High-fidelity time-series data synthesis based on finite element simulation and data space mapping

Abstract

Dynamic responses can provide rich information for supporting the entire life cycle of structures, and they can either be measured from actual structures or simulated using the finite element (FE) method. For the FE simulation, insufficient fidelity of simulation data can significantly affect the confidence of analysis results, while FE model updating methods can partially address this problem by reducing the simulation error. However, most FE model updating methods inevitably update the hyperparameters of FE models using sophisticated algorithms with high computational complexities. Thus, one question was raised: whether there is a mapping that can transfer the FE simulation data to the corresponding measurement data directly without performing FE model updating? To achieve this, we proposed a data synthesis method using FE simulation and deep learning space mapping, which can be used to synthesise high-fidelity dynamic responses excited by some unseen load patterns in the measurement. A Dilated Causal Convolutional Neural Network (DCCNN) was designed for realising space mapping. Vibration experiments were conducted on both an I-shaped steel beam and the corresponding FE model to establish datasets and test the proposed method. The quality of the synthetic data was analysed in both the time domain and the frequency domain. The accurate amplitudes, natural frequencies, and mode shapes of the synthetic data successfully demonstrate the effectiveness of the proposed high-fidelity data synthesis method.