Estimation of rocking capacity of soil-structure systems using a hybrid inverse solver

Abstract

Among the geotechnical-earthquake community, the rocking concept is being acknowledged as an energy dissipation mechanism that benefits soil–structure systems during strong vibrations. Nevertheless, several involving factors such as geomaterials behavior and superstructure size can make the rocking analysis of soil–foundation–structure systems complicated. The mobilized moment and the dissipated energy can be represented as the two primary performance indicators of the soil–structure systems under strong motions. This study employs an assembled database comprised of a wide range of geomaterials with different stiffness values associated with high-rise structures with different dimensions acquired from the implementation of a dynamic nonlinear elastic-perfect plastic finite element model. This study aims to develop predictive models for the responses mentioned above using the implemented database. To predict the both mobilized moment and dissipated energy, a hybrid gene-expression programming–artificial neural network technique (GEP–ANN) was used. The results show that the GEP model can yield promising predictions with reasonable accuracy. However, the GEP model can be fine-tuned by introducing the hybrid model. The hybrid model decreases the recorded prediction errors by a factor of three for both the mobilized moment and damping ratio as compared to the GEP model. The results show that the predictive models yield a sensible performance power that minimizes the efforts needed for implementation of time-consuming finite element method. This study also deploys a local sensitivity analysis technique to assess how the input parameters are attributed to the target values.