Asset management inventory and data collection.

Details

• Creators:
  Kim, Y. Richard ; Hummer, Joseph E. ; Gabr, Mohammed ; Johnston, David ; Underwood, B. Shane ; Findley, Daniel ; Cunningham, Christopher M.
• Corporate Creators:
  North Carolina State University. Department of Civil, Construction, and Environmental Engineering
• Corporate Contributors:
  North Carolina. Dept. of Transportation. Research and Analysis Group
• Subject/TRT Terms:
  • Bridges
  • Roads
  • Asset management
  • Inventory
  • Data collection
  • Automatic data collection systems Bridges--North Carolina
  • Roads--North Carolina
  • Automatic data collection systems
• Publication/ Report Number:
  FHWA/NC/2008-15
• Resource Type:
  Tech Report
• Geographical Coverage:
  North Carolina
• Edition:
  Final report; Apr. 2008-Dec. 2008.
• Corporate Publisher:
  North Carolina. Department of Transportation. Research and Analysis Group
• Description:
  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
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/5731/dot_5731_DS1.pdf
• File Type:
  [PDF - 3.65 MB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:2a17d195aaeabb21c8dbd3cb7e55ce00b3d8554291046c2b146948c3def5fe8e28c8068d6059cfe82d7d07152aa19acc9068763b23f0ca75716230d25f463aa5