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ZENODO
Dataset . 2019
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2019
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2019
License: CC BY
Data sources: Datacite
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Development of Metals Corrosion Maps of Arkansas and Maintenance of Cross-Drains

Authors: Hossain, Zahid; Elsayed, Ashraf; Hasan, MdAriful;

Development of Metals Corrosion Maps of Arkansas and Maintenance of Cross-Drains

Abstract

Corresponding data set for Tran-SET Project No. 18GTASU01. Abstract of the final report is stated below for reference: "Corrosion potential of metallic structures in alluvial soils is governed by chemical and electromagnetic properties of the soils. Geotechnical engineers are generally more concerned about different types of soils and their physical and mechanical properties than the chemical aspects. The main objective of this study is to analyze the geotechnical, electrochemical and electromagnetic properties of soils in Arkansas. Important parameters (e.g., soil resistivity) related to corrosion potential of metal culverts have been predicted through neural network (NN) models. The developed NN models have been trained and verified by using laboratory test results of soil samples collected from Arkansas Department of Transportation (ARDOT), and survey data obtained from the United States Department of Agriculture (USDA) and Arkansas Department of Environmental Quality (ADEQ). Finally, the Geographic Information System (GIS) based corrosion risk maps of three different types of metal pipes have been developed based on the available soil properties, metal properties, and water quality data. The developed maps will help ARDOT engineers to assess corrosion potential of metal pipes prior to the new construction and repair projects and use proper culvert and cross drain materials."

Tran-SET Project No. 18GTASU01

Related Organizations
Keywords

Metal Pipe Culvert, Neural Network Model, Corrosion Risk Map, Life Cycle Cost Analysis

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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