An adaptive model order reduction method for nonlinear seismic analysis of civil structures based on the elastic-plastic states

As an approach to evaluating the seismic performance of civil structures, nonlinear seismic analysis faces a challenge in terms of computational efficiency for high-fidelity simulations of large-scale finite element models. In this article, an adaptive model order reduction (AMOR) method is proposed for alleviating the computational burden of nonlinear seismic analysis based on elastic-plastic states. The elastic-plastic states are classified as the initial-elastic, plastic-deforming, and residual-elastic states. The finite element model of a civil structure is automatically partitioned into linear and nonlinear substructures according to the time-varying spatial distribution of the plastic deformation zone, and the Craig-Bampton method was employed to construct the reduced governing equations in each elastic-plastic state. An elastic-plastic-triggered reanalysis method was proposed to conduct a seamless transformation of the reduced governing equations between different elastic-plastic states. In the back-off step, the reduced response vectors are projected back to the physical coordinate space. In the reanalysis step, the response vectors are initialized, partitioned, and transformed onto the hybrid coordinate space spanned by reduced-order bases. Compared with a conventional time step integration method, the AMOR method yields comparative results with a higher computational efficiency.

Automated summary

The article introduces an adaptive model order reduction (AMOR) method for nonlinear seismic analysis to achieve higher computational efficiency by partitioning structures into linear and nonlinear substructures and using simplified governing equations. Compared to traditional time step integration methods, AMOR yields comparable results with higher efficiency.