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

Details

• Creators:
  Turkan, Yelda ; Laflamme, Simon ; Tan, Liangyu
• Corporate Creators:
  Iowa State University. Institute for Transportation
• Corporate Contributors:
  Midwest Transportation Center
• Subject/TRT Terms:
  • Structural health monitoring
  • Lasers
  • Bridges
  • Optical scanners Terrestrial Laser Scanning
• Resource Type:
  Tech Report
• Geographical Coverage:
  Iowa
• Corporate Publisher:
  Iowa State University. Institute for Transportation
• Abstract:
  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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• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/30812/dot_30812_DS1.pdf
• File Type:
  [PDF - 546.72 KB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:f952152d576bbacc61a1567b39f77a972661ee1c695935931b5a4c1eb70274e021b95f72c7490035fb5a20d408e3d4fbaddec562bf3392078df774bd928031fd