A training-efficient Wiener-type neural network modeling approach for structural dynamic response prediction

Abstract

Neural network techniques have been widely exploited to model structural dynamics. Among them, the continuous-time state-space neural network (CSNN) possesses great potential because of its advantage that a trained CSNN model can operate at different sampling rates without retraining. However, it has relatively low training efficiency due to the integration operations involved. To address this limitation, this study proposes a Wiener-type neural network (WNN) based on CSNN by reducing the nonlinear state derivative calculator present in CSNN to a linear equation. Based on this modification, an explicit state expression for WNN that discards high-order differentiable items for back propagation is derived, which not only facilitates rapid computation of the state variable but also greatly enhances training efficiency. The effectiveness of WNN is assessed through a numerical example of a cubic-stiffness structure, an on-site measurement example of a 6-story hotel building, and an experimental example of a magneto-rheological fluid damper. WNN models are compared with different models for these examples, and the results indicate that WNN models achieve high and consistent prediction accuracy, with Pearson correlation coefficients exceeding 0.85 and normalized root mean square errors below 0.06 across all cases. Meanwhile, the WNN model exhibits up to an 83% reduction in training time relative to the CSNN model. The developed WNN effectively balances prediction accuracy and training efficiency, making it a promising approach for dynamic modeling of large and complex systems.