Nonlinear finite element model updating and fourier neural operator for digital twin of reinforced concrete containment vessel under seismic excitations

Abstract

A series of unidirectional seismic excitation tests on a 1/20 scaled reinforced concrete containment vessel (RCCV) specimen was conducted using a 6-DOF shake table. This study presents the development of a digital twin to accurately capture the complex behavior of reinforced concrete structures under seismic effects. With the aid of finite element (FE) software Abaqus, the RCCV structure was initially modeled and analyzed to obtain preliminary structural acceleration and displacement results. To enhance the accuracy of these simulations, a nonlinear FE model updating framework was developed by integrating Abaqus software with Python. The model updating of RCCV was conducted subsequently based on the shake table measured data to obtain results with significantly improved accuracy. It is shown that the FE simulations and the nonlinear FE model updating algorithm are highly effective for the high-fidelity simulation of large and complex structures. Furthermore, a Fourier neural operator (FNO) model is used to learn the FE simulation database and predict the FE simulation results. It is observed that FNO has an excellent performance in predicting the structural responses with various material and damping parameters and accomplished speedup over 5000 times compared to the FE simulations. The findings provide a basis for the application of nonlinear model updating algorithms at the structure system level through the high-performance GPU-based parallel computing and underscore that the strategy of FE analysis combined with artificial intelligence (AI) has great potential in the field of digital twins for large and complex structures.