Developing condition-based triggers for bridge deck maintenance and rehabilitation treatments

Zhibo Zhang, Purdue University

Abstract

The bridges in the U.S. highway system suffer from deficiencies in both their structural condition and functionality. In an effort to improve the condition of bridges, highway agencies continually seek effective and efficient approaches to maintenance and rehabilitation (M&R) treatments for their bridges. However, one drawback to new approaches is that highway agencies have long relied on the subjective judgment of their engineers to determine the time or condition at which to implement the treatments as well as the types of treatments to be applied. The literature shows that previous researchers mainly focused on time-based M&R strategies, but there have been some efforts toward developing condition-based strategies, such as the Indiana Bridge Management System (IBMS). While IBMS and similar systems were laudable efforts, they also were developed on the basis of the judgment and experience of bridge management personnel and were not data-driven. This dissertation proposes condition-based performance thresholds for bridge deck M&R treatments using data-driven analytical methods. The framework was developed for both deterministic and stochastic situations. Under the former, deterministic statistical models for bridge deterioration and costs were developed. The optimization framework was based on life-cycle agency and user costs, and its performance was demonstrated in this dissertation using data from state-owned bridges in the state of Indiana. Separate analyses were conducted with respect to different climate regions and highway functional classes. Sensitivity analysis was performed to investigate the impacts of changes in the relative weights of agency and user costs and traffic volume on the outcome of the analyses. Under the stochastic situation, hazard-based duration models were developed to estimate the probability distribution of the time spent by a bridge deck in a given condition state. Stochastic life-cycle cost analysis was carried out by measuring and incorporating the uncertainty associated with each evaluation factor. The analysis outcomes from the stochastic analysis were found to be generally consistent with those of the deterministic situation. On the basis of the analysis results, this dissertation recommended modifications to the existing decision tree (DTREE) currently used in the IBMS. The thresholds for specific deck overlay treatments were incorporated, and the logic flows of the existing DTREE were revised to eliminate redundancies and to address other issues. It is expected that this dissertation’s data-driven analysis and results will serve as a resource to bridge management practice by enhancing the decision-making process with respect to the condition-based timing of bridge deck M&R treatments.

Degree

Ph.D.

Advisors

Sinha, Purdue University.

Subject Area

Civil engineering|Transportation planning

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