Addressing the Feasibility of Employing NDE Data for Bridge Condition Assessment Using Gaussian Process Regression [Research Brief]

Atadero, Rebecca; Guo, Yanlin; Elnahla, Mahmoud

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Addressing the Feasibility of Employing NDE Data for Bridge Condition Assessment Using Gaussian Process Regression [Research Brief]

2025-10-01
By Atadero, Rebecca ; Guo, Yanlin ; Elnahla, Mahmoud

File Language:

English

Details

Creators:

Atadero, Rebecca ; Guo, Yanlin ; Elnahla, Mahmoud
Corporate Creators:

Colorado State University
Corporate Contributors:

Mountain-Plains Consortium ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
Subject/TRT Terms:

Asset Management Bridges Condition Surveys Data Analysis Evaluation Freeze Thaw Tests Infrastructure Life Cycle Costing Nondestructive Tests
Publication/ Report Number:

618
Resource Type:

Brief
Geographical Coverage:

United States ; North Dakota
Edition:

Research Brief
Corporate Publisher:

Mountain-Plains Consortium
Abstract:

This project tested whether limited non-destructive evaluation (NDE) data can be used to predict bridge deck deterioration more effectively. NDE tests, such as Impact Echo, provide objective measurements of deck damage, but they are costly and not collected regularly. Using bridge data from the Federal Highway Administration’s Long-Term Bridge Performance program, the research team applied a statistical learning model to estimate deck delamination. The model was trained on 20 sets of NDE records and included variables such as deck thickness, traffic levels, freeze-thaw cycles, and design details.
Format:

PDF
Funding:

MPC 25-579
Collection(s):

University Transportation Centers
Main Document Checksum:

urn:sha-512:d5fff6e2c7e554e1fef84f65801d981c05da935862ae326944c5613424781e1d93ea1633bcc73822f67f26c7d4a351470baf7b5cacd658ec0cb510ea7080a392
Download URL:

https://rosap.ntl.bts.gov/view/dot/87786/dot_87786_DS1.pdf
File Type:

[PDF - 808.95 KB ]

File Language:

English

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