Civil Engineering Faculty Publications
Airfield Infrastructure Management Using Network-Level Optimization and Stochastic Duration Modeling
Document Type
Article
Publication Date
2019
Abstract
This paper proposes a facility-specific modeling approach to plan maintenance and rehabilitation (M&R) activities on a network of airport runway pavement facilities. The objective of the modeling approach is to minimize system M&R cost while recommending M&R activities for each runway pavement facility over a planning horizon. To do so, pavement condition forecast is derived from estimating stochastic duration models which capture the inherent uncertainty and dynamics in pavement deterioration and impacts of exogenous factors. Building on the pavement condition forecast, a network optimization-based M&R planning framework is developed which accounts for the interdependence of M&R activities among facilities as reflected in (1) the requirement for aggregate pavement performance and (2) simultaneous implementation of a major M&R action on connected facilities. The budget constraint is also respected. The M&R planning framework with the stochastic duration model-based pavement condition forecast is applied to Chicago O’Hare International Airport. It is found that the proposed approach leads to much reduced M&R cost compared to the state-of-the-practice which does not consider the interdependence of M&R activities among different pavement facilities. On the other hand, accounting for the simultaneous implementation of a major M&R action on connected facilities would substantially increase M&R cost.
Recommended Citation
Noruzoliaee, Mohamadhossein, and Bo Zou. "Airfield infrastructure management using network-level optimization and stochastic duration modeling." Infrastructures 4, no. 1 (2019): 2. https://doi.org/10.3390/infrastructures4010002
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Publication Title
Infrastructures
DOI
10.3390/infrastructures4010002
Comments
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).