Population-based structural identification for reserve-capacity assessment of existing bridges

Abstract

Transportation networks provide an essential contribution to addressing the needs of reliable and safe mobility in urban environments. The core of these networks is made up of infrastructure such as roads and bridges that often, have not been designed to meet current needs. Optimal management requires an accurate knowledge of how existing structures behave. This helps avoid unnecessary replacement and expensive interventions when cheaper and more sustainable alternatives are available. Structural-model updating takes advantage of measurements and more qualitative observations to identify suitable behaviour model classes and values for parameters that influence real behaviour. Error domain model falsification (EDMF) has been proposed as a robust population-based methodology to identify sets of models by comparing finite-element model predictions with measurements at sensor locations. This paper introduces a methodology, which is compatible with EDMF, to assess the reserve capacity of bridges for serviceability and ultimate limit states. A case study—the structural identification of a reinforced-concrete bridge in Singapore—illustrates the framework developed for the estimation of reserve capacity. Several analyses with increasing levels of model detail using design and updated values of relevant parameters are presented. Traffic-load specifications of design-stage codes (British Code—1978) and current codes (Eurocodes) are compared. Results show that typical conservative practices carried out during design and construction have led to an as-built reserve capacity of 60%. A large proportion of the as-built reserve capacity has been exploited to accommodate dramatically increased values of traffic-load specifications that are provided by current Singapore codes which have caused a reduction in reserve capacity to 20%. Such a reduction may be less significant in countries where code specifications have not changed as much. Finally, it is shown that advanced methods of analysis and assessment are more suitable than design-stage approaches to quantify the reserve capacity.