Hotspot and Sampling Analysis for Effective Maintenance Management and Performance Monitoring (Research Brief]

Details

• Creators:
  Liu, Xiaoyue Cathy ; Chen, Zhuo
• Corporate Creators:
  University of Utah. Department of Civil and Environmental Engineering ; Mountain-Plains Consortium
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  • Algorithms
  • Asset management
  • Cluster analysis
  • Condition surveys
  • Data analysis
  • Highway maintenance
  • Inspection
  • Maintenance management
  • Performance measurement
  • Literature reviews
• Publication/ Report Number:
  MPC 19-392
• Resource Type:
  Brief
• Geographical Coverage:
  United States ; Utah
• Edition:
  Research Brief
• Corporate Publisher:
  Mountain-Plains Consortium
• Abstract:
  Researchers developed a sampling method utilizing machine learning techniques to suggest the location and frequency of sampling roadway assets. The method strives to choose proper highway segments where the conditions of sampled assets can represent the maintenance performance of the full inventory within the network. To this end, the researchers present a high-dimensional clustering-based stratified sampling (HDCSS) method for roadway asset inspection. The method allows transportation agencies to adjust parameters, such as sample size, inspection frequency, and assets of interest. The HDCSS method integrates asset deterioration prediction, high-dimensional clustering, and locality-sensitive hashing (LSH). The sampling method can also incorporate various features of the asset network, such as asset condition, geographic information, traffic condition, and geometric design, as the information upon which samples can be selected. The method is adaptable to any asset changes, as the sampling process is constantly updated with previous inspection results and maintenance records.
• Format:
  PDF
• Funding:
  528
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/92935/dot_92935_DS1.pdf
• File Type:
  [PDF - 335.62 KB]
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:845b5a0eff079116716f872cd230de986eb0f1130787b4934c96ab7715f17da7e18df208228f11ee7bd9294a9039569240b49883231c99392a38733be120062d