Element-Based Multi-Objective Optimization Methodology Supporting a Transportation Asset Management Framework for Bridge Planning and Programming

Abstract

The U.S. Moving Ahead for Progress in the 21st Century Act (MAP-21) mandates the development of a risk-based transportation asset management plan and the use of a performance-based approach in transportation planning and programming. This paper introduces a systematic element-based multi-objective optimization (EB-MOO) methodology integrated into a goal-driven transportation asset management framework to improve bridge management and support state departments of transportation with their transition efforts to comply with these MAP-21 requirements. The methodology focuses on the bridge asset class and is structured around five modules: data processing, improvement, element-level optimization, bridge-level optimization, and network-level optimization modules. It relies on a leading-edge forecasting model, three separate screening processes (i.e., the element deficiency, alternative feasibility, and solution superiority screening processes) to overcome computer memory and processing time limitations, and a simulation arrangement to generate life-cycle alternatives (series of improvement actions). Additionally, the EB-MOO methodology consists of three levels of optimization assessment based on the Pareto optimality concept: element-level, bridge-level, and network-level (following either a top-down or bottom-up approach). A robust metaheuristic genetic algorithm handles the different multi-objective optimization problems. A prototyping tool was developed for the implementation of the methodology through several examples of unconstrained and constrained (by budget, performance, or both) scenarios. Results reveal the capability of the methodology to generate Pareto optimal or near-optimal solutions, predict performance, and determine funding requirements and short- and long-term intervention strategies detailed at the bridge-element level for planning and programming. The EB-MOO methodology can also be expanded to accommodate other asset classes or modes.

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“…Thus, optimization of budget allocations is critical. Naji et al ( 33 ) introduced a systematic element-based multiobjective optimization (EB-MOO) methodology that is structured around five modules: data processing; improvement; element-level optimization; bridge-level optimization; and network-level optimization modules. EB-MOO incorporates a leading-edge forecasting model, three separate screening processes, and a simulation arrangement to generate life cycle alternatives, which results in varying budget scenarios with competing objectives that can be examined to facilitate decision-making.…”