A Novel Imputation Model for Missing Concrete Dam Monitoring Data

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...

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Bibliographic Details
Published inMathematics (Basel) Vol. 11; no. 9; p. 2178
Main Authors Cui, Xinran, Gu, Hao, Gu, Chongshi, Cao, Wenhan, Wang, Jiayi
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.05.2023
Subjects
Analysis
Cluster analysis
Clustering
Concrete
concrete dam
Concrete dams
Dam failure
Dam foundations
Dam safety
Dams
Data models
Deformation effects
distance similarity index
Human error
Longitudinal studies
Machine learning
Mathematics
measurement point clustering
Methods
Missing data
missing data imputation
Monitoring
Monitoring systems
Neural networks
panel data model
Similarity
Vector quantization
Weighting methods
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Summary: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.
ISSN:2227-7390
2227-7390
DOI:10.3390/math11092178