Co-Active Prioritization by Means of Contingency Tables for Analyzing Element-level Bridge Inspection Results and Optimizing Returns

Abstract

An efficient prioritization of bridge actions such as preventive maintenance, rehabilitation, or replacement (MRR) that accounts for inter-element interactions will optimize a long-term return on investments (ROI) in terms of service life extension. What enables this return is the assignment of “Co-Active” elements. This study develops a methodology based on the concept of “Co-Active elements”. The word, “Co-Active”, is used to represent a small group of elements that act together to improve the Bridge Health Index (BHI). The Co-Active parameters for three major bridge groups in Georgia are presented. To illustrate how the Co-Active model works, 1439 in-service bridges’ Element-Level Bridge Inspection results from the state of Georgia in U.S.A., representing a concrete bridge group with six Co-Active elements, are studied. The analysis results indicate that the overall BHI improves by 20% over the subsequent 20 years when expansion joints are replaced. The effects of Co-Active elements on the BHI predictions are quantifiable and depend on factors such as the timing of MRR, the condition of bridge elements as well as the type of MRR. Furthermore, it is concluded that inter-dependent relationships among Co-Active elements are highly affected by Co-Active coefficients. They increase when the degree of dependency among elements increases. Finally, the proposed bridge Co-Active prioritization analysis accounts for a performance target and associated gaps and thus is able to identify critical elements that affect bridge service life the most.

A Strategic Move for Long-Term Bridge Performance within a Game Theory Framework by a Data-Driven Co-Active Mechanism

“…This strategic move is implemented in a game theory framework. In computing a time-dependent bridge performance relationship, this study employs a co-active mechanism, which was developed in the previous study [15]. The mechanism accounts for time-dependent bridge element interactions in predicting time-bridge overall performance relationships.…”

“…(Part II) Long-term bridge performance by recognizing dependent and thus co-acting elements (i.e., a co-active approach [15]) with and without a strategic move-improving all joints or conducting joint MRRs. In this part, the depreciation models determined from Part I and the co-active coefficients determined by studying correlations among elements [15] are used to predict the bridge performance resulting from the strategic move.…”

“…Part II identified and computed the co-active parameters that influence the overall bridge health indices (BHIs). The co-active approach was first introduced and described in detail in the previous study [15]. In brief, the co-active model hypothesizes that when one repairs an element, it should reduce the deterioration of the other elements.…”

“…The approach involved in the co-active model [15] is not repeated here in detail, and the process below focuses on describing how a game theory approach is used for optimal decision-making.…”

“…Infrastructures 2020, 5, x FOR PEER REVIEW 6 of 23 HI = Health index for an element in the year, t (e.g., 2018); HI = Health index for an element in the preceding year, t − 1 (e.g., 2017); DF = Depreciation factor calculated for an element (e.g., column) not being maintained, repaired, and rehabilitated; AF = Appreciation factor calculated for an element (e.g., expansion joint) being maintained, repaired, and rehabilitated; and ρ CA = Co-active coefficient between the acting element (e.g., expansion joint) and its co-active elements (e.g., column). The approach involved in the co-active model [15] is not repeated here in detail, and the process below focuses on describing how a game theory approach is used for optimal decision-making.…”