The Application of a New Compromise Thresholding Method in the Safety-Monitoring of Dam Displacement

2014 ◽  
Vol 584-586 ◽  
pp. 2113-2116
Author(s):  
Gaotang Cai
Keyword(s):  
Prediction Accuracy ◽  
Safety Monitoring ◽  
Horizontal Displacement ◽  
Monitoring Data ◽  
Dam Safety ◽  
Important Work ◽  
Thresholding Method ◽  
Dam Safety Monitoring ◽  
Displacement Based ◽  
Dam Monitoring

How to identify and remove the noise of the dam monitoring data is an important work of dam safety-monitoring. The article makes use of a new compromise thresholding method to denoise the the dam horizontal displacement. Based on the de-noising data, the article uses the PSO-SVM to overfit and predict the dam horizontal displacement and compare the results with multiple regression.The result shows that the combination of the new compromise thresholding method and PSO-SVM has a better prediction accuracy.

A New Method of Dam Crack Safe-Monitoring Based on the Markov Chain

2014 ◽  
Vol 638-640 ◽  
pp. 722-725
Author(s):  
Ai Xiang Shi ◽  
Kai Zhu
Keyword(s):  
Markov Chain ◽  
Crack Width ◽  
Safety Monitoring ◽  
Regression Method ◽  
Dam Safety ◽  
Important Work ◽  
Correction Model ◽  
Dam Safety Monitoring ◽  
Multiple Regression Method ◽  
Dam Monitoring

How to identify and remove the noise of the dam monitoring data is an important work of dam safety-monitoring. The article makes use of a new compromise thres-holding method to de-noise the dam horizontal dam crack width. Afterwards, the article uses the Markov correction model to predict the crack width of the dam body based on the de-nosing data. In order to identify the effectiveness of the model, the article makes a comparison between the results of the above method with that of the multiple regression method. And the results show that the method of the article is of effective.

scholarly journals Application of the Bayesian Belief Nets in dam safety monitoring

2012 ◽  
Vol 44 (1) ◽  
pp. 25-33 ◽  
Author(s):  
Dorota Mirosław-Świątek ◽  
Mariusz Kembłowski ◽  
Władysław Jankowski
Keyword(s):  
Water Pressure ◽  
Safety Monitoring ◽  
Data Interpretation ◽  
Technical Condition ◽  
Monitoring Data ◽  
Earth Dam ◽  
Physical Parameters ◽  
Dam Safety ◽  
Dam Safety Monitoring ◽  
Bayesian Belief Nets

Application of the Bayesian Belief Nets in dam safety monitoring The systems for earth dam monitoring should enable measurements of basic physical parameters describing the behavior of the structure, including: soil water pressure, soil stress, displacements, leaks, and drainage discharges. In the case of earth dam safety assessment, the monitoring data are used to detect any anomalies in dam behavior. In this paper, the dam safety has been analyzed using the Bayesian Nets. Two types of information: water pressure measurements and drainage discharge measurements are used in analyses. The seepage anomalies in the Klimkówka Dam were considered in demonstrate the practical advantages of using the Bayesian Nets for monitoring data interpretation. Presented examples of the Bayesian Nets applications (forward and backward propagation) in analysis of seepage through earth dams show that this method can be an effective tool supporting an assessment of dams technical condition and monitoring of the dam safety.

scholarly journals Treatment of Errors in Dam Safety Monitoring Data

2019 ◽  
Vol 304 ◽  
pp. 042021
Author(s):  
Xuan Zhang ◽  
Junjie Li
Keyword(s):  
Safety Monitoring ◽  
Monitoring Data ◽  
Dam Safety ◽  
Dam Safety Monitoring

scholarly journals Analysis Method of Dam Safety Monitoring Data Based on the Least Trimmed Square Estimation

2017 ◽  
Author(s):  
De Xiu Hu ◽  
Pan Guo ◽  
Shi Yi Chen ◽  
Lin Cheng ◽  
Zhi Ming Zhao ◽  
...  
Keyword(s):  
Safety Monitoring ◽  
Monitoring Data ◽  
Dam Safety ◽  
Analysis Method ◽  
Dam Safety Monitoring

The Application of Neural Network in Dam Safety Monitoring

2011 ◽  
Vol 304 ◽  
pp. 84-89
Author(s):  
Wei Zhang ◽  
Dong Jian Zheng ◽  
Cong Cong Wang
Keyword(s):  
Prediction Accuracy ◽  
Strain Analysis ◽  
Safety Monitoring ◽  
Support Vector ◽  
Dam Safety ◽  
Strain Monitoring ◽  
Dam Safety Monitoring ◽  
Important Means ◽  
High Prediction ◽  
Accuracy Of Prediction

Dam safety monitoring is an important means for remaining the dam safe, while stress-strain monitoring has been an extremely important part in the dam monitoring. Sometimes the traditional forecasting methods are not high accuracy, so, in order to improve the accuracy of prediction. This paper presents a dam strain prediction model based on Least Squares Support Vector Machines(LS-SVM). Applied in one dam, LS-SVM shows the advantages of good robustness and high prediction accuracy. The strain prediction accuracy improves a lot than using the traditional stepwise regression method, so it provides reliable and effective ways and means in dam strain analysis.

scholarly journals Spatial-Temporal Features Based Sensor Network Partition in Dam Safety Monitoring System

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2517
Author(s):  
Hao Chen ◽  
Yingchi Mao ◽  
Longbao Wang ◽  
Hai Qi
Keyword(s):  
Time Series ◽  
Sensor Network ◽  
Dynamic Change ◽  
Safety