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

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.

Deformation Monitoring and Data Analysis for Metro Construction with Mining Method in Dalian

2013 ◽  
Vol 353-356 ◽  
pp. 1604-1608
Author(s):  
Guang Bin Bai ◽  
Jie Zhao ◽  
Li Sheng Liu
Keyword(s):  
Data Analysis ◽  
Construction Method ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
Ground Subsidence ◽  
Tunnel Construction ◽  
Mining Method ◽  
Construction Process ◽  
Subway Tunnel ◽  
The Future

Based on a subway tunnel construction, the construction method was introduced. The ground subsidence, crown settlement and convergence displacement caused by the cut tunnel are monitored during the tunneling construction and the results of monitoring data for them are analyzed. This technology wells to guide the tunnel-entering construction effectively and avoid the tunnel-entering construction process prone to landslides, thus ensuring the safety of the tunnel construction and will guiding the future construction.

Safety Monitoring Information System of Railway Tunnel Construction Based on GIS

2013 ◽  
Vol 303-306 ◽  
pp. 811-814 ◽  
Author(s):  
Ning Suo ◽  
Hui Lin Wang
Keyword(s):  
Data Analysis ◽  
Early Warning ◽  
Information Acquisition ◽  
Warning System ◽  
Safety Monitoring ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
Tunnel Construction ◽  
Railway Tunnel ◽  
Engineering Information

This paper puts forward the railway tunnel construction based on GIS for deformation monitoring data analysis as the foundation of railway tunnel construction safety monitoring and risk early warning system. Practice shows that the system in engineering information acquisition, construction deformation data analysis, early warning and monitoring data has obvious advantages. And it is still in help users to make decisions and plays an important role to ensure the safety of tunnel construction.

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.

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 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 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.

A spatio-temporal clustering and diagnosis method for concrete arch dams using deformation monitoring data

2018 ◽  
Vol 18 (5-6) ◽  
pp. 1355-1371 ◽  
Author(s):  
Bo Chen ◽  
Tianyi Hu ◽  
Zishen Huang ◽  
Chunhui Fang
Keyword(s):  
Time Series ◽  
Water Level ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
Arch Dams ◽  
Temporal Clustering ◽  
Diagnosis Method ◽  
Spatio Temporal ◽  
Dam Deformation ◽  
Health Diagnosis

The timely analysis of deformation monitoring data and reasonable diagnosis of the structural health are key tasks in dam health monitoring studies. This article presents a spatio-temporal clustering and health diagnosis method for super-high concrete arch dams that uses deformation monitoring data obtained from plumb meters. The spatio-temporal expression of the deformation monitoring data is proposed first by upgrading a punctuated time series to a curved panel time series, including cross-sectional, dam axial, and temporal changing directions. Second, a comprehensive similarity indicator on three aspects, namely, the absolute distance, incremental distance, and growth rate distance, is constructed after a deep discussion on deformation similarity characteristics both temporally and spatially. Next, the temporal clustering method is proposed by keeping the key features, namely, extreme points and turning points, while eliminating extraneous details, namely, noise points. Finally, the optimal spatio-temporal clustering of dam deformation is achieved by designing a multi-scale fuzzy C-means method of data mining and its iterative algorithm. The proposed method is applied to the Jinping-I hydraulic structure, which is the highest concrete arch dam in the world. The clustering results is quite sensitive in different weight coefficients of the comprehensive similarity indicator and clustering numbers of fuzzy C-means method. The dam deformation behaviors on high-water-level, water-falling, and low-water-level periods are analyzed and diagnosed. The advanced version of proposed methods is verified by comparative analysis on dam health diagnosis results obtained from ordinary deformation distribution figures and the spatio-temporal clustering figures. The proposed method will facilitate the recognition of abnormal deformation areas and associated safety diagnoses.

scholarly journals Outlier Detection Based on Multivariable Panel Data and K-Means Clustering for Dam Deformation Monitoring Data

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jintao Song ◽  
Shengfei Zhang ◽  
Fei Tong ◽  
Jie Yang ◽  
Zhiquan Zeng ◽  
...  
Keyword(s):  
Panel Data ◽  
Outlier Detection ◽  
Correlation Coefficients ◽  
Safety Monitoring ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
Structural Safety ◽  
Dam Safety ◽  
Dam Safety Monitoring ◽  
Dam Deformation

A dam is a super-structure widely used in water conservancy engineering fields, and its long-term safety is a focus of social concern. Deformation is a crucial evaluation index and comprehensive reflection of the structural state of dams, and thus there are many research papers on dam deformation data analysis. However, the accuracy of deformation data is the premise of dam safety monitoring analysis, and original deformation data may have some outliers caused by manual errors or instruments aging after long-time running. These abnormal data have a negative impact on the evaluation of dam structural safety. In this study, an analytical method for detecting outliers of dam deformation data was established based on multivariable panel data and K-means clustering theory. First, we arranged the original spatiotemporal monitoring data into the multivariable panel data format. Second, the correlation coefficients between the deformation signals of different measuring points were studied based on K-means clustering theory. Third, the outlier detection rules were established through the changes of the correlation coefficients. Finally, the proposed model was applied to the Jinping-I Arch Dam in China which is the highest dam in the world, and results indicate that the detection method has high accuracy detection ability, which is valuable in dam safety monitoring applications.

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