Date of Award
9-30-2020
Document Type
Thesis
Degree Name
Engineering, MSE
First Advisor
Zahid Hossain
Committee Members
Ashraf Elsayed; Shubhalaxmi Kher
Call Number
LD 251 .A566t 2020 H49
Abstract
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 soil types and their physical and mechanical properties than the chemical properties. 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 validated using laboratory test results of soil samples collected from Arkansas Department of Transportation (ARDOT), survey data obtained from the United States Department of Agriculture (USDA) and Arkansas Department of Environmental Quality (ADEQ). Finally, Geographic Information System (GIS) based corrosion risk maps for 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 assess corrosion potential of metal pipes prior to new construction and repair projects, and consequently use proper culvert and cross drain materials.
Rights Management
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Hasan, Md Ariful, "Neural Network Based Life Cycle Cost Analysis of Metal Culverts Due to Corrosion Risks in Arkansas" (2020). Student Theses and Dissertations. 366.
https://arch.astate.edu/all-etd/366
