Probabilistic evaluation of road bridges under heavy load platforms

Abstract

Road bridges are one of the most important infrastructure assets for global transportation. With the growing freight demands, traffic loads on bridges are also increasing. Specifically, the increase of heavy load platforms (superloads) may impose an unacceptable risk for existing bridges. Because these events are statically-significant, they offer a unique opportunity to determine the true behaviour of the bridge. Notably, measurements from these events can be used to determine statistical parameters which are commonly used in reliability assessement. These measurements can also be very useful for calibrating a model of the bridge, as may be used for bridge assessment. Intuitively, heavy vehicle crossings have inherit randomness in their properties ﾖ such as nominal axle loads, vehicle speed, and travel paths. This paper evaluates the variations in model updating performance when considering the random nature of heavy load platforms movements. This paper also provides statistical parameters determined based on measurements for reliability assessment. For this work, bridge load effects were measured for multiple heavy load platforms across a reference bridge. Statistical evaluation of the bridge load effects of multiple heavy load platforms is conducted. Grillage analysis is used to simulate the crossing of these heavy load platforms. Following this, model updating is conducted for each heavy load platform measurement by tuning key model parameters (e.g., longitudinal stiffness of beams). The variations in the updated models (i.e., updated parameters and model outputs) are then evaluated. The findings should inform users on the variability in bridge load effect due to heavy load platforms and its influence on bridge model errors.