• Proceedings of the Computational Structural Engineering Institute Conference
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• 2009.04a
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• pp.553-556
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• 2009

최근 우리나라의 건설 분야에서는 생애주기비용(Life Cycle Cost;이하 LCC)/가치공학(Value Engineering; 이하 VE)을 적용한 구조물 설계가 실시되고 있다. 이는, 건설교통부에서 공공건설사업의 효율성을 제고하기 위하여 실시하고 있으며, 대통령령에 따라 "건설기술관리법시행령"을 제정하여 공공사업분 수행 절차와 기준을 법제화 하였으며 이후 시행령 38조 13의 "설계의 경제성등 검토" 실시를 의무화하는 시행지침을 작성하여 수행하고 있다. 이러한, LCC/VE의 검토에서 보수 보강 공사비 산정은 유지보수공사 프로파일링을 통한 보수 보강 시기를 산정(건설교통부, 2003)하여 교량 구성요소별 보수 교체 주기를 산정(건설교통부, 2001)에서 제시한 기간을 적용하여 LCC/VE를 평가하고 있다. 하지만, 이러한 보수 보강 공사비의 적용은 일괄적인 적용이며, 예전 국내의 교량 건설기술이 현재와 같이 발전된 상태에서의 현황이 아니므로 본 연구에서는 현재 고속국도에 완공되어 운용중인 교량 구조물을 시설물의 안전관리에 관한 특별법 시행령(2008)에 따른 "시설물의 안전점검 및 정밀안전진단 지침"에 의한 교량의 초기점검, 정밀점검 및 정밀안전진단 자료를 조사 분석하여 보수 보강 공사비 곡선을 추정하려 한다.

• Construction Engineering and Management
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• v.4 no.1 s.13
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• pp.43-47
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• 2003

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.2
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• pp.165-173
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• 2023

The data management and the evaluation of defects in the bridge are generally conducted based on inspection and diagnosis data, including the exterior damage map and defect quantity table prepared by periodic inspection. Since most of these data are written in 2D-based documents and are difficult to digitize in a standardized manner, it is challenging to utilize them beyond the defined functionality. This study proposed methods to efficiently build a BIM (Building Information Modeling)-based bridge damage model from raw data of inspection report and to manage and utilize the damage information linking to bridge model through the spread sheet data generated by COBie (Construction Operations Building Information Exchange). In addition, a method to conduct the condition assessment of defects in bridge was proposed based on an automatic evaluation process using digitized bridge member and damage information. The proposed methods were tested using superstructure of PSC-I girder concrete bridge, and the efficiency and effectiveness of the methods were verified.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.4
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• pp.505-516
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• 2018

In order to establish an efficient bridge maintenance strategy, the future performance of a bridge must be estimated by considering the current performance, which allows more rational way of decision-making in the prediction model with higher accuracy. However, personnel-based existing maintenance may result in enormous maintenance costs since it is difficult for a bridge administrator to estimate the bridge performance exactly at a targeting management level, thereby disrupting a rational decision making for bridge maintenance. Therefore, in this work, we developed a representative performance prediction model for each bridge element considering uncertainty using domestic bridge inspection data, and proposed a bayesian updating method that can apply the developed model to actual maintenance bridge with higher accuracy. Also, the feasibility analysis based on calculation of maintenance cost for monitoring maintenance scenario case is performed to propose advantages of the Bayesian-updating-driven preventive maintenance in terms of the cost efficiency in contrast to the conventional periodic maintenance.

