Life-cycle cost profiles selection for different superstructure bridge types

Abstract

During the service life of a given structure, both the initial and long-term costs are added together to provide the total cost of the bridge, resulting in a complete bridge life-cycle cost analysis (BLCCA). Historically, BLCCA has been used to select maintenance strategies but not to perform a comprehensive life-cycle cost profile (LCCP) selection. Therefore, this paper focuses on selecting cost-effective life-cycle cost profiles for different superstructure types using stochastic analysis. Life-cycle cost profile selection for different superstructures for planning purposes is still a research topic that needs development. This research aim is focused on helping to close that gap. Different maintenance and repair working actions were considered for both concrete and structural steel superstructures. Substructures were not included in the analysis. Multiple LCCPs were compared depending on the material and the superstructure type. Monte Carlo simulations were used based on probability distribution functions obtained from a historical pay item database provided by the Indiana Department of Transportation (INDOT). Cost-effective LCCPs for each superstructure type were selected based on stochastic dominance. A sensitivity analysis is also performed for each superstructure type group to identify the more impactful working actions within the life-cycle cost analysis. Life-cycle profiles that involved routine preventive working actions are more cost-effective than options that consider rehabilitation processes. The sensitivity analysis shows that the variable that most affected the results is the discount rate. When only working actions are considered, pay items related to the main structural element to have a higher impact on the mean. Cost-effective Life-cycle profiles obtained for different superstructure types can optimize the working actions scheduled for each type and be a starting point to consider and implement this method into the superstructure selection and the planning process of a project. Results presented in this paper correspond to practices and cost data in Indiana. However, the methodology can be implemented elsewhere using local data available as needed.

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