## 6 ATT-BiLSTM and SVR Repair Accuracy Evaluation
### ATT-BiLSTM and SVR Accuracy Validation

Table 6: Correlation matrix for all original data and all data after ATT-BiLSTM repair and SVR repair. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Type** & **Deflection Monitoring Point** & **Point1\_Data** & **Point2\_Data** & **Point3\_Data** \\ \hline \multirow{3}{*}{Original anomaly data} & Point1\_data & 1.00000 & 0.962409 & 0.954059 \\  & Point2\_data & 0.962409 & 1.000000 & 0.960798 \\  & Point3\_data & 0.954059 & 0.960798 & 1.000000 \\ \hline \multirow{3}{*}{Anomalous data repaired by ATT-BiLSTM} & Point1\_data & 1.000000 & 0.989927 & 0.954059 \\  & Point2\_data & 0.989927 & 1.000000 & 0.985518 \\ \cline{1-1}  & Point3\_data & 0.954059 & 0.985518 & 1.000000 \\ \hline \multirow{3}{*}{Anomalous data repaired by SVR} & Point1\_data & 1.000000 & 0.985372 & 0.954059 \\ \cline{1-1}  & Point2\_data & 0.985372 & 1.000000 & 0.985186 \\ \cline{1-1}  & Point3\_data & 0.954059 & 0.985186 & 1.000000 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Original, ATT-BiLSTM-repaired, and SVR-repaired correlation matrix for anomalous data. For the correlation between colf3_data and colf2_data, the ATT-BiLSTM model enhances the correlation from 0. The range of values is from 982,825 to 0.985148, while the SVR model improves it to 0.983359, which is also larger than that of the SVR model. This indicates that, compared to the SVR model, the ATT-BiLSTM model, in terms of the overall data correlation after repairing the anomalous data, is capable of more accurately recovering the correlation between colf1_data and colf2_data and between colf3_data and colf2_data. (3) To further quantify this advantage, the score_SVR and score_ATT-BiLSTM scores were calculated. The two scores under consideration represent the SVR and ATT-BiLSTM model scores, respectively. These scores are obtained by comparing the correlation coefficients of the repaired data and the corresponding features in the anomalous (or raw) data and accumulating the differences. The mathematical formulas for score_SVR and score_ATT-BiLSTM can be expressed as follows:  
\[score\_SVR=\sum_{i=1}^{n}\sum_{i=1}^{n}\left(corr\_SVR[i,j]-corr[i,j]\right) \tag{11}\]  
\[score\_ATT-BiLSTM=\sum_{i=1}^{n}\sum_{i=1}^{n}\left(corr\_ATT-BiLSTM[i,j]- corr[i,j]\right) \tag{12}\]  
where \(n\) is the number of features, \(corr\) is the correlation matrix between the features of the original data, \(corr\_SVR\) is the correlation matrix between the features of the predicted results of the SVR model, and \(corr\_ATT-BiLSTM\) is the correlation matrix between the features of the ATT-BiLSTM repaired data. The discrepancy between the correlation matrix of the SVR predictions and the correlation matrix of the original data was calculated, and these differences were summed up to obtain \(score\_SVR\) = 0.0477. In a similar manner, for the ATT-BiLSTM-repaired data, the discrepancy between the repaired correlation matrix and the correlation matrix of the original data was calculated. These discrepancies were then summed to obtain \(score\_ATT-BiLSTM\) = 0.052. The specific scores are displayed in Figure 10.  
The calculation of these two scores is performed in a manner that quantifies the discrepancy between the repair effects of SVR and LSTM. The results of this calculation clearly demonstrate that the repair of anomalous data by ATT-BiLSTM is superior to that of the SVR model. Therefore, the ATT-BiLSTM model proposed in this paper has been demonstrated to possess both reliability and high accuracy when utilized for data repair. Figure 10: Comparison of repair accuracy scores for SVR and ATT-BiLSTM models.