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Feature extraction from 3D point clouds for automatic update of bridge inspection database

Objective, accurate, and fast assessment of bridge structural condition is critical to timely assess safety risks. Current practices for bridge condition assessment rely on visual observations and manual interpretation of reports and sketches prepared by inspectors in the field. Visual observation, manual reporting and interpretation has several drawbacks such as being labor intensive, subject to personal judgment and experience, and prone to error. Terrestrial laser scanners (TLS) are promising sensors for automatically identifying structural condition indicators, such as cracks, displacements and deflected shapes, as they are able to provide high coverage and accuracy at long ranges. However, there is limited research conducted on employing laser scanners to detect cracks for bridge condition assessment, which mainly focused on manual detection and measurements of cracks, displacements or shape deflections from the laser scan point clouds. This research project proposes to measure the performance of TLS for automatic detection of cracks for bridge structural condition assessment. Laser scanning is an advance imaging technology that is used to rapidly measure the 3D coordinates of densely scanned points within a scene. The data gathered by a laser scanner is provided in the form of point clouds with color and intensity data often associated with each point within the cloud. Point cloud data can be analyzed using classification metrics and computer vision algorithms to detect cracks for condition assessment of reinforced concrete structures. In this research project, computer vision algorithms for detecting cracks from laser scan point clouds have been developed based on the state-of-the-art condition assessment codes and standards. Using the proposed method for crack detection would enable automatic and remote assessment of bridge condition. This would, in turn, result in reducing costs associated with infrastructure management, and improving the overall quality of our infrastructure by enhancing maintenance operations.

Author(s):

Yelda Turkan    
Iowa State University
United States

Liangyu Tan    
Iowa State University
United States

Simon Laflamme    
Iowa State University
United States