CIAM-UTC-REG49

Research Team

PI: Monty Abbas, Virginia Tech

Co-PI: Elise Miller-Hooks, George Mason University

Funding Sources

Virginia Tech Federal Share — $75,000

George Mason University Federal Share — $37,500

Virginia Tech Match Share — $75,000

George Mason University Match Share — $37,500

Total Project Cost — $225,000

Agency ID or Contract Number

69A3551847103

Start and End Dates

01/04/2022 — 07/03/2023

Project Description

In this project, we will build on knowledge gained from this earlier work and our expertise in artificial intelligence (AI), stochastic optimization, and Markovian processes to develop a practical, deployable tool to support decision-makers not only in long-term investment planning but also short-term maintenance and rehabilitation activities that are equally important to protecting our roadways and other critical infrastructure components and lifelines. The developed tool will also account for societal costs due to changes in demographics and land use from SLR, long-term effects of flooding resettlement, and other societal and economic challenges.

The work has three key objectives:

  1. Development of a multi-objective, meta-simulation framework that can be used to evaluate the life-cycle cost and economic benefits for specific rehabilitation and/or infrastructure climate protection policies
  2. Development of metrics, including equity-inspired metrics, to capture the current infrastructure serviceability level and detect and emphasize deteriorated conditions of roadway segments and infrastructure installations/land-use changes that may positively or negatively impact road users and local inhabitants inequitably across different socioeconomic groups
  3. Development of AI-based learning and optimization methods for determination of optimal roadway system rehabilitation and improvement policies, utilizing the meta-simulation framework, while considering feasible alternate options