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 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.

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.

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.

Design of Mine Production Safety Monitoring System

2012 ◽  
Vol 503-504 ◽  
pp. 1330-1333
Author(s):  
Yan Kun Wang ◽  
Yun Xu Shi ◽  
Hong Mei Fan
Keyword(s):  
Data Processing ◽  
Monitoring System ◽  
Processing System ◽  
Safety Monitoring ◽  
Monitoring Data ◽  
Data Processing System ◽  
Network Systems ◽  
Safety Monitoring System ◽  
Monitoring Data Processing ◽  
And Control

The mine safety monitoring system is a set of sensor technology, electronics technology, power electronics technology, computer technology, wireless communication and network technology in one of China's leading multi-functional computer network systems, including underground, Inoue environment and equipment the detection of network systems and the Inoue monitoring data processing system. Environment and equipment for testing network system to achieve underground, of Inoue environment physical monitoring and control; monitoring data processing system is a comprehensive treatment of the collected data in order to achieve the sub-station set up and control equipment or detection sensors, through LAN detection information sharing, may constitute the enterprise information system.

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 Concrete Dam Deformation Prediction Method Based on LSTM With Attention Mechanism

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 185177-185186
Author(s):  
Dashan Yang ◽  
Chongshi Gu ◽  
Yantao Zhu ◽  
Bo Dai ◽  
Kang Zhang ◽  
...  
Keyword(s):  
Prediction Method ◽  
Attention Mechanism ◽  
Deformation Prediction ◽  
Concrete Dam ◽  
Dam Deformation

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>

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.

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