To ensure the safety of concrete dams, a large number of monitoring instruments are embedded in the bodies and foundations of the dams. However, monitoring data are often missing due to failure of monitoring equipment, human error and other factors that cause difficulties in diagnosis of dam safety and failure to precisely predict their deformation. In this paper, a new method for imputing missing deformation data is proposed. First, since the traditional deformation increment speed distance index of the deformation similarity index does not take into account the fact that there is little change in deformations occurring in two consecutive days, the denominator of the index tends to be equal to zero. In this paper, an improved index for solving this problem is proposed. A combined weighting method for calculating the deformation similarity comprehensive index and the k-means clustering method is then proposed and used to classify deformation monitoring points. Subsequently, a panel data model that imputes different types of missing data is established. The method proposed in this paper can impute missing concrete dam deformation data more accurately; therefore, it can effectively solve the missing deformation monitoring data problem.

For the structural health diagnostic of concrete dams, the mathematical monitoring model based on the measured deformation values is of great significance. The main purpose of this paper is to reconstruct the ageing component and the temperature component in the traditional Hydraulic-Seasonal-Time (HST) hybrid model by combining the measured values. On the one hand, a better mathematical model for the ageing displacement of concrete dams is proposed combined with the Burgers model to separate the instantaneous elastic hydraulic deformation and the hysteretic hydraulic deformation, and then it subsumes the latter into the ageing deformation to describe its reversible component. According to the Burgers model, the inverted elastic modulus of the Jinping-Ⅰ concrete dam is 46.5 GPa, which is closer to the true value compared with the HST model. On the other hand, the kernel principal component analysis (KPCA) method is used to extract the principal components of the dam thermometers for replacing the period harmonic thermal factor. A multiple linear regression (MLR) model is established to fit the measured displacement of the concrete arch dam and to verify the accuracy of the proposed hybrid model. The results show that the proposed model reaches higher accuracy than the traditional HST hybrid model and is helpful to improve the interpretation of the separated displacement components of the concrete dams.