Hotspot and Sampling Analysis for Effective Maintenance Management and Performance Monitoring

A high-dimensional clustering-based sampling method for roadway asset condition inspection is proposed in this study. The method complements existing literature by selecting sample roadway segments that contain multiple types of assets (e.g., signage, shoulder work, pavement marking, etc.) for the accurate estimation of their respective levels of maintenance (LOMs). This is consistent with the standard maintenance procedure, as inspection activities are often conducted on roadway segment basis. The proposed method consists of three components: current condition estimation, similarity matrix construction, and stratification. Current condition estimation predicts assets’ “current condition” by considering historical inspection records. Similarity matrix construction represents the core piece of the sampling framework, which employs a locality-sensitive hashing algorithm to define the similarity between segments. The stratification process is implemented with spectral clustering, which assigns segments into clusters based on the similarity matrix. The proposed method outperforms simple random sampling, which is widely used by state agencies, especially under the circumstances where LOM varies greatly across assets. The main highlight of the proposed method is the ability to select sample segments with multiple types of assets that are representative of their respective LOMs of the full inventory, which directly translates into an efficient maintenance activity management. The method is implemented using asset inspection records in the state of Utah from September 2014 to March 2016. It represents a potentially useful tool for agencies to effectively conduct asset inspection, and it can be easily adopted for choosing samples containing multiple features.

• Record URL:
  https://www.ugpti.org/resources/reports/details.php?id=955
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  University of Utah, Salt Lake City

  Department of Civil and Environmental Engineering
  122 South Central Campus Drive, Suite 104
  Salt Lake City, UT  United States  84112

  Mountain-Plains Consortium

  North Dakota State University
  Fargo, ND  United States  58108

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Authors:
  • Liu, Xiaoyue Cathy
  • Chen, Zhuo
• Publication Date: 2019-7

Language

• English

Media Info

• Media Type: Digital/other
• Features: Appendices; Figures; References; Tables;
• Pagination: 26p

Subject/Index Terms

• TRT Terms: Algorithms; Asset management; Cluster analysis; Condition surveys; Data analysis; Highway maintenance; Inspection; Maintenance management
• Geographic Terms: Utah
• Subject Areas: Data and Information Technology; Highways; Maintenance and Preservation;

Filing Info

• Accession Number: 01714564
• Record Type: Publication
• Report/Paper Numbers: MPC 19-392
• Files: UTC, TRIS, ATRI, USDOT
• Created Date: Aug 26 2019 10:14AM