Details:

• Creators:
  Turkan, Yelda ; Laflamme, Simon ; Tan, Liangyu

  Turkan, Yelda ; Laflamme, Simon ; Tan, Liangyu Less -
• Corporate Creators:
  Iowa State University. Institute for Transportation
• Corporate Contributors:
  Midwest Transportation Center
• Subject/TRT Terms:
  [+]

  Bridges Lasers Optical Scanners Structural Health Monitoring 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.
  
  
• Format:
  PDF
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  [+]

  urn:sha256:487465db724a8ce0a1bfea67f9ff28e673be4fe35fe63270c9280473bde59588
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/30812/dot_30812_DS1.pdf
• File Type:
  [PDF-546.72 KB]