Citation

Goyal R, Whelan MJ, Cavalline TL. Struct. Infrastruct. Eng. 2017; 13(7): 894-905.

Copyright

(Copyright © 2017, Informa - Taylor and Francis Group)

DOI

10.1080/15732479.2016.1217888

PMID

unavailable

Abstract

Within asset management of infrastructure systems, increases in maintenance needs subject to budgetary constraints have motivated the development of tools to forecast deterioration to optimise maintenance intervention. Current bridge deterioration modelling approaches, including the evolving duration-based methods, routinely rely on a priori categorisation of bridges based on design, functional, and geographic factors to account for their effects on deterioration rates. However, such preclassification is often based on engineering judgement and may not reflect the true influence of these explanatory factors. In the current study, a proportional hazards regression-based methodology was developed to identify the most critical factors affecting deterioration using the entire unsegmented bridge database. The framework designed to perform this duration-based regression on large bridge databases is presented in this paper and results from implementation on a state inventory of over 17,000 bridges are discussed. The results provide insight into the extent that explanatory factors influence deterioration rates of different bridge components. A novel aspect of the developed framework is its ability to analyse the time-dependent effects of explanatory factors on deterioration rates over the lifecycle of the structural components. This analysis can be used to develop multivariate deterioration models and inform decision-making and prioritisation strategies.

Language: en

Keywords

Bridge deterioration models; bridge deterioration rates; bridge management systems; multivariate proportional hazards regression; survival analysis