Date of Award
Program or Major
Doctor of Philosophy
The Moving Ahead for Progress in the 21st Century Act (MAP-21) mandates the development of a risk-based transportation asset management plan and use of a performance-based approach in transportation planning and programming. This research introduces a systematic element-based multi-objective optimization (EB-MOO) methodology integrated into a goal-driven transportation asset management framework to
(1) improve bridge management,
(2) support state departments of transportation with their transition efforts to comply with the MAP-21 requirements,
(3) determine short- and long-term intervention strategies and funding requirements, and
(4) facilitate trade-offs between funding levels and performance.
The proposed methodology focuses on one transportation asset class (i.e., bridge) and is structured around the following five modules:
1. Data Processing Module,
2. Improvement Module,
3. Element-level Optimization Module,
4. Bridge-level Optimization Module, and
5. Network-level Optimization Module.
To overcome computer memory and processing time limitations, the methodology relies on the following three distinct screening processes:
1. Element Deficiency Process,
2. Alternative Feasibility Process, and
3. Solution Superiority Screening Process.
The methodology deploys an independent deterioration model (i.e., Weibull/Markov model), to predict performance, and a life-cycle cost model, to estimate life-cycle costs and benefits. Life-cycle (LC) alternatives (series of element improvement actions) are generated based on a new simulation arrangement for three distinct improvement types:
1. maintenance, repair and rehabilitation (preservation);
2. functional improvement; and
A LC activity profile is constructed separately for each LC alternative action path. The methodology consists of three levels of optimization assessment based on the Pareto optimality concept:
(1) an element-level optimization, to identify optimal or near-optimal element intervention actions for each deficient element (poor condition state) of a candidate bridge;
(2) a bridge-level optimization, to identify combinations of optimal or near-optimal element intervention actions for a candidate bridge; and
(3) a network-level optimization, following either a top-down or bottom-up approach, to identify sets of optimal or near-optimal element intervention actions for a network of bridges.
A robust metaheuristic genetic algorithm (i.e., Non-dominated Sorting Genetic Algorithm II, [NSGA-II]) is deployed to handle the large size of multi-objective optimization problems. A MATLAB-based tool prototype was developed to test concepts, demonstrate effectiveness, and communicate benefits. Several examples of unconstrained and constrained scenarios were established for implementing the methodology using the tool prototype.
Results reveal the capability of the proposed EB-MOO methodology to generate a high quality of Pareto optimal or near-optimal solutions, predict performance, and determine appropriate intervention actions and funding requirements. The five modules collectively provide a systematic process for the development and evaluation of improvement programs and transportation plans. Trade-offs between Pareto optimal or near-optimal solutions facilitate identifying best investment strategies that address short- and long-term goals and objective priorities.
Naji, Karim, "Element-Based Multi-Objective Optimization Methodology Supporting a Transportation Asset Management Framework for Bridge Planning and Programming" (2020). Doctoral Dissertations. 2513.