# A Data Loss Recovery Technique Using EMD-BiGRU Algorithm for Structural Health Monitoring
## 6 Conclusions and Future Work  
This study proposed the EMD-BiGRU method for predicting missing data where the EMD algorithm decomposes acceleration data into many subseries reflecting structural model information, and BiGRU effectively extracts the pre-post correlations of subsequence decomposed by acceleration. Specifically, the proposed EMD-BiGRU method can decompose raw measured signal data into several IMFs and a residual. Every IMF can reflect the complex changes of the measured data in dimensions. Then, the imputation ability of the proposed EMD-BiGRU is evaluated using acceleration monitoring data collected from a three-story building structure. Compared with traditional methods such as EMD-GRU, BiGRU, GRU, GBR, RFR, and SVR, experimental results on A_1, A_2, A_3, and A_4 show that EMD-BiGRU has high accuracy in predicting missing points 5, 10, 15, 20. The main findings of the present study are summarized as follows. 1. In recognition of the influence of EMD, the EMD-BiGRU and single models such as BiGRU, GRU, GBR, RFR, and SVR are investigated. The results show that the proposed EMD-BiGRU method achieves better performance for data imputation and demonstrates that the proposed method effectively captures the dynamic temporal characteristics of acceleration data. 2. With the increasing missing data, the data recovery ability of most algorithms could decrease. EMD-BiGRU has lower errors in MSE, RMSE, and MAE than single algo  
Figure 16: Descriptions of the three-story building structure. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Structural** & \multicolumn{4}{c}{**10 Points**} & \multicolumn{4}{c}{**15 Points**} & \multicolumn{4}{c}{**20 Points**} \\ \cline{2-13}
**Conditions** & **MSE** & **RMSE** & **MAE** & **R2** & **MSE** & **RMSE** & **MAE** & **R2** & **MSE** & **RMSE** & **MAE** & **R2** \\ \hline Case one & 0.0118 & 0.1084 & 0.0842 & 95.81\% & 0.0149 & 0.1221 & 0.0954 & 94.70\% & 0.0235 & 0.1532 & 0.1204 & 91.57\% \\ Case two & 0.0064 & 0.0799 & 0.0623 & 96.71\% & 0.0092 & 0.096 & 0.0746 & 95.27\% & 0.0155 & 0.1243 & 0.0980 & 92.12\% \\ Case three & 0.0119 & 0.1092 & 0.0848 & 95.54\% & 0.0143 & 0.1195 & 0.0937 & 94.49\% & 0.0261 & 0.1617 & 0.1282 & 90.02\% \\ Case four & 0.0093 & 0.0965 & 0.0753 & 96.44\% & 0.0131 & 0.1146 & 0.0894 & 94.99\% & 0.0255 & 0.1610 & 0.1275 & 90.10\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Predicted results of the three-story building structure using the EMD-BiGRU algorithm. rithms such as GRU, GBR, and BiGRU. It indicates that the proposed method has strong robustness and can be promoted on a large scale in practical applications
3. For different structural conditions of a three-story building, the EMD-BiGRU exhibits better data imputation performance. It shows that EMD-BiGRU, as a flexible and data-driven method, is effective for mining measured acceleration data. 4. One of the limitations of this study is that EMD-BiGRU is tested using original acceleration data without considering the effect of noise. In future work, raw acceleration data with the noises are considered to solve the imputation of the missing data. Another future research direction is that the extension of EMD to tackle missing data should be studied in the future. Funding acquisition, investigation, data curation, D.L. ; software, validation, visualization, Y.B. ; data curation, visualization, Writing--original draft, Y.H. data curation, methodology, supervision, validation, L.Z. All authors have read and agreed to the published version of the manuscript. The work described in this paper was supported by the Foundation project of Scientific Research Platform of Hechuan for Chengdu-Chongqing Economic Area Research Base and the Teaching Reform Project of School (Grant No. 19 CRKXJ[G05]), the Scientific Research Program of Chongqing College of Humanities, Science & Technology (Grant No. CQRKZX2020003). Not applicable. The authors would like to thank the anonymous reviewers for their valuable comments on the manuscript.