Terrestrial Laser Scanning-Based Bridge Structural Condition Assessment : Tech Transfer Summaries
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Terrestrial Laser Scanning-Based Bridge Structural Condition Assessment : Tech Transfer Summaries

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      Problem Statement While several state departments of transportation (DOTs) have used terrestrial laser scanning (TLS) in the project planning phase, limited research has been conducted on employing laser scanners to detect cracks for bridge condition assessment. Background Most bridge condition assessments in the US currently require trained inspectors to conduct complex and time-consuming visual inspections. TLS is a promising alternative method for documenting infrastructure condition. This advanced imaging technology rapidly measures the three-dimensional (3D) coordinates of densely scanned points within a scene to produce 3D point clouds, which are then analyzed using computer vision algorithms to assess structural conditions. This technology has been shown to effectively identify structural condition indicators, such as cracks, displacements, and deflected shapes, and is able to provide high coverage and accuracy at long ranges. However, large-scale, high-resolution scanning requires a significant amount of time on site, and data file sizes are typically very large and require extensive computational resources. Therefore, advanced algorithms are needed that would enable automated 3D shape detection from low-resolution point clouds during data collection. Project Objectives • Measure the performance of TLS for the automatic detection of cracks for bridge structural condition assessment • Develop adaptive wavelet neural network (WNN) algorithms for detecting cracks from laser scan point clouds based on state-of-the-art condition assessment codes and standards Laser scanning a concrete cylinder MTC Iowa State University 2711 S. Loop Drive, Suite 4700 Ames, IA 50010-8664 515-294-8103 The Midwest Transportation Center (MTC) is a regional University Transportation Center (UTC). Iowa State University, through its Institute for Transportation (InTrans), is the MTC lead institution. MTC’s research focus area is State of Good Repair, a key program under the 2012 federal transportation bill, the Moving Ahead for Progress in the 21st Century Act (MAP-21). MTC research focuses on data-driven performance measures of transportation infrastructure, traffic safety, and project construction. The opinions, findings, and conclusions expressed in this publication are those of the authors and not necessarily those of the project sponsors. Using computer vision algorithms to process laser scanner point cloud data would allow a bridge’s condition to be assessed automatically and remotely, which would ultimately help improve infrastructure management.
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