Integration of Agency Rules with the Preservation Optimization Model in the Pontis Bridge Management System

Abstract

The Pontis® Bridge Management System was initially designed as a tool for performing network-level analyses of bridge needs and work. Although even the earliest versions of the system simulated conditions and work at the bridge level, activities such as model calibration and reporting were designed at the network level. The most recent Pontis version, Release 4, adds significant new capabilities for performing bridge-level analyses, including the ability to invoke agency rules that allow recommendations to simulate agency practices. The intent of the rules is to recognize agency practices and their effects on bridge-level recommendations, while preserving the system’s basic modeling approach. This paper describes the new components of the Pontis modeling approach and analyzes a case that illustrates the effects of applying bridge-level agency rules to both network-level and bridge-level results generated by the Pontis system. Five-year program simulations, with and without agency rules, were performed on a sample set of bridges. The analysis indicates that for the limited sample, applying a typical set of agency rules improved bridge-level recommendations qualitatively by triggering recommendations for less frequent but larger projects than those recommended in the absence of the rules. Further, the rules, particularly those designed to upscope Pontis recommendations on the basis of agency practice, increased the estimate of initial network-level needs by approximately 23%. However, the backlog growth or reduction at a given budget level was not significantly altered by the rules’ application.

Identifying the Factors Impacting Bridge Deterioration in the Gulf Cooperation Council

“…The software was first developed in 1991 and then licensed by AASHTO in 1994 [5,8]. Approximately 50% of the U.S. transportation agencies have adopted PONTIS for their bridge management activities [9]. However, the accuracy of the predicted conditions of bridges might not be reliable enough if the input data are based on visual inspections only [10].…”

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

“…Finally, the payoffs of the two players in prioritizing element MRR were quantified based on a service life extension of bridges when a threshold bridge health index (BHI) was set to be 70. In this study, a threshold BHI of 70 was selected because it is the dividing factor for determining whether a bridge needs preventive maintenance or rehabilitation [30][31][32]. For a fair comparison, the threshold BHI of 60 was used for New York because the observed bridge performance in the state appears to be relatively lower than the other states.…”

Integrated bridge management from 3D-model to network level

“…Examples are SIB-Bauwerke in Germany (Abram 2003), KUBA-MS in Switzerland (Haller & Bascuro, 2006), DANBRO in Denmark (Henriksen, 1999), Eirspan in Ireland (Duffy, 2004), BridgeLife in Finland (Vesikari, 2006 and2008), Pontis (Robert et al, 2003) and BRIDGIT (Hawk, 1999) in the USA, and Ontario Bridge Management System in Canada (Thompson et al, 1999).…”