A Risk-Based Framework for Optimizing Inspection Planning of Utah Culverts [Research Brief]

Rashidi, Abbas; Mohammadi, Pouria; Sherafat, Behnam

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A Risk-Based Framework for Optimizing Inspection Planning of Utah Culverts [Research Brief]

2023-07-01
By Rashidi, Abbas ; Mohammadi, Pouria ; Sherafat, Behnam

File Language:

English

Details

Creators:

Rashidi, Abbas ; Mohammadi, Pouria ; Sherafat, Behnam
Corporate Creators:

University of Utah ; Mountain-Plains Consortium
Corporate Contributors:

United States. Department of Transportation. University Transportation Centers (UTC) Program
Subject/TRT Terms:
Publication/ Report Number:

MPC-692
Resource Type:

Brief
Geographical Coverage:

Utah ; United States
Edition:

Research Brief
Corporate Publisher:

Mountain-Plains Consortium
Abstract:

Researchers developed a robust framework to enhance culvert inspection planning in Utah as the first step in developing the CMS. The objective of this study is to assist UDOT in establishing a comprehensive CMS by creating a Utah culvert management manual. To achieve this, the researchers identified the culvert deterioration curves based on historical data from Utah, Colorado, and Vermont. These curves are then employed by a risk-based framework for life cycle analysis to estimate the frequency of culvert inspections and service life. The proposed method for determining culvert deterioration curves and enhancing inspection planning involves using machine learning algorithms, including support vector regression (SVR) and random forest regression (RFR), as well as a risk assessment approach. The culvert life cycle was analyzed for risk assessment to consider associated risks during the culvert life cycle and estimate future risks. The developed solution is intended to be integrated into the Atom software, which is used to manage assets and maintenance for UDOT.
Format:

PDF
Funding:

MPC-23-501
Download URL:

https://rosap.ntl.bts.gov/view/dot/68537/dot_68537_DS1.pdf
File Type:

[PDF - 363.24 KB]
Collection(s):

Artificial Intelligence Deployment Center of Excellence (AI COE) ; University Transportation Centers
Main Document Checksum:

urn:sha-512:f7603a99ff33ae77ecbf9e54fcaac13ab63755f6192e0321f790e80ef5645bebc2ad484c5b019c4efbc1715a19216ea00aeb541fd3128cdaec0222cadbf6c68c

File Language:

English

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