Dynamic interpretation of the factors causing dam deformation with hybrid grey dynamic incidence model

•A novel hybrid grey dynamic incidence model is designed considering curve and angle.•The classic grey incidence model is expanded for multi-point monitoring deformation.•The key indicators are given by considering fluctuation range and changing trend.•The dynamic interpretation of dam deformation to environmental factors is explored.•The results provide knowledge of value for dam safety diagnosis and prediction. Deformation measured from various measuring points within and on a dam provides important information on the structural behaviour. The effective analysis of the development trend of factors causing deformation is a significant prerequisite for the structural health monitoring of dams. However, the existing research, focuses on qualitative analysis without distinguishing the difference in the contribution of deformation-influencing factors and revealing their dynamic evolution. Additionally, the quantitative analyses concentrated on single-point information cannot reflect the global correlation between the overall deformation and environmental factors, and the correlation cannot be fully characterised either by the fluctuation range or changing trend of monitoring data series. To overcome these issues, a novel hybrid grey dynamic incidence model is designed by integrating the Deng’s grey incidence model and grey dynamic trend incidence model to dynamically analyse the factors causing dam deformation. In particular, the classic grey incidence models are improved through dimensional expansion for multi-point monitoring environment. Moreover, considering the effect of fluctuation range and changing trend, the hybrid grey dynamic incidence degree is defined to improve the interpretability of the influencing variables. Hence, the proposed model is applied to dynamically quantify the global connections of environmental factors against the deformation of an arch dam. The results are verified to be effective in improving the prediction accuracy in the structural safety of a dam and can be used to diagnose the working state of individual instrument location.