Modified dam deformation monitoring model considering periodic component contained in residual sequence

2020 ◽  
Vol 27 (12) ◽  
Author(s):  
Dongyang Yuan ◽  
Bowen Wei ◽  
Bin Xie ◽  
Zimeng Zhong
Keyword(s):  
Deformation Monitoring ◽  
Periodic Component ◽  
Monitoring Model ◽  
Dam Deformation ◽  
Residual Sequence

Dam Deformation Prediction Based on EEMD-ELM Model

2020 ◽  
Author(s):  
Tao Yan ◽  
Bo Chen
Keyword(s):  
Training Sample ◽  
Ensemble Empirical Mode Decomposition ◽  
Deformation Monitoring ◽  
Concrete Gravity Dam ◽  
Deformation Prediction ◽  
Monitoring Model ◽  
Mode Decomposition ◽  
Dam Deformation ◽  
Learning Machine ◽  
Bpnn Model

<p>Establishing a reasonable and reliable dam deformation monitoring model is of great significance for effective analysis of dam deformation monitoring data and accurate assessment of dam working conditions. Firstly, the dam deformation is decomposed by the EEMD algorithm to obtain IMF components representing different characteristic scales, and different influencing factors are selected for different IMF components. Secondly, each IMF component is used as the ELM training sample to analyze, fit and predict the dam deformation component. Finally, the prediction results of each IMF component are accumulated to obtain the dam deformation prediction value. Taking a roller compacted concrete gravity dam as an example, the EEMD-ELM model is used to predict the deformation of the dam. At the same time, it is compared and analyzed with the prediction results of the BPNN model and the ELM model. The mean square error of the EMD-ELM model is 0.566, which is 54% and 14.8% lower than the BPNN model and the ELM model, indicating that the EEMD-ELM model has higher prediction accuracy and has certain application value.</p><p><strong>Key words:</strong> dam deformation;prediction model; ensemble empirical mode decomposition; extreme learning machine</p>

Dam Safety Analysis Based on Stepwise Regression Model

2011 ◽  
Vol 204-210 ◽  
pp. 2158-2161 ◽  
Author(s):  
De Xiu Hu ◽  
Zhi Qi Zhou ◽  
Yong Li ◽  
Xiao Long Wu
Keyword(s):  
Regression Model ◽  
Stepwise Regression ◽  
Least Squares Method ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
Analysis Model ◽  
Monitoring Model ◽  
Rockfill Dam ◽  
Measured Deformation ◽  
Dam Deformation

The simulating and predicting analysis model is studied by the deformation monitoring data of Bikou earth-rockfill dam. Based on the least squares method of Statistics principles, the stepwise regression model has been established of earth-rockfill dam deformation displacement, which is used to fit and forecast the measured deformation data sequences of dam. The results shows that the deformation monitoring model of Bikou earth-rockfill dam having higher fitting precision, longer predict cycle, can be better applied to the fitting and prediction of dam deformation monitoring data.

scholarly journals GEOMETRIC ASPECTS EVALUATION OF GNSS CONTROL NETWORK FOR DEFORMATION MONITORING IN THE JATIGEDE DAM REGION

2019 ◽  
Vol 15 (2) ◽  
pp. 167
Author(s):  
Made Ditha Ary Sanjaya ◽  
T. Aris Sunantyo ◽  
Nurrohmat Widjajanti
Keyword(s):  
Least Squares ◽  
Deformation Monitoring ◽  
Network Configuration ◽  
Control Networks ◽  
Quality Of Control ◽  
Least Squares Adjustment ◽  
Standard Deviations ◽  
Dam Deformation ◽  
Gnss Networks

Many factors led to dam construction failure so that deformation monitoring activities is needed in the area of the dam. Deformation monitoring is performed in order to detect a displacement at the control points of the dam. Jatigede Dam deformation monitoring system has been installed and started to operate, but there has been no evaluation of the geometry quality of control networks treated with IGS points for GNSS networks processing. Therefore, this study aims to evaluate the geometric quality of GNSS control networks on deformation monitoring of Jatigede Dam area. This research data includes the GNSS measurements of five CORS Jatigede Dam stations (R01, GG01, GCP04, GCP06, and GCP08) at doy 233 with network configuration scenarios of 12 IGS points on two quadrants (jat1), three quadrants (jat2), and four quadrants (jat3 and jat4). GNSS networks processing was done by GAMIT to obtain baseline vectors, followed by network processing usingparameter method of least squares adjustment. Networks processing with least squares adjustment aims to determine the most optimal  by precision and reliability criterion. Results of this study indicate that network configuration with 12 IGS stations in the two quadrants provides the most accurate coordinates of CORS dam stations. Standard deviations value of CORS station given by jat1 configuration are in the range of 2.7 up to 4.1 cm in X-Z components, whereas standard deviations in the Y component are in the range 5.8 up to 6.9 cm. An optimization assessment based on network strength, precision, and reliability factors shows optimum configuration by jat1.

scholarly journals GPS in the Tailings Dam Deformation Monitoring

2011 ◽  
Vol 26 ◽  
pp. 1648-1657 ◽  
Author(s):  
Wei Li ◽  
Chang Wang
Keyword(s):  
Deformation Monitoring ◽  
Tailings Dam ◽  
Dam Deformation

Automatic Measuring Instrument of Dam Horizontal Deformation Based on Laser Scan Concept

2013 ◽  
Vol 442 ◽  
pp. 372-377
Author(s):  
Jian Yin Lu ◽  
Yun Fei Zhou
Keyword(s):  
Hardware Design ◽  
Deformation Monitoring ◽  
Flow Chart ◽  
Measuring Instrument ◽  
Horizontal Deformation ◽  
Automatic Measuring ◽  
Design Scheme ◽  
Automatic Measuring Instrument ◽  
Dam Deformation

In this paper, a design of an automatic measuring instrument of dam horizontal deformation based on laser scan concept is introduced. According to the demand of the dam deformation monitoring, the design chooses STM32F103CBT6 as the hardware control platform, and the laser scan concept is introduced. The measuring principle, main hardware design scheme and software flow chart are introduced in this paper.

A Method to Determine Unstable Point and its Impact Estimation in Debris-Blocking Dam Monitoringsurvey Network

2011 ◽  
Vol 55-57 ◽  
pp. 698-703
Author(s):  
Da Bing Huang ◽  
Zhi Qiang Yang ◽  
Zhen Shi
Keyword(s):  
Earth Pressure ◽  
Monitoring Network ◽  
Horizontal Displacement ◽  
Approximate Model ◽  
Deformation Monitoring ◽  
Deformation Model ◽  
Observation Data ◽  
Network Stability ◽  
Other Information ◽  
Dam Deformation

The purpose of this paper is to determine the unstable points of debris-blocking dam deformation network and to lay the foundation for later accurate monitoring and data processing, for the reason that the affection of earth pressure and water-level-fluctuation lead to increased instability to debris-blocking dam. The author signed one horizontal displacement deformation monitoring network, measured two groups data by changing some point’s horizontal displacement with man, judged the point’s stability of the network by the method of mean gap and obtained the deformation model, and analysis the model sensitivity. The results is that the deformation network stability meet the design requirements through application the method in Fengjie Debris-blocking dam which blocked the stones debris for Shirushan tunnel, the actual displacement amount had been proven similar as the result with the method; It is concluded that this method have some practical value in deformation monitoring because it can construct the approximate model by direct observation data, which is smaller rely on other information.

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
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