Concrete gravity dams model parameters updating using static measurements

Abstract The structural control of concrete gravity dams is of primary importance. In this context, numerical models play a fundamental role both to assess the vulnerability of gravity dams and to control their behaviour during normal operativity and after extreme events. In this regard, data monitoring represents an important source of information for numerical model calibrations. This study proposes a novel probabilistic procedure, defined in the Bayesian framework, to calibrate the parameters of finite elements models of dams. To this aim, monitoring data and the results of material tests are used as reference information. The computational burden is reduced by using a new hybrid-predictive model of the dam displacements. An application on an Italian dam shows the feasibility of the proposed procedure.

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