## 6 ATT-BiLSTM and SVR Repair Accuracy Evaluation
### ATT-BiLSTM and SVR Accuracy Validation  
In the evaluation of the ATT-BiLSTM model and the SVR model's anomaly data repair accuracy, the correlation matrix is constructed using Pearson's correlation coefficient. This coefficient helps us to understand the relationship between the two sets of data features and is used to evaluate feature selection, data preprocessing, and model interpretation. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Data Point**} & **Measurement Point 1** & **Measurement Point 3** & **Measurement Point 2 SVR** \\  & **Input Data** & **Input Data** & **Restoration Data** \\ \hline
6 & 12.2500 & 11.6875 & 12.414220 \\
311 & 3.8750 & 2.3125 & 3.614474 \\
312 & 3.5000 & 2.0625 & 2.768712 \\
313 & 3.1875 & 1.6875 & 2.438040 \\
314 & 2.8750 & 1.3750 & 2.078867 \\ \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\
99,199 & 16.1250 & 15.8750 & 16.027738 \\
99,200 & 15.8750 & 15.8750 & 15.763535 \\
99,201 & 15.6250 & 15.5625 & 15.206753 \\
99,202 & 14.9375 & 15.3750 & 14.994968 \\
99,203 & 13.9375 & 15.0000 & 14.723242 \\ Total: 4061 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: SVR model predicts input set and repair set (mm). The correlation matrix is defined as a square matrix in which the elements represent the correlation between the corresponding features. In the upper and lower triangles, the elements on the diagonal are assigned a value of 1. This is due to the fact that each feature is maximally correlated with itself, indicating a perfect positive correlation. The Pearson correlation coefficient, in contrast, is a statistical measure of the degree of linear relationship between two variables. This coefficient ranges from \(-1\) to 1, with 1 indicating a perfect positive correlation between the two variables [31; 32]. The deflection monitoring data were calculated before and after the integration of the restoration. The correlation matrix between the columns of the original and restored data was obtained, and clearly demonstrated the linear relationship between different features in the data, as shown in Tables 5 and 6. As illustrated in the above table, the original anomaly data exhibited a high degree of correlation. When the model selects the training data, the three deflection measurement points with the highest correlation are selected as inputs to ensure that the anomaly data in deflection measurement point 2 can be accurately recognized. A comparison of the repair effects of SVR and ATT-BiLSTM reveals the following findings:  
(1) The ATT-BiLSTM and SVR models are demonstrated to enhance the correlations of the corr-colf1-colf2 and corr-colf2-colf3 datasets following data repair. This outcome indicates that the correlations of the repaired deflection monitoring data on the entire dataset are further enhanced, thereby substantiating the rationality and feasibility of data repair. (2) The ATT-BiLSTM model enhances the correlation between colf1_data and colf2_data from 0.983706 to 0.986342, while the SVR model elevates it to 0.984546. This enhancement is more substantial for the ATT-BiLSTM model compared to the SVR model. \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}{*}{All original data} & Point1\_data & 1.00000 & 0.983706 & 0.978996 \\  & Point2\_data & 0.983706 & 1.00000 & 0.982825 \\  & Point3\_data & 0.978996 & 0.982825 & 1.000000 \\ \hline \multirow{3}{*}{All data after ATT-BiLSTM repair} & Point1\_data & 1.00000 & 0.986342 & 0.978996 \\  & Point2\_data & 0.986342 & 1.000000 & 0.985148 \\ \cline{1-1}  & Point3\_data & 0.978996 & 0.985148 & 1.000000 \\ \hline \multirow{3}{*}{All data after SVR repair} & Point1\_data & 1.00000 & 0.984546 & 0.978996 \\ \cline{1-1}  & Point2\_data & 0.984546 & 1.000000 & 0.98359 \\ \cline{1-1}  & Point3\_data & 0.978996 & 0.983359 & 1.000000 \\ \hline \hline \end{tabular}
\end{table}