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

The Moving Ahead for Progress in the 21st 2 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 one transportation asset (i.e., bridges) and is structured around five modules: data processing, improvement, element-level optimization, bridge-level optimization, and network-level optimization. It relies on a leading-edge forecasting model, three separate screening processes (i.e., 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 element 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 large size of multi-objective optimization problems. A tool prototype was developed for the implementation of the methodology through several examples of unconstrained and constrained (budget and performance) scenarios. Results reveal the capability of the methodology to generate Pareto optimal or near-optimal solutions, predict performance, and determine long-term intervention strategies and funding requirements. These capabilities facilitate the trade-off between funding scenarios and performance. The EB-MOO methodology can also be expanded to accommodate other asset classes or modes.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 28p

Subject/Index Terms

Filing Info

  • Accession Number: 01763702
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-01021
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:09AM