Most concrete dams have seepage problems to some degree, so it is a common strategy to maintain ongoing monitoring and take timely repair measures. In order to grasp the real operation state of dam seepage, it is vital to analyze the measured data of each monitoring indicator and establish an appropriate prediction equation. However, dam seepage states under the load and environmental influences are very complicated, involving various monitoring indicators and multiple monitoring points of each indicator. For the purpose of maintaining the temporal continuity and spatial correlation of monitoring objects, this paper used a multi-indicator grey clustering analysis model to explore the grey correlation among various indicators, and realized a comprehensive evaluation of a dam seepage state by computation of the clustering coefficient. The case study shows that the proposed method can be successfully applied to the health monitoring of concrete dam seepage.

The seepage analysis of earth-rock dams is required to define the seepage field as a community of saturated and unsaturated regions. The two complement each other. The motion characteristics of unsaturated seepage are relatively complex, and the influence factors are more than saturated seepage. In this paper, combined with engineering examples, the finite element numerical simulation is used to simulate the variation of the seepage field of the earth-rock dam in saturated and unsaturated seepage, and the wetting line, seepage flow and stress and strain inside the dam are obtained. The data results are reasonable and correct. It has certain reference value in the construction of anti-seepage system of earth-rock dam.

Tunnel seismic prediction(TSP) and ground penetrating radar(GPR) are used in the advance geological prediction of a limestone tunnel. The working principle of two geophysical methods is briefly described. The additional detection work of GPR is carried out in the abnormal area of TSP prediction. The predicted dissolution distribution area of the two methods is relatively consistent. The actual excavation results of the tunnel verify the reliability of the geophysical prediction method. The results show that the velocity of TSP P-wave or S-wave corresponding to the dissolution zone is relatively low, which corresponds to the electromagnetic anomaly with strong amplitude of GPR with hyperbolic event.

Rotation of rotor blades is typically micro-motion and would induce micro-Doppler modulation. However, accurate detection will become extremely difficult when the echo signal is corrupted by strong noise. This paper proposes a method to realize extraction of micro-Doppler feature from the rotating rotor blade echo signals with low signal-to-noise ratio (SNR). The proposed method is characterized by applying low-rank decomposition for saliency detection of spectrogram. MATLAB simulation results verify that the proposed method is effective in saliency detection of the spectrogram from rotor blade echo signals and is robust in estimating the maximum micro-Doppler shift under low SNR.