scholarly journals A Completion Method for Missing Concrete Dam Deformation Monitoring Data Pieces

2021 ◽  
Vol 11 (1) ◽  
pp. 463
Author(s):  
Hao Gu ◽  
Tengfei Wang ◽  
Yantao Zhu ◽  
Cheng Wang ◽  
Dashan Yang ◽  
...  
Keyword(s):  
Missing Data ◽  
Back Propagation ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
Concrete Dam ◽  
Human Errors ◽  
Data Completion ◽  
Deformation Behaviors ◽  
Dam Deformation ◽  
Deformation Patterns

A concrete dam is an important water-retaining hydraulic structure that stops or restricts the flow of water or underground streams. It can be regarded as a constantly changing complex system. The deformation of a concrete dam can reflect its operation behaviors most directly among all the effect quantities. However, due to the change of the external environment, the failure of monitoring instruments, and the existence of human errors, the obtained deformation monitoring data usually miss pieces, and sometimes the missing pieces are so critical that the remaining data fail to fully reflect the actual deformation patterns. In this paper, the composition, characteristics, and contamination of the concrete dam deformation monitoring information are analyzed. From the single-value missing data completion method based on the nonlocal average method, a multi-value missing data completion method using BP (back propagation) mapping of spatial adjacent points is proposed to improve the accuracy of analysis and pattern prediction of concrete dam deformation behaviors. A case study is performed to validate the proposed method.

scholarly journals Dam Deformation Monitoring Data Analysis Using Space-Time Kalman Filter

2016 ◽  
Vol 5 (12) ◽  
pp. 236 ◽  
Author(s):  
Wujiao Dai ◽  
Ning Liu ◽  
Rock Santerre ◽  
Jiabao Pan
Keyword(s):  
Kalman Filter ◽  
Data Analysis ◽  
Space Time ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
Dam Deformation

scholarly journals A Fractal Prediction Method for Safety Monitoring Deformation of Core Rockfill Dams

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Liang Pei ◽  
Jiankang Chen ◽  
Jingren Zhou ◽  
Huibao Huang ◽  
Zhengjun Zhou ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Prediction Method ◽  
Safety Monitoring ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
Dimension Theory ◽  
Deformation Prediction ◽  
The Core ◽  
Dam Deformation ◽  
And Control

Deformation mechanism in the core rockfill dams with heavy load and high-stress level is difficult to predict and control, which is one of the key problems to be solved in the dam operation safety management and control. Aiming at the large error problems obtained by the parameter-based functional models (regression model, grey theory model, etc.) in the deformation prediction of the core rockfill dams, a fractal prediction method and its technical process by combining the variable dimension fractal dimension and the "metabolism" of prediction data are proposed through analyzing the fractal adaptability and deformation characteristics of original monitoring data based on the resealed-range (R/S) method and fractal dimension theory. It effectively solves the error in the process of constant dimension fractal accumulation and transformation greatly in dam deformation prediction and provides a new way for dam safety monitoring deformation prediction and early warning. The trend analysis of deformation monitoring data of the Pubugou core rockfill dam and the deformation prediction show that the fractal prediction information of dam deformation has a good corresponding relationship with its physical causes, which is in line with the actual deformation trend and operation state of the dam. Compared with the traditional stepwise regression method, the prediction results obtained by the proposed method in this paper are of high accuracy, implying that the improved fractal prediction of dam deformation is effective and the Hurst fractal index is applicable in the evaluation of the dam deformation trend.

Deformation Monitoring Model of Jinping-I Hydropower Station Based on NACA-BP Neural Network

2014 ◽  
Vol 704 ◽  
pp. 257-260
Author(s):  
De Wen Cai ◽  
Chen Fei Shao ◽  
Di Kai Wang ◽  
Er Feng Zhao ◽  
Meng Yang
Keyword(s):  
Neural Network ◽  
Local Search ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Ant Colony Algorithm ◽  
Back Propagation ◽  
Deformation Monitoring ◽  
Ant Colony ◽  
Monitoring Data ◽  
Neural Network Modeling

Back Propagation (BP) neural network can learn and store a large number of input-output model nonlinear relationships with simple structure. Niche ant colony algorithm (NACA) combines the ant colony algorithm (ACA) with the niche technology in order to add its local search ability to ACA with preserving the intelligent search ability and robustness of ACA. To optimize predicting model establishment of the dam monitoring data, NACA and BP neural network modeling method are combined to establish a prediction model of horizontal displacement monitoring data. The traditional BP neural network prediction model is established to make a comparison with the NACA. The results show that NACA-BP neural network method can speed up the convergence rate of BP neural network and enhance local search ability and prediction accuracy.

Deformation Forecasting Using a Hybrid Time Series and Neural Network Model

2012 ◽  
Vol 256-259 ◽  
pp. 2343-2346
Author(s):  
Qiang Wang ◽  
Ning Gao ◽  
Wen Zhe Jiao ◽  
Guan Jie Wang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Autoregressive Model ◽  
Back Propagation ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
High Demand ◽  
Modeling And Forecasting

In order to improve the accuracy and reliability of prediction of deformation monitoring data, a hybrid modeling and forecasting approach based on autoregressive model( AR) and the back-propagation( BP) neural network is proposed to forecast the deformation. The results of experiments show that this method can forecast the deformation precisely, and it is more suitable for those occasions where the deformation monitoring data should meet the high demand.

scholarly journals Exploration of Multi-Scale Reconstruction Framework in Dam Deformation Prediction

2021 ◽  
Vol 11 (16) ◽  
pp. 7334
Author(s):  
Rongyao Yuan ◽  
Chao Su ◽  
Enhua Cao ◽  
Shaopei Hu ◽  
Heng Zhang
Keyword(s):  
Prediction Models ◽  
Short Term Memory ◽  
Negative Impact ◽  
Reference Value ◽  
Original Data ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
Deformation Prediction ◽  
Multi Scale ◽  
Dam Deformation

Affected by various complex factors, dam deformation monitoring data usually reflect volatility and non-linear characteristics, and traditional prediction models are difficult to accurately capture the complex laws of dam deformation. A multi-scale deformation prediction model based on Variational Modal Decomposition (VMD) signal decomposition technology is proposed in this study. The method first decomposes the original deformation sequence into a series of sub-sequences with different frequencies, then the decomposed sub-sequences are modeled and predicted by Long Short-Term Memory neural network (LSTM) and Random Forest (RF) according to different frequencies. Finally, the prediction results of all sub-sequences are reconstructed to obtain the final deformation prediction results. In this process, it is proposed to use the instantaneous frequency mean method to determine the decomposition modulus of VMD. The innovation of this paper is to decompose the monitoring data with high volatility, and use LSTM and RF prediction, respectively, according to the frequency of the monitoring data, so as to realize the more accurate capture of volatility data during the prediction process. The case analysis results show that the proposed model can effectively solve the negative impact of the original data volatility on the prediction results, and is superior to the traditional prediction models in terms of stability and generalization ability, which has an important reference value for accurately predicting dam deformation and has far-reaching engineering significance.

Dam Deformation Monitoring and Data Analysis Based on Cloud Model

2012 ◽  
Vol 459 ◽  
pp. 479-482 ◽  
Author(s):  
Teng Jun Wang ◽  
Bo Yang ◽  
Hai Yan Yang
Keyword(s):  
Data Analysis ◽  
Quantitative Analysis ◽  
Qualitative Analysis ◽  
Cloud Model ◽  
Scientific Data ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
Monitoring Method ◽  
Reliable Monitoring ◽  
Dam Deformation

Dam deformation monitoring plays an important role in order to ensure the safety of dam operation, to improve project efficiency and the level of design and construction. Reliable monitoring method and scientific data analysis is the best protection for control the deformation law. Mathematical methods have been used to precisely quantitative analysis the deformation of the dam monitoring points. Usually, when assess the stability of deformation and evaluate the monitored data, qualitative languages are used to analyze qualitative result. The article combines cloud model with reliable monitoring data of Xiaolangdi to try to make qualitative analysis result quantitatively, and the quantitative analysis result can verify the qualitative analysis conclusion. It has realized the change between those two analyses. Also, utilize cloud model to analyzing deformation monitoring data is verified reliable.

Research on singular value detection method of concrete dam deformation monitoring

Measurement ◽  
2021 ◽  
Vol 179 ◽  
pp. 109457
Author(s):  
Jie Yang ◽  
Xudong Qu ◽  
Dexiu Hu ◽  
Jintao Song ◽  
Lin Cheng ◽  
...  
Keyword(s):  
Detection Method ◽  
Deformation Monitoring ◽  
Singular Value ◽  
Concrete Dam ◽  
Dam Deformation
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