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Innovative Overload Permitting in Manitoba Allowing a 363 250Kg (GVM) “Superload”


Within the past few years, the demand for hauling heavier loads on highways has increased significantly. Manitoba Infrastructure and Transportation (MIT) has over 1000 Bridge structures on their highway network that are nearing the end of their service life.    Faced with an increasing number of overload permit applications and aging structures, a refined method of load rating is needed to permit overloads efficiently while avoiding damage to bridge structures. The Heavy Haul Industry has developed equipment that can control the distribution of truck loads on bridges. MIT uses AASHTO’s Non‐Standard Gage (NSG) Distribution Factor Method to predict the performance of superstructure elements for bridges. These particular overloads weighing 80% more than the design loads have been reviewed and approved using the NSG method. MIT is the only Agency in Canada that uses the AASHTO Load and Resistance Factor Design (LRFD) Bridge Design Specification and the corresponding Load and Resistance Factor Rating (LRFR) specification for bridge load rating. AASHTO’s Bridgeware rating program (Virtis) employs the NSG method for load rating and using this method, MIT was able to issue permits for safely moving three transformers each weighing 357 000 kg (Gross Vehicle Mass, GVM). Actual GVM’s for each load were also confirmed using portable scales. Field monitoring and measurements were undertaken in order to compare the structure’s actual performance against theoretical values. This Paper will discuss the details of the load rating analysis using the Non‐Standard Gage Distribution Factor Method, the instrumentation used for monitoring, and the results of the comparison between actual and theoretical values. By utilizing Virtis, MIT rated the bridges along a predetermined route and obtained Operating Rating Factors which were greater than one; hence the transformers were successfully moved and upon inspection, the structures behaved as predicted.

Conference Paper Details

Session title:
Geoffrey C. Oramasionwu
Marta E. Flores‐Barrios
Ryan M. Sanders