Numerical Predictions of Surface Settlements in Mechanized Tunneling: Hybrid POD and ANN Surrogate Modeling for Reliability Analyses

Computational reliability analyses of engineering structures are often time-consuming, in particular, if time-dependent structural behavior is considered. If (almost) real time prognoses are required, surrogate models may be used to approximate the structural behavior described by advanced numerical models. In this paper, a hybrid surrogate modeling strategy based on a combination of Proper Orthogonal Decomposition (POD) and Artificial Neural Networks (ANN) is introduced. The hybrid approach is developed for the approximation of time variant surface settlements in mechanized tunneling due to uncertain geological and process parameters. The approximation capabilities are demonstrated by means of an example. The new hybrid surrogate model can be applied for numerical, efficient, real-time reliability analyses in mechanized tunneling.