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Title: Multi-objective optimization for improved tunneling project management from planning to operation stages
Authors: Guo, Kai
Keywords: Engineering::Civil engineering::Construction management
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Guo, K. (2022). Multi-objective optimization for improved tunneling project management from planning to operation stages. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: Project management aims to achieve better project performance by making some changes in the construction projects, and a lot of activities could be involved in the process, such as project planning, design, and safety management. The key of achieving project performance improvement is to improve particular aspects of the target project, i.e., to realize the project improvement through the optimization of closely related influential factors that can contribute to the desired project performance. Traditionally, the optimization problems in construction projects are often set as single-objective optimization. However, it should be noticed that though many studies focused on the optimization of one main aspect for improving the project management, the project integration, i.e., elements of the project that can be effectively coordinated, is fundamental to achieve the project improvement. Factors in the construction project are closely related to each other, and even one particular aspect is set as the main objective, other factors are inevitably involved. In fact, the construction project has the nature of complexity and uncertainty. The project improvement tasks often involve many different factors, and a conflicting relationship could even exist between the factors. The project improvement-related tasks, such as design, scheduling, safety management, and others, are inherently characterized by solving the conflicts between those objectives to achieve better performance. With the recognition of the importance of considering different objectives simultaneously and the crucial role of the project integration for the project performance, more and more researchers began to investigate the project improvement from the perspective of optimizing the desired multi-objectives at the same time, i.e., the multi-objective optimization (MOO) perspective. However, compared to other industries, the development and applications of MOO for construction projects have not been as fast and wide as expected. The complexity of construction projects necessitates the incorporation of MOO and also poses a huge challenge to solve project problems from the MOO perspective. To promote project management and explore the methods of incorporating MOO into the construction industry, this thesis proposes approaches with the integration of MOO methods to improve project performance of tunneling projects covering planning, designing, construction, and operation stages. For the planning stage, a hybrid approach with the integration of real option theory and MOO is proposed to seek the optimized concession period that can benefit both the private and public sectors in a public private partnership (PPP) tunneling project. For the designing stage, a genetic algorithm-based approach is proposed with the attempt of striking a balance between objectives of the cost, comfortable degree and headway for a tunnel alignment project. For the construction stage, an innovative approach with the integration of random forest (RF) and non-dominant sorting genetic algorithm-II (NSGA-II) is suggested for the optimization of tunnel-induced damage mitigation during the tunneling process. For the operation stage, a hybrid approach by incorporating BIM model, evacuation simulation tool (Anylogic), and MOO algorithms is built to find out the possible optimal solutions to improve the evacuation efficiency at metro stations in case of emergency events.
DOI: 10.32657/10356/164561
Schools: School of Civil and Environmental Engineering 
Rights: This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:CEE Theses

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