Files in this item

FilesDescriptionFormat

application/pdf

application/pdfKANG-DISSERTATION-2019.pdf (5MB)Restricted Access
(no description provided)PDF

Description

Title:Development of mechanistic rolling resistance model for pavement life cycle assessment
Author(s):Kang, Seunggu
Director of Research:Al-Qadi, Imad L
Doctoral Committee Chair(s):Al-Qadi, Imad L
Doctoral Committee Member(s):Harvey, John T; Ozer, Hasan; Roesler, Jeffery R; Spencer, Billie F
Department / Program:Civil & Environmental Eng
Discipline:Civil Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):life cycle assessment
life cycle inventory database
sustainability
sustainable infrastructure
rolling resistance
vehicle dynamics
excess fuel consumption
dynamic wheel load
pavement performance prediction
Abstract:As of 2017, the transportation sector accounted for the largest share (29 percent) of the total greenhouse gas (GHG) emissions in the U.S. Nearly 82 percent of the transportation GHG emissions results from the operation of on-road vehicles. An average American drives 26.9 miles per day and nearly 65 percent of freight by weight is transported by trucks, a demand that makes building and maintaining an adequate roadway infrastructure crucial for the national economy. The construction of roadway infrastructure consumes large amount of resources and energy; therefore, mitigating emissions has been a keen interest of the transportation industry to achieve sustainable transportation. Life cycle assessment (LCA) quantifies the environmental impacts of a product. It has been shown that a major component of pavement LCA results from rolling resistance (RR) between vehicle-pavement interaction. This study focuses primarily on the development of mechanistic RR model to capture the impact of excess fuel consumption (EFC) and dynamic wheel load (DWL) caused by pavement roughness. The study begins with the development of regional life cycle impact assessment (LCIA) databases and models for Illinois. The LCIA databases were regionalized based on inventory data obtained from questionnaire surveys and simulations; the databases were supplemented with default models in commercial databases and modified to add spatial and temporal proximity. Due to the nature of large infrastructure construction projects, great amounts of materials and equipment need to be hauled during a pavement’s service life. Based on the multi-parameter hauling model developed in the study, considering the effect of the hauling truck driving cycle may increase the contribution of hauling by up to 4% in the life cycle global warming potential (GWP). A mechanistic multi-degree-of-freedom tractor-trailer model captures not only complex truck dynamic motion excited by roughness, but also the excess fuel consumption (EFC) caused by road roughness and speed. In general, the truck EFC increases with IRI and speed prior to 65 mph. At speeds over 65 mph, the EFC begins to decrease because of a sharp increase in aerodynamic drag force. Finally, vehicle traveling on rough pavement produces dynamic wheel loads (DWLs) that result in additional pavement damage that may lead to a premature pavement failure. This study adopted a tractor-trailer model developed at the Illinois Center for Transportation (ICT) and three-dimensional (3-D) pavement finite element (FE) models coupled with 3-D tire contact stresses to examine the effect of DWLs on pavement performance. The Mechanistic Empirical Pavement Design Guide (MEPDG)’s approach was employed to predict pavement performance that identifies pavement maintenance schedule. Based on the maintenance schedule, an LCA case study was carried out for thin and thick asphalt concrete (AC) pavement sections. The result of the case study indicates that the impact of DWLs may produce probabilistic LCA results for both pavement sections. In addition, high extreme DWLs tend to cause shorter pavement service lives that trigger more frequent rehabilitation treatments, making the maintenance stage contribution high. However, more frequent rehabilitation treatments tend to keep the overall pavement roughness levels low, significantly reducing the use stage impact caused by roughness-induced RR. This study contributes to a more accurate estimation of pavement environmental impacts caused by roughness-induced DWLs through the development of regional LCIA databases, hauling, mechanistic tractor-trailer, and pavement performance prediction models. The results of the study may be used in the transportation industry’s decision-making process to mitigate transportation-related fuel consumption and GHG emissions.
Issue Date:2019-10-28
Type:Text
URI:http://hdl.handle.net/2142/106438
Rights Information:Copyright 2019 Seunggu Kang
Date Available in IDEALS:2020-03-02
Date Deposited:2019-12


This item appears in the following Collection(s)

Item Statistics