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• 2021

본 논문은 국내·외 (미국, 영국, 일본, 한국)의 교량유지관리활동을 비교하였다. 교량은 물류 및 교통망에 있어 매우 중요한 역할을 하고 있다. 제한된 예산으로 시민에게 적절한 수준의 서비스를 제공하기 위해서는 효율적인 교량 유지관리 활동 (예, 점검 및 보수·보강)이 이루어져야만 한다. 1980년대 후반 급속한 경제 성장은 국내 사회기반 시설 증가로 이어졌고 교량의 개소수 또한 급속도로 증가 하게 하였다. 교량 증가와 함께 노후화 또한 최근 빠르게 진행되고 있으며 이러한 교량 노후화 속도는 교량 유지관리를 담당하고 있는 관리주체에게는 상당한 부담이 되고 있다. 다행이 이러한 과정을 이미 경험하고 있는 해외국가의 체계적이고 종합적인 교량 유지관리 체계는 국내 유지관리 체계에 상당한 시사점을 줄 수 있을 것이라 판단하다. 따라서, 본 연구의 목적은 교량유지관리 선진국 (예, 미국, 영국, 일본)에서 시행되고 있는 교량 유지관리활동을 비교 분석하여 국내 유지관리에 시사하는 점을 분석하였다.