A Multi-asset Transportation Infrastructure Asset Management Framework and Modeling for Local Governments

Wang, Zhaohua; Salameh, Ryan; Mildner, Caitlin; Li, Mingshu; Xu, Yihua

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A Multi-asset Transportation Infrastructure Asset Management Framework and Modeling for Local Governments

2019-06-07
By Wang, Zhaohua ; Salameh, Ryan ; Mildner, Caitlin

File Language:

English

Details

Creators:

Wang, Zhaohua ; Salameh, Ryan ; Mildner, Caitlin ; Li, Mingshu ; Xu, Yihua
Corporate Creators:

Center for Transportation, Equity, Decisions & Dollars (CTEDD). University of Texas ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; Georgia Institute of Technology
Corporate Contributors:

United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
Subject/TRT Terms:
Publication/ Report Number:

CTEDD 017-03
Resource Type:

Tech Report
Geographical Coverage:

United States;Georgia
Edition:

Final report; 9/1/2017 – 8/31/2018
Corporate Publisher:

Center for Transportation, Equity, Decisions and Dollars (CTEDD) (UTC)
Abstract:

Local governments (LGs) in the United States are managing 3/4 of 4 billion miles of roadway and more than 1/2 of nearly 600,000 bridges, which are critical transportation infrastructure assets that support the mobility, economy, and homeland security on both the local and national levels. To maintain the aging infrastructure in a state of good repair under shrinking budgets, LGs must adopt a systematic approach to conduct cost-effective maintenance, rehabilitation, and reconstruction (MR&R) instead of relying on subjective individual knowledge and experience. As a result, benefit from the scarce transportation budget could be maximized; and accountability could be demonstrated to the stakeholders.
Format:

PDF
Funding:

CTEDD 017-03
Download URL:

https://rosap.ntl.bts.gov/view/dot/65589/dot_65589_DS1.pdf
File Type:

[PDF - 5.89 MB]
Collection(s):

University Transportation Centers
Main Document Checksum:

urn:sha-512:52a16c59e837e3a634df6ace8991f84aef016cfefe85a3d51aa23eedd94b16df0381f69354e93ac2d302f5860b904c1d6d0df933538a23a5ec86d665c1db8c68

File Language:

English

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