Optimizing the construction planning of airport expansion projects

To address the steady increase in air travel in recent years, there is a pressing need to expand and modernize many of the existing airports in the US that serve more than three million daily passengers. Airport expansion projects often include construction of new terminals, expansion of existing terminals, and construction of new runways and taxiways. These projects often cause disruptions and delays in air traffic due to their impact on the number, length, and capacity of operational airport runways and/or taxiways. On the other hand, FAA regulations that require airport operations be minimally disrupted during expansion projects often lead to an overrun in construction cost. Accordingly, airport and construction planners need to carefully analyze and optimize the construction planning of airport expansion projects in order to minimize construction-related disruptions in airport operations while keeping total construction cost to a minimum. The main goal of this research study is to develop novel models for optimizing the planning of airport expansion projects that provide the capability of quantifying and minimizing the disruptive impact of construction activities on air traffic and minimizing the total construction cost. To accomplish this goal, the research objectives of this study are to develop: (1) a novel optimization model for the planning of airport expansion projects that is capable of minimizing both airport operations disruptions cost and total construction cost; (2) an innovative machine learning methodology that can be used to create robust machine learning models for predicting the impact of alternative airport area closures on flights ground movement time without the need for repetitive and time-consuming simulation computations; and (3) a novel methodology for optimizing the phasing plans of airport expansion project that identifies optimal daily and hourly work plans for all construction activities that strikes an optimal balance between construction-related airport disruptions and construction cost. The performance of the developed models was analyzed using real-life case studies of airport expansion projects. The results of this analysis illustrated the original contributions of the developed models and their novel methodologies for (i) optimizing the planning of airport expansion projects and generating optimal tradeoffs between minimizing construction-related disruptions in airport operations and minimizing total construction costs; (ii) developing optimal schedules for all airport construction phases at different levels of details including daily and hourly work plans; (iii) accurately and efficiently predicting the impact of alternative phasing plans on flights ground movement time during airport construction activities and their associated construction-related disruption costs; and (iv) analyzing and minimizing the impact of air traffic on total construction cost of airport expansion projects. These original contributions and novel capabilities of the developed models are expected to improve the functional performance of operational airports during construction activities and enhance the cost-effectiveness of airport expansion projects.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Civil Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2023

Author dc:creator

• Al-Ghzawi, Mamdouh Yacoub Mamdouh

Contributors dc:contributor

• El-Rayes, Khaled A
• Tutumluer, Erol
• Golparvar Fard, Mani
• Hajj, Ramez

• Airport Expansion Projects
• Construction Optimization
• Machine Learning
• Air Traffic Prediction
• Construction Management
• Planning And Scheduling
• Construction-related Disruptions
• Construction Cost
• Genetic Algorithms
• Airport Planning
• Flights Ground Movement Time
• Airport Area Closures
• Airport Phasing Plans

Statement dc:rights

• Copyright 2023 Mamdouh Yacoub Mamdouh Al-Ghzawi

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/120541

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F120541