Details:

Creators:
Kim, Y. Richard ; Hummer, Joseph E. ; Gabr, Mohammed ; Johnston, David ; Underwood, B. Shane ; ... More +
Kim, Y. Richard ; Hummer, Joseph E. ; Gabr, Mohammed ; Johnston, David ; Underwood, B. Shane ; Findley, Daniel J. ; Cunningham, Christopher M. Less -
Corporate Creators:
North Carolina State University. Dept. of Civil, Construction, and Environmental Engineering
Corporate Contributors:
North Carolina. Dept. of Transportation. Research and Analysis Group
Subject/TRT Terms:
[+]
Automatic Data Collection Systems
Bridges--North Carolina
Roads--North Carolina
Asset Management
Bridges
Data Collection
Inventory
Roads

Publication/ Report Number:
FHWA/NC/2008-15
Resource Type:
Tech Report
Geographical Coverage:
North Carolina
OCLC Number:
664030830
Edition:
Final report; Apr. 2008-Dec. 2008.
Corporate Publisher:
North Carolina. Dept. of Transportation. Research and Analysis Group
NTL Classification:
NTL-ECONOMICS AND FINANCE-ECONOMICS AND FINANCE ; NTL-HIGHWAY/ROAD TRANSPORTATION-Bridges and Structures ; NTL-HIGHWAY/ROAD TRANSPORTATION-HIGHWAY/ROAD TRANSPORTATION ; NTL-PLANNING AND POLICY-PLANNING AND POLICY ; NTL-REFERENCES AND DIRECTORIES-Statistics ;
Abstract:
An efficient and accurate inventory of a state highway agency’s assets, along with the means to assess the condition

of those assets and model their performance, is critical to enabling an agency to make informed investment decisions

in a Transportation Asset Management (TAM) environment. Today, new technologies provide fast and improved

ways to gather, process, and analyze data. The key is to identify and gather the most useful, reliable, cost-effect

information and use it to make informed decisions for asset management.

Four key infrastructure areas have been identified as primary asset components; pavements, bridges, geotechnical

features, and roadside appurtenances. Each area contains multiple categories and data elements important for sound

decision making. Although some similarities exist in these four primary categories, the nature of data collection may

differ, depending on the asset type. The, sheer number of data elements and the length of asset networks for

pavements and roadside appurtenances render the automated highway speed data collection method a necessity

rather than a luxury. However, the discrete nature of bridges and geotechnical features make the automated mobile

data collection method on a network level unfeasible with today’s technology.

Important issues in the collection process include precision, subjectivity and variability of the process itself, as well

as speed, safety of the survey crew, proximity of the public, cost, etc. Although previous research has attempted to

address these issues and determine the most appropriate method(s), the question remains as to which roadway data

collection system is best for state highway agencies given real world constraints. This research set up a “sealed

envelope” experiment wherein the identification, location, description, and quality of the asset data elements are

known only to NCSU researchers. Vendors are informed of only the data necessary to perform their evaluation. To

support this effort at 95-mile test course near Raleigh, North Carolina was identified, which contained a sampling of

pavement, roadside, geotechnical and bridge elements. This document reports on the findings from the study.


Format:
PDF
Collection(s):
US Transportation Collection
Main Document Checksum:
[+]
urn:sha256:40705cb4cf0cf9dfa0e4d4cda2ada79de09975ffc65925ddede51eed61e005a2
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