Abstract
The Federal Highway Administration (FHWA) requires that states have less than 10% of the total deck area that is structurally deficient. It is a minimum risk benchmark for sustaining the National Highway System bridges. Yet, a decision-making framework is needed for obtaining the highest possible long-term return from investments on bridge maintenance, rehabilitation, and replacement (MRR). This study employs a data-driven coactive mechanism within a proposed game theory framework, which accounts for a strategic interaction between two players, the FHWA and a state Department of Transportation (DOT). The payoffs for the two players are quantified in terms of a change in service life. The proposed framework is used to investigate the element-level bridge inspection data from four US states (Georgia, Virginia, Pennsylvania, and New York). By reallocating 0.5% (from 10% to 10.5%) of the deck resources to expansion joints and joint seals, both federal and state transportation agencies (e.g., FHWA and state DOTs in the U.S.) will be able to improve the overall bridge performance. This strategic move in turn improves the deck condition by means of a co-active mechanism and yields a higher payoff for both players. It is concluded that the proposed game theory framework with a strategic move, which leverages element interactions for MRR, is most effective in New York where the average bridge service life is extended by 15 years. It is also concluded that the strategic move can lead to vastly different outcomes. Pennsylvania’s concrete bridge management strategy currently appears to leverage a co-active mechanism in its bridge MRR strategies. This is noteworthy because its bridges are exposed to similar environmental conditions to what is obtainable in Virginia and New York and are subjected to more aggressive weather conditions than those in Georgia. This study illustrates how a strategic move affects the payoffs of different players by numerically quantifying changes in service life from bridge time-dependent bridge performance relationships.
Highlights
Transportation asset management often requires a data-driven decision-making process to effectively preserve the long-term performance of transportation assets
This study provides that a co-active mechanism and a simple decision framework, similar to a decision tree method used for machine learning, can help state and federal transportation agencies to numerically quantify long-term returns on MRR investments
It is concluded that co-activeness exists in the element data, and the extent of co-activeness among elements affects the long-term bridge performance
Summary
Transportation asset management often requires a data-driven decision-making process to effectively preserve the long-term performance of transportation assets. For an effective application of these measures, bridge agencies need to implement a bridge management framework that effectively prioritizes MRR actions, most especially for large inventories, which form a network of strategic highway systems within the United States, serving as a major pipeline for the national economy, well-being, and defense. FHWA’s Long-Term Bridge Performance (LTBP) program [4] has developed machine-learning models by mining the historical National Bridge Inventory (NBI) and climate data and employing a deep learning approach. This study provides that a co-active mechanism and a simple decision framework, similar to a decision tree method used for machine learning, can help state and federal transportation agencies (e.g., state DOTs and FHWA in the U.S.) to numerically quantify long-term returns on MRR investments. Other important factors for measuring bridge performance, such as the average life cycle, must be considered
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
