## 4 Case Study
### Anomaly Identification of Uplift Pressure  
Firstly, the measured uplift pressure and the reservoir water level are normalized from \(-1\) to \(1\), and all monitoring points of uplift pressure are identified using the anomaly identification model based on DBSCAN. This model identity that the P2-2 point has been abnormal since March 2016, and several uplift pressure monitoring points have been abnormal since 7 October 2017. These abnormal monitoring points include P2-2, P2-3, P2-4, and P2-5 in Section 3# as well as P6, P7, and P8 in the vertical section. The other measuring points are normal. Taking the P2-3 monitoring point as an example, the anomaly identification results of the uplift pressure based on the DBSCAN model are shown in Figure 7. Additionally, 6 abnormal occurrence points (noise points) from 7 October 2017 to 14 October 2017 and 4 abnormal occurrence points from 15 September 2019 to 19 September 2019 are identified by the DBSCAN model. At the same time, the new cluster #5 - cluster #12 after the abnormal occurrence points are identified as abnormal stabilization values of uplift pressure. According to its P2-3 measuring process line, the abnormal occurrence points approximate corresponds to the initial stage of the abnormal process, and cluster #5 - cluster \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Density (kN/m3)** & **Modulus of Elasticity** & **Poisson** & **Coefficient of Linear** \\  & & **(GPa)** & **Ratio** & **Extensibility (\(U^{\circ}\)C)** \\ \hline Dam concrete & 24 & 20 & 0.167 & \(0.83\times 10^{-5}\) \\ Foundation above 865 m elevation & 27 & 10 & 0.27 & \(1\times 10^{-5}\) \\ Foundation below 865 m elevation & 27 & 15 & 0.21 & \(1\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Material properties of the dam and foundation. Figure 6: Layout of uplift pressure monitoring points in concrete arch dam. #12 approximate corresponds to the stable stage of the abnormal process. The anomaly identification results of other monitoring points are similar to those of P2-3. At the same time, based on the monitoring data of the P2-3 measuring point and the upstream water level, local outlier factor and K-means are used to identify the anomaly of uplift pressure monitoring data to compare with the DBSCAN. The identification results of K-means are different from the actual uplift pressure anomaly, mainly because the distribution distance between abnormal measured values and normal measured values is close, so the clustering method based on distance alone cannot identify the occurrence of abnormal uplift pressure. Otherwise, six abnormal uplift pressures were identified by the local outlier factor, which is less than all abnormal uplift pressure monitoring data. Therefore, the application effect of the anomaly identification model of uplift pressure, based on DBSCAN, a Density-based clustering method, is good, identifying the abnormal occurrence point and abnormal stabilization values. In addition, from the P2-3 measuring curve, it can be seen that abnormal uplift pressure usually corresponds to the rapid growth period of reservoir water level, so it is preliminarily speculated that the rapid growth of reservoir water level is the main reason for abnormal uplift pressure. To verify the accuracy of the anomaly identification result of the DBSCAN, the variation of the annual maximum reduction coefficient of uplift pressure monitoring points in 3# cross section and vertical section is statistically analyzed, as shown in Figure 8. It can be seen that: 1 P2-1 point is located on the upstream side of the grouting curtain. Thus, the measured value of the P2-1 point is approximately equal to the reservoir water level, and the reduction factor is close to 1.0. 2 P2-2 point is located at the downstream side of the grouting curtain. The smallest reduction factor of the P2-2 point was 0.30 in 2015, and the reduction coefficient is more significant and above 0.80 after 2016.