• Journal of Korean Society of Disaster and Security
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• v.16 no.4
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• pp.123-128
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• 2023

This paper presents the results of an analysis of the discrimination accuracy of a bridge load carrying capacity estimation model based on data from inspection reports. The load carrying rate estimation model was derived using statistical methods through the collection of 2,161 inspection reports. By entering the bridge specifications and maintenance information, you can check the estimated load carrying capacity of the bridge. In order to verify the discrimination accuracy of the estimated load carrying rate model, the estimated load carrying rate was compared with the load carrying rate in the inspection and diagnosis report for 164 public bridges for which data was available. Although there are differences depending on the bridge type, the results were obtained with an accuracy of over 80% in determining the estimated load carrying capacity.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2022.10a
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• pp.347-348
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• 2022

본 연구에서는 무인이동체를 활용한 철도교량의 외관조사 점검을 보다 효율적이고 객관성 있게 수행하기 위하여 무인이동체를 통해 촬영된 이미지를 딥러닝 기반 분석기술을 활용하여 손상 자동으로 분석 하기위한 기술을 연구하였다. 철도교량의 외관 손상 중 균열, 콘크리트 박리·박락, 누수, 철근노출에 대한 손상 이미지를 추출하여 딥러닝 분석 모델을 생성하고 학습한 분석 모델을 적용한 시스템을 실제 현장에 적용 테스트를 수행하였으며 학습 구현된 분석모델의 검측 재현율을 검토한 결과 평균 95%이상의 감지성능을 검토할 수 있었다. 개발 제안된 자동손상분석 기술은 기존 육안점검 결과 대비 보다 객관적이고 정밀한 손상 검측이 가능하며 철도 유지관리 분야에서 무인이동체를 활용한 외관조사 업무를 수행함에 있어 기존 대비 객관적인 결과도출과 소요시간, 비용저감이 가능할 것으로 기대된다.

• Computational Structural Engineering
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• v.20 no.1 s.75
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• pp.78-82
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• 2007

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.180-185
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• 2003

The increase of traffic over a bridge has been emerged as one of the most severe problems in view of bridge maintenance, since the load effect caused by the vehicle passage over the bridge has brought out a long-term damage to bridge structure, and it is nearly impossible to maintain operational serviceability of bridge to user's satisfactory level without any concern on bridge maintenance at the phase of completion. Moreover, bridge maintenance operation should be performed by regular inspection over the bridge to prevent structural malfunction or unexpected accidents front breaking out by monitoring on cracks or deformations during service. Therefore, technical breakthrough related to this uninterested field of bridge maintenance leading the public to the turning point of recognition is desperately needed. This study has the aim of development on automated inspection system to lower surface of bridge superstructures to replace the conventional system of bridge inspection with the naked eye, where the monitoring staff is directly on board to refractive or other type of maintenance .vehicles, with which it is expected that we can solve the problems essentially where the results of inspection are varied to change with subjective manlier from monitoring staff, increase stabilities in safety during the inspection, and make contribution to construct data base by providing objective and quantitative data and materials through image processing method over data captured by cameras. By this system it is also expected that objective estimation over the right time of maintenance and reinforcement work will lead enormous decrease in maintenance cost.

• The Safety technology
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• no.168
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• pp.54-57
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• 2011

• Journal of the Society of Disaster Information
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• v.17 no.3
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• pp.556-567
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• 2021

Purpose: In this study, various methods of deep learning-based automatic damage analysis technology were reviewed based on images taken through Unmanned Aerial Vehicle to more efficiently and reliably inspect the exterior inspection and inspection of railway bridges using Unmanned Aerial Vehicle. Method: A deep learning analysis model was created by defining damage items based on the acquired images and extracting deep learning data. In addition, the model that learned the damage images for cracks, concrete and paint scaling·spalling, leakage, and Reinforcement exposure among damage of railway bridges was applied and tested with the results of automatic damage analysis. Result: As a result of the analysis, a method with an average detection recall of 95% or more was confirmed. This analysis technology enables more objective and accurate damage detection compared to the existing visual inspection results. Conclusion: through the developed technology in this study, it is expected that it will be possible to analysis more accurate results, shorter time and reduce costs by using the automatic damage analysis technology using Unmanned Aerial Vehicle in railway maintenance.