Hotspot and sampling analysis for effective maintenance and performance monitoring.

Liu, Xiaoyue Cathy; Chen, Zhuo; University of Utah. Department of Civil and Environmental Engineering

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ROSA P serves as an archival repository of USDOT-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by USDOT or funded partners. As a repository, ROSA P retains documents in their original published format to ensure public access to scientific information.

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Hotspot and sampling analysis for effective maintenance and performance monitoring.

2017-05-01
By Liu, Xiaoyue Cathy ; Chen, Zhuo

English

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Details:

Creators:

Liu, Xiaoyue Cathy ; Chen, Zhuo

Liu, Xiaoyue Cathy ; Chen, Zhuo Less -
Corporate Creators:

University of Utah. Department of Civil and Environmental Engineering
Corporate Contributors:

Utah. Dept. of Transportation ; United States. Federal Highway Administration

Utah. Dept. of Transportation ; United States. Federal Highway Administration Less -
Subject/TRT Terms:

[+]

Bridge Management Systems Fisher Information High-dimensional Clustering Highway Maintenance Infrastructure Maintenance Management Maintenance Management Pavement Maintenance Pavement Management Systems Spatial Sampling Transportation, Hydraulic And Utility Facilities
Publication/ Report Number:

UT- 17.12
Resource Type:

Tech Report
Geographical Coverage:

Utah ; United States

Utah ; United States Less -
Corporate Publisher:

Utah. Department of Transportation
Abstract:

In this project, we propose two sampling methods addressing “how much and where” the agencies need to collect infrastraucture condition data for accurate Level-of-Maintenance (LOM) estimation in maintenance network with single type or multiple types of infrastructures. The method for single type infrastructure integrates Fisher information with spatial sampling technique that can be customized based on local agencies’ requirements, such as station-balanced, spatially-balanced, or others. For infrastructure condition inspection in a network with multiple types of infrastructures, a high-dimensional clustering-based sampling method is proposed. The method is based on the fact that inspection activities are carried out on the roadway segment basis, and selects sample segments that contain multiple types of infrastructures for the accurate estimation of their respective LOMs. The sampling process consists of two components: current condition estimation and high-dimensional cluster analysis. The methods are implemented using the infrastructure inspection records in the State of Utah from September, 2014 to March 2016. The sampling results indicate that both methods outperform simple random sampling method which is widely used across agencies.
Format:

PDF
Funding:

16-8506, Work Unit No. 5H07622H
Collection(s):

US Transportation Collection
Main Document Checksum:

[+]

urn:sha256:2f07f6a0589295db9de5452b1122eea867dfcbfde61a00ea619e3db7cddaeb3c
Download URL:

https://rosap.ntl.bts.gov/view/dot/32415/dot_32415_DS1.pdf
File Type:

[PDF-1.90 MB]