Techno Press
Techno Press

Smart Structures and Systems   Volume 13, Number 6, June 2014, pages 901-925
Automated condition assessment of concrete bridges with digital imaging
Ram S. Adhikari, Ashutosh Bagchi and Osama Moselhi

Abstract     [Full Text]
    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.
Key Words
    visual inspection; 3D visualization model; condition rating; neural networks; image analysis; depth perception; bridge defects
Ram S. Adhikari, Ashutosh Bagchi and Osama Moselhi: Department of Building, Civil, and Environment Engineering, Concordia University, Montreal, Canada

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