Multi‐fidelity data meta‐learning approach for seismic response prediction of high‐rise shear wall buildings

Abstract Rapid and accurate estimation of seismic responses in city‐scale buildings is critical for post‐earthquake loss assessment and pre‐event identification of vulnerable buildings. However, conventional numerical simulation methods struggle to balance efficiency and accuracy when applied to large‐scale buildings, while existing data‐driven methods often rely on single‐source datasets, limiting generalizability. Numerical simulation data of varying detail (e.g., floor‐ and component‐based models) and field monitoring data form inherently multi‐fidelity datasets, but integrating these heterogeneous sources remains challenging, particularly when different fidelities correspond to different building targets. To address this gap, we propose a multi‐fidelity meta‐learning algorithm that extends deep learning methods for seismic response prediction, demonstrated on multiple high‐rise shear wall buildings. The proposed algorithm enables incremental data learning and model updates and is applied and validated across datasets of varying fidelities, including multiple numerical simulations and field monitoring data. Under small‐sample field monitoring scenarios, the proposed method reduces overall prediction errors by 40.4%, compared to the typical transfer learning approach, demonstrating superior learning capabilities in limited‐data settings. Additionally, to account for inaccuracies and potential noise in acquired structural information inputs under real‐world conditions, the meta‐learning model was trained and evaluated with varying levels of noise based on field monitoring data. Results indicate that the proposed meta‐learning algorithm exhibits strong robustness when handling noisy inputs.