A Novel Imputation Model for Missing Concrete Dam Monitoring Data

Abstract

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.

A Multi-Point Joint Prediction Model for High-Arch Dam Deformation Considering Spatial and Temporal Correlation

“…The K-means time series clustering approach [32] is employed to categorize multiple measurement points of arch dams into distinct clusters. The fundamental principle behind this approach is to minimize the dissimilarity among samples within the same cluster while maximizing the dissimilarity between clusters, thereby reflecting the spatial correlation among similar measurement points and the spatial variability among dissimilar ones.…”

A Deformation Analysis Method for Sluice Structure Based on Panel Data

“…Zhao et al [23] established a functional relationship between the measured values and the real-time risk probability based on the estimation of robustness of the panel model. Cui et al [24] proposed a novel imputation model for missing concrete dam deformation data based on their evaluation of the accuracy of the panel model. In fact, one of the main uses of panel data theory is to handle the unobservable individual or overall effects [25], and we can utilize panel data theory to identify the individual effects of each measurement point, and further to evaluate the nonuniform deformation status of the sluice.…”