A Completion Method for Missing Concrete Dam Deformation Monitoring Data Pieces

Gu, Hao; Wang, Tengfei; Zhu, Yantao; Wang, Cheng; Yang, Dashan; Huang, Lixian

doi:10.3390/app11010463

Cited by 18 publications

(5 citation statements)

References 12 publications

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“…D (1) j,:,i ∈ R F is the difference between the expression of time interval j and the mean value of the surrounding time interval. Its formula is shown in (13). Similar to D (1) , the tensor D (2) is also used to describe the difference in time interval expression, and D (2) j,:,k ∈ R F .…”

Section: Objective Function and Parametermentioning

confidence: 99%

“…Although the KNN completion algorithm is simple, intuitive, and easy to implement without the need for prior knowledge, the accuracy of its completion depends on the average value of the neighboring sample data. Literature [13] based on the single-value missing data completion method of nonlocal averaging method proposed a multivalue missing data completion method using spatial neighbor BP (backpropagation) mapping to achieve higher precision data completion. However, this method does not consider the timing of the data to be completed.…”

Section: Introductionmentioning

confidence: 99%

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Joint Matrix Decomposition-Based Missing Data Completion in Low-Voltage Area

Yang

Xie

et al. 2021

Mathematical Problems in Engineering

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In-depth mining and analysis of electricity data in low-voltage area are essential for the further intelligent development of power grids. However, in the actual data collection and measurement of low-voltage area, there will be missing data, and complete electricity data cannot be obtained. To obtain complete power data, this paper proposes a low-voltage station area missing data complement model based on joint matrix decomposition. First, we analyse the characteristics of the low-pressure station data. Then, a model that comprehensively considers the characteristics of the low-voltage station area data is proposed, which includes three parts: the construction of a low-voltage station area data tensor, the joint matrix decomposition, and the completion of the missing data, and it is named LPZ. After that, the CIM learning algorithm proposed in this paper is used to iteratively solve the model to obtain the completed data. Finally, the method proposed in this paper is used to complement the two situations of random loss and all-day loss of real current data in a low-voltage station area and compared with the traditional complement method. The experimental results show that this method is not only effective but also that the completion effect is better than that of other completion methods.

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Section: Objective Function and Parametermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Joint Matrix Decomposition-Based Missing Data Completion in Low-Voltage Area

Yang

Xie

et al. 2021

Mathematical Problems in Engineering

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“…As a comprehensive effect quantity of the performance of the concrete dam, deformation always attracts more attention as it indicates the transformation of the structural behavior of the dam [3][4][5]. An enormous amount of deformation monitoring data were gathered for fundamental analysis and predictions of dam deformation behavior during the lifespan of a concrete dam [6]. However, the failure of monitoring instruments, the complicated monitoring environments and human errors inevitably led, to some extent, to the loss of actual measured data [7].…”

Section: Introductionmentioning

confidence: 99%

“…When faced with data that have more influencing factors and higher data dimensions, machine learning-based methods usually show better performance. Gu et al [6] proposed a multi-value missing data imputation approach using BP (back propagation) mapping of spatially adjacent points from the singlevalue missing data completion method based on the nonlocal average method. Li et al [8] suggested a framework for imputing missing sensor data based on a deep-stacked bidirectional long short-term memory neural network with a self-attention mechanism to handle various missing data scenarios in dam structural monitoring systems with different missing rates with high accuracy and robustness.…”

Section: Introductionmentioning

confidence: 99%

A Novel Imputation Model for Missing Concrete Dam Monitoring Data

Cui

et al. 2023

Mathematics

Self Cite

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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.

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“…As an indicator of the safety and work performance of concrete dam, the original measurement data always attract much attention for its integrity and accuracy [1][2][3]. However, due to the impact of such influencing factors, such as complicated and ever-changing operating conditions and human error, it is often difficult to prevent the actual measurement data from suffering a loss to varying extents [4].…”

Section: Introductionmentioning

confidence: 99%

Processing Method of Missing Data in Dam Safety Monitoring

Wei

2021

Mathematical Problems in Engineering

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A large amount of data obtained by dam safety monitoring provides the basis to evaluate the dam operation state. Due to the interference caused by equipment failure and human error, it is common or even inevitable to suffer the loss of measurement data. Most of the traditional data processing methods for dam monitoring ignore the actual correlation between different measurement points, which brings difficulties to the objective diagnosis of dam safety and even leads to misdiagnosis. Therefore, it is necessary to conduct further study on how to process the missing data in dam safety monitoring. In this study, a data processing method based on partial distance combining fuzzy C-means with long short-term memory (PDS-FCM-LSTM) was proposed to deal with the data missing from dam monitoring. Based on the fuzzy clustering performed for the measurement points of the same category deployed on the dam, the membership degree of each measurement point to cluster center was described by using the fuzzy C-means clustering algorithm based on partial distance (PDS-FCM), so as to determine the clustering results and preprocess the missing data of corresponding measurement points. Then, the bidirectional long short-term memory (LSTM) network was applied to explore the pattern of changes of measurement values under identical clustering conditions, thus processing the data missing from monitoring effectively.

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A Completion Method for Missing Concrete Dam Deformation Monitoring Data Pieces

Cited by 18 publications

References 12 publications

Joint Matrix Decomposition-Based Missing Data Completion in Low-Voltage Area

Joint Matrix Decomposition-Based Missing Data Completion in Low-Voltage Area

A Novel Imputation Model for Missing Concrete Dam Monitoring Data

Processing Method of Missing Data in Dam Safety Monitoring

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