Steel Corrosion in Underground Transportation Infrastructure [Research Brief]

Morsy, Amr M.; Janardhanan, Shiv K.; Ebo, Islam A.

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Steel Corrosion in Underground Transportation Infrastructure [Research Brief]

2026-04-01
By Morsy, Amr M. ; Janardhanan, Shiv K. ; Ebo, Islam A.

File Language:

English

Details

Creators:

Morsy, Amr M. ; Janardhanan, Shiv K. ; Ebo, Islam A.
Corporate Creators:

San Jose State University. College of Business. Mineta Transportation Institute
Corporate Contributors:

State of California SB1 2017/2018, Trustees of the California State University Sponsored Programs Administration ; 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 Corrosion Corrosive Materials Deterioration Evaluation Infrastructure Material Aging Mathematical Prediction Soils Steel Structures Testing Transportation System Management Wear
Publication/ Report Number:

2523
Resource Type:

Brief
Geographical Coverage:

United States
Edition:

Research Brief
Corporate Publisher:

San Jose State University. College of Business. Mineta Transportation Institute
Abstract:

This research examined soil resistivity and corrosivity by studying several key factors that influence how buried steel corrodes. Three studies were conducted as part of this research, which used new experimental and analytical methods:(1) experimental evaluation of soil resistivity in controlled, constant conditions using a newly devised experimental protocol to implement a comprehensive experimental program by varying one testing parameter at a time; (2) experimental evaluation of soil resistivity in controlled, variable conditions using a newly piloted experimental setup capable of capturing the variation in soil resistivity in a continuously varying environment; and (3) analytical evaluation of a curated digital database of field corrosion measurements and site parameters, which was used to devise a supervised machine learning prediction model capable of predicting corrosion.
Format:

PDF
Collection(s):

University Transportation Centers
Main Document Checksum:

urn:sha-512:0a2d349dd6296b39d7111f65e8084db5fe4a9c48e99bb73c5d61a264d0094f2e109e3a60f2064001b62d444a4f35afbbb974ae9b0f7aff27998596c57f2a08d5
Download URL:

https://rosap.ntl.bts.gov/view/dot/90733/dot_90733_DS1.pdf
File Type:

[PDF - 939.54 KB ]

File Language:

English

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