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Title:Hotspot and Sampling Analysis for Effective Maintenance Management and Performance Monitoring
Authors:Xiaoyue Cathy Liu and Zhuo Chen
University:University of Utah
Publication Date:Jul 2019
Report #:MPC-19-392
Project #:MPC-528
TRID #:01714564
Keywords:algorithms, asset management, cluster analysis, condition surveys, data analysis, highway maintenance, inspection, maintenance management



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.

How to Cite

Liu, Xiaoyue Cathy, and Zhuo Chen. Hotspot and Sampling Analysis for Effective Maintenance Management and Performance Monitoring, MPC-19-392. North Dakota State University - Upper Great Plains Transportation Institute, Fargo: Mountain-Plains Consortium, 2019.

NDSU Dept 2880P.O. Box 6050Fargo, ND 58108-6050