Hotspot and Sampling Analysis for Effective Maintenance Management and Performance Monitoring

Field inspection is critical to the effective performance monitoring and asset maintenance. Given the constraints on budget and time, the inspection activities thus require significant attention for planning and monitoring. Particularly, sampling technique is needed for each asset item to determine the portion of population to be inspected, frequency at which the inspection should be conducted, and method to be used to collect the information. The new Maintenance Management Quality Assurance (MMQA) Mobile, a mobile application developed for the Utah Department of Transportation (UDOT) maintenance crew to automate the field inspection process, provides an innovative solution for accurately recording and tracking the asset conditions through geotagging the information during inspection. It also provides great data sources and new dimensionality for uncovering the maintenance condition with great level-of-details that was previously impossible to achieve. For example, the signage inventory was collected from September 2014 to March 2015 through MMQA Mobile. There is a total of 67,259 sign assemblies statewide. More than 8,500 defect observations were recorded in the database. Figure 1 illustrates the maintenance network with segments color-coded to represent Level-of-Maintenance during this data collection effort. A snapshot which is a sample zoom-in inspection on the signs in desire/deficient conditions is also shown. Using the MMQA mobile data, this research will identify the defect hotspots within the network. On the basis of another ongoing research for developing the sampling standard for MMQA, this research aims at using this dataset with finer resolution to determine the location and frequency for asset sampling. The previous roadway maintenance segmentation is of different length even for the same station, it poses great challenges for providing a sampling solution that is applicable to all stations and all routes. The Markov Decision Process is developed as a technique in the ongoing research to model the sampling location and frequency. However, the previous segmentation and data resolution issue make it difficult to construct deterioration transition matrix. With the new MMQA mobile data, it allows the sampling method to be accurately developed by fine-tuning the segment/sample unit. Coupled with optimization technique that takes into account budget and time limitation, the final sampling method will provide a comprehensive guidance in both spatial and temporal dimensions to optimize the inspection work flow.

Language

  • English

Project

  • Status: Active
  • Funding: $100000
  • Contract Numbers:

    DTRT13-G-UTC38

  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Project Managers:

    Kline, Robin

  • Performing Organizations:

    University of Utah, Salt Lake City

    College of Engineering, Department of Civil Engineering
    Salt Lake City, UT  United States  84112-0561
  • Principal Investigators:

    Xiaoyue Cathy, Liu

  • Start Date: 20161220
  • Expected Completion Date: 20180731
  • Actual Completion Date: 0
  • Source Data: MPC-528

Subject/Index Terms

Filing Info

  • Accession Number: 01648662
  • Record Type: Research project
  • Source Agency: Mountain-Plains Consortium
  • Contract Numbers: DTRT13-G-UTC38
  • Files: UTC, RiP
  • Created Date: Oct 20 2017 12:05PM