Degree Type

Dissertation

Date of Award

2019

Degree Name

Doctor of Philosophy

Department

Civil, Construction, and Environmental Engineering

Major

Civil Engineering

First Advisor

R C. Williams

Abstract

Maintenance, rehabilitation, and reconstruction of highway system are the major expenses

in a state general expenditure. The emergence of predicting pavement performance and detecting

the current state of the pavement health encourages pavement agencies to develop an

accurate, efficient, and intelligent model to predict the remaining life of a pavement. Relating

pavement condition, surface distresses, and structural properties, to a set of predictors including

material properties, traffic loading, environmental factors, etc. through mathematical

expressions is called performance modeling. To measure and predict pavement performance, a

reproducible, authoritative, and field calibrated condition evaluating system is required. However,

in the existence of numerous important predictors and their interrelationships, developing

a predictive model for pavement performance is not a trivial task. The present study tackles the

problem of developing a pavement performance predictive model in two ways. First, a machine

learning-based predictive framework is developed based on the laboratory-produced performance

data. The developed framework is implemented to predict the amount of permanent

deformation in asphalt pavement as well as the asphalt pavement dynamic modulus. The developed

framework is then used to solve a performance-based pavement design problem along

with an evolutionary optimization algorithm. In the second approach, the structural behavior

of a gantry crane way pavement at intermodal facilities is investigated by assessing the

interactions between pavement, subgrade, and operational loading conditions. The pavement

structural response to the crane load is measured through the installed strain gages in the field

and used to validate a finite element-based model through an inverse analysis. The validated

model is implemented to predict the fatigue life of the pavement structure as well as maintenance,

rehabilitation and design recommendation for the existing and new crane way pavement

sections.

Copyright Owner

Parnian Ghasemi

Language

en

File Format

application/pdf

File Size

217 pages

Available for download on Thursday, November 19, 2020

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