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MPC |
| Title: | Hotspot and Sampling Analysis for Effective Maintenance Management and Performance Monitoring |
| Principal Investigators: | Xiaoyue Cathy Liu |
| University: | University of Utah |
| Status: | Completed |
| Year: | 2016 |
| Grant #: | DTRT13-G-UTC38 (MAP21) |
| Project #: | MPC-528 |
| RH Display ID: | 15707 |
| Keywords: | high risk locations, highway maintenance, inspection, maintenance management, mobile applications, monitoring, performance measurement, quality assurance, sampling |
Using the Maintenance Management Quality Assurance (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.
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