Reliability‐based optimization of a prestressed concrete roof girder using a surrogate model and the double‐loop approach

Abstract

Abstract The paper describes the reliability‐based optimization of a TT‐shaped precast roof girder produced in Austria. An extensive experimental programme was performed using laboratory specimens to gain information on the mechanical fracture parameters of the utilized concrete. Subsequently, destructive shear tests were performed on scaled‐down and full‐scale girders under laboratory conditions. The experiments helped in the development of an accurate numerical model of the girder. The developed model was consequently used for the advanced stochastic analysis of structural response, followed by reliability‐based optimization, which was used to maximize the shear and bending capacities of the girder and minimize production cost under defined reliability constraints. The enormous computational requirements of the double‐loop optimization approach were significantly reduced by the utilization of an artificial neural network‐based surrogate model instead of the original nonlinear finite element model of the optimized structure.

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