## 4 Conclusions  
Deformation prediction is essential for the healthy operation of large structural buildings. This study investigates a similarity clustering deformation prediction method based on GNSS/accelerometer time-frequency analysis, achieving overall deformation reconstruction through displacement and acceleration predictions. The following conclusions are drawn from the analysis of deformation monitoring data simulated by a shaking table experiment. 1. The monitoring sequences are decomposed into several simple signal components for each monitoring data by the CEEMDAN algorithm, which effectively reduces the adverse effects of the nonlinear and nonstationary characteristics of the monitoring sequences on the subsequent model training. 2. A data response reconstruction is proposed by analyzing time-frequency feature similarity and clustering. The clustered monitoring sequences exhibit complex and distinct characteristics, allowing better capture of key features in the structural deformation process. Shaking table experiment results showed that the proposed deformation prediction framework could offer an average prediction error of RMSE = 0.011 m for predicted GNSS displacement and RMSE = 0.002 m/s2 for predicted acceleration. Figure 18: (**a**) Overall deformation reconstruction; (**b**) Reconstruction Error. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Model** & \(\overline{\mathbf{E}}\) & **R** & **RMSE/m** \\ \hline Overall & CEEMDAN-SC-LSTM & 0.004 & 0.958 & 0.005 \\ Displacement & CEEMDAN-LSTM & 0.018 & 0.281 & 0.020 \\ Reconstruction & LSTM & 0.016 & 0.272 & 0.019 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistics of Reconstructed Displacement Errors from Predicted Acceleration. When compared to the CEEMDAN-LSTM and LSTM prediction models, the proposed model exhibits higher prediction accuracy and reliability. 3. The overall displacement reconstructed by the method proposed in this paper has a strong correlation with the reference displacement, The experimental results show that the RMSE of the displacement reconstructed from predicted acceleration is less than 1 mm, with a reconstruction similarity of over 99%. Furthermore, the overall displacement reconstruction similarity can reach over 95%. The GNSS/accelerometer integrated displacement reconstruction algorithm accurately identifies structural deformation, thereby enhancing the precision and reliability of deformation monitoring. This algorithm can be applied to health monitoring and early warning systems for large structures. In summary, the predictive model proposed in this paper demonstrates significant advantages in predictive performance, providing valuable insights for structural deformation prediction. However, when making predictions, we must pay close attention to the choice of input layer indicator variables, relevant parameters, and network architecture. A reasonable combination of these elements is crucial for enhancing prediction stability and accuracy, while also effectively reducing network training time. This necessitates thorough parameter tuning and network structure design during model construction and training to ensure the model can fully capture the key information in the data and accurately predict structural deformations. It is also important to note that structural deformation results from the interplay of various factors, including but not limited to material properties and external environments. Therefore, we should comprehensively consider these influencing factors when training the network. Future research can further explore how to integrate more physical knowledge and engineering experience to optimize model design, as well as leverage advanced computational techniques and algorithms to improve training efficiency and predictive accuracy. Conceptualization, H.H. ; methodology, H.H. ; writing--review & editing, R.L. and T.X. ; investigation, R.L. ; writing--original draft, H.H. ; Data curation, W.M. and M.D. ; visualization, R.L. and H.W.