A concrete dam is a kind of large-volume hydraulic structure made of concrete materials. The deformation of the concrete material of the dam body has an obvious time effect. The time-effect component of deformation comprehensively reflects the trend change of the dam deformation and its stability is an important index for evaluating the long-term safety state of concrete dams. Dam safety monitoring theory is generally applied to obtain the time-effect component based on statistical models. The regression parameters of traditional concrete dam deformation statistical models are fixed values, which cannot effectively reflect the dynamic changes in concrete dam deformation characteristics. The variable parameter statistical model is more applicable to practical deformation law of concrete dam based on time-varying parameter. This paper constructs a novel variable parameter statistical model based on the recursive least squares with forgetting factor (FFRLS) method to derive a more precisely concrete dam time-effect component. FFRLS method assigns different weights to the modeling historical data and enhances the impact of new data on the statistical model. To analyze the long-term stability of the time-effect component of concrete deformation, the stability evaluation criterion of the time-effect component is established by its long-term variation characteristics and cusp catastrophe theory. The engineering example shows that the prediction effect of the variable parameter statistical model is better than that of the fixed parameter statistical model and traditional variable parameter statistical model, the mean absolute percentage error of FFRLS model are decreased by 97.39% and 54.63% on average compared with least squares (LS) and recursive least squares (RLS) model. By fusing FFRLS variable parameter statistical model and cusp point mutation model, the safety state of concrete dam after long-term operation can be rapidly and effectively evaluated. The proposed evaluation method has significant engineering application value in concrete dam statistical modeling and safety evaluation.
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