Multimodel structural performance monitoring

Measurements from load tests may lead to numerical models that better reflect structural behavior. This kind of system identification is not straightforward due to important uncertainties in measurement and models. Moreover, since system identification is an inverse engineering task, many models may fit measured behavior. Traditional model updating methods may not provide the correct behavioral model due to uncertainty and parameter compensation. In this paper, a multi-model approach that explicitly incorporates uncertainties and modeling assumptions is described. The approach samples thousands of models starting from a general parameterized finite element model. The population of selected candidate models may be used to understand and predict behavior, thereby improving structural management decision making. This approach is applied to measurements from structural performance monitoring of the Langensand Bridge in Lucerne, Switzerland. Predictions from the set of candidate models are homogenous and show an average discrepancy of 4 to 7% from the displacement measurements. The tests demonstrate the applicability of the multi-model approach for the structural identification and performance monitoring of real structures. The multi-model approach reveals that the Langensand Bridge has a reserve capacity of 30 % with respect to serviceability requirements.

Uncontrolled Keywords

Structural identification, Bridge behavior, Static measurement, multi-model, data interpretation, uncertainties, dynamic behavior