Abstract
Although survival analyses have long been used in biomedical research, their application to engineering in general, and bridge engineering in particular, is a more recent phenomenon. In this research, survival (reliability) of bridge superstructures in Wisconsin was investigated using the Hypertabastic accelerated failure time model. The 2012 National Bridge Inventory (NBI) data for the State of Wisconsin were used for the analyses. A recorded NBI superstructure condition rating of 5 was chosen as the end of service life. The type of bridge superstructure, bridge age, maximum span length (MSL) and average daily traffic (ADT) were considered as possible risk factors in the survival of bridge superstructures. Results show that ADT and MSL can substantially affect the survival of bridge superstructures at various ages. The reliability of Wisconsin superstructures at the ages of 50 and 75 years is on the order of 63% and 18%, respectively, when the ADT and MSL values are at Wisconsin’s mean values.
Highlights
Featured Application: The methodology provided in this article can be used for probabilistic assessment of service life of bridge superstructures based on survival analysis of long-term field inspection data from various geographic locations
The 2012 National Bridge Inventory (NBI) records were used for statistical analysis and parameter estimations for the survival model, considering a recorded superstructure rating of 5 as the end of service life
At ages older than 40, the superstructure failure rate increases are nearly linear with respect to age
Summary
Featured Application: The methodology provided in this article can be used for probabilistic assessment of service life of bridge superstructures based on survival analysis of long-term field inspection data from various geographic locations. There is substantial interest in seeking better design, maintenance and rehabilitation methods to achieve longer service lives. Durability-based design, preventive maintenance, and structural health monitoring can provide important long-term benefits in a variety of bridge types [1,2,3,4]. Optimal bridge management strategies require effective probabilistic assessment of future bridge conditions based on reliability analyses. Such reliability analyses benefit from understanding and quantifying the existing field performance data as influenced by various parameters
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