ORCID Identifier(s)

ORCID 0009-0004-9964-3010

Graduation Semester and Year

Summer 2025

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Industrial Engineering

Department

Industrial and Manufacturing Systems Engineering

First Advisor

Professor Jay M. Rosenberger

Second Advisor

Professor Victoria C.P. Chen

Third Advisor

Professor Mohsen Shahandashti

Fourth Advisor

Professor Yuan Zhou

Abstract

Water is an essential part of human life. However, there are critical infrastructures that enable water availability in communities and homes. One of such is a water distribution network. Water distribution network performance depends on its reliability, which could be threatened by external agents like earthquakes. When earthquakes occur, they cause damages on some pipes within the distribution network and this limits performance of water distribution network. While earthquakes cannot be prevented, effective maintenance intervention may reduce the impact of earthquakes on water distribution networks. In order to develop an effective maintenance plan, researchers approach it in different ways. However, this dissertation focuses on developing a design and analysis of computer experiments optimization (DACE – Optimization) framework that determines a set of pipes that are critical for water distribution network resilience to rehabilitate under a limited budget. We used Sobol sequence as a design of experiments to develop rehabilitation plans through an algorithm that ensures optimal use of budget. The design of experiments was also used to develop set of earthquake scenarios, which the rehabilitation plans were evaluated through simulation using average systems serviceability index (SSI) as a performance measure. We later used statistical methods to identify a proxy and used the proxy to develop an optimization model for our problem, and it was solved in linear time using GUROBI. Using a series of decision steps, we developed an iterative DACE-Optimization approach for the problem. The framework was applied to a real network. It produced a result that was benchmarked with a genetic algorithm. The framework was eventually validated with 30 water distribution networks.

Keywords

Optimization, Design of Experiments, Data Science, Statistical Modeling, Analysis of Variance, Mathematical Modeling, Network Resilience Optimization, Simulation, Water Distribution Network, Seismic Rehabilitation Optimization

Disciplines

Industrial Engineering | Operational Research | Other Civil and Environmental Engineering

License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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