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RISS 인기검색어
- 검색키워드
- 저자 : Ram S. Adhikari (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
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Automated condition assessment of concrete bridges with digital imaging
Adhikari, Ram S.,Bagchi, Ashutosh,Moselhi, Osama Techno-Press 2014 Smart Structures and Systems, An International Jou Vol.13 No.6
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, Naive Bayes Classifiers and Bagged Decision Tree.
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Automated condition assessment of concrete bridges with digital imaging
Ram S. Adhikari,Ashutosh Bagchi,Osama Moselhi 국제구조공학회 2014 Smart Structures and Systems, An International Jou Vol.13 No.6
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, provied 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 inconcrete bridge components is considered as a candidate defect and the guidelinges in the Ontario Structure Inspection Manual (OSIM) have been adopted for developing and testing the proposed method. The automated algorighms 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, Naive Bayes Classifiers and Bagged Decision Tree.
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