Statistical model is a traditional safety diagnostic model for dam seepage. It can hardly display the nonlinear relationship between dam seepage and the load sets and has the disadvantage of poor extension prediction. In this paper, the theories of Back Propagation Neural Network (BPNN) combined with Genetic Algorithm (GA) are applied to the seepage prediction model. Taking a typical dam in China as an example, the prediction results of BPNN-GA model and statistical model are compared with the monitoring values. The results show that the improved dam seepage model enhances the ability of nonlinear mapping and generalization and makes the seepage prediction more accurate and reasonable in the near future. According to the established criterion, the safety state of the dam in flood season is evaluated.

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.

This study proposes a method for evaluating crack variations on structural performance of concrete dams using observational and computational output. The structural performance is represented by the system entropy in concrete dams, and the stress-strain field is established by incorporating observations and simulations for entropy calculations. Considering the characterization ability of the sample points, the weighted entropy sequence is constructed with a selected index system. Thereafter, the weighted fusion entropy is successively separated and analyzed with rescaled range (R/S) analysis, and the Hurst exponent difference and corresponding warning indicators are calculated to quantify the destructiveness of the crack variation. The application of the presented method is elaborated in a concrete arch dam, and the behavior of J01-crack exhibits no significant destructiveness on its structural condition. The analysis results verified the feasibility of the proposed method in destructiveness evaluation of crack behavior on the structural performance of concrete dams.