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The study on developing the reasonable safety monitoring indexes plays a most importantly role in the health monitoring of high core rockfill dams. However, researches on this topic are relatively scarce both at home and abroad. In this paper, the characteristics and failure modes of seepage in high core rockfill dam are analyzed firstly. Then, a safety monitoring index based on seepage quantity, which reflects the overall seepage behavior, is developed, using the real-time monitoring data and its safety monitoring model. Moreover, another safety monitoring index based on seepage gradient, reflecting the local seepage behavior, is proposed, combining the spatial layout of osmo- meters and local failure mechanisms of core wall. Additionally, one more safety monitoring index based on permeability coefficient, which considers the overall and local seepage behaviors, is developed, on the basis of establishing the finite element analysis model and real-time seepage coefficient inversion analysis model of high core rockfill dam. A case study on these indexes of Nuozhadu high core rockfill dam is developed, which improves the reliability of seepage safety evaluation of the dam.

The modeling of cracks and identification of dam behavior changes are difficult issues in dam health monitoring research. In this paper, a time-varying identification model for crack monitoring data is built using support vector regression (SVR) and the Bayesian evidence framework (BEF). First, the SVR method is adopted for better modeling of the nonlinear relationship between the crack opening displacement (COD) and its influencing factors. Second, the BEF approach is applied to determine the optimal SVR modeling parameters, including the penalty coefficient, the loss coefficient, and the width coefficient of the radial kernel function, under the principle that the prediction errors between the monitored and the model forecasted values are as small as possible. Then, considering the predicted COD, the historical maximum COD, and the time-dependent component, forewarning criteria are proposed for identifying the time-varying behavior of cracks and the degree of abnormality of dam health. Finally, an example of modeling and forewarning analysis is presented using two monitoring subsequences from a real structural crack in the Chencun concrete arch-gravity dam. The findings indicate that the proposed time-varying model can provide predicted results that are more accurately nonlinearity fitted and is suitable for use in evaluating the behavior of cracks in dams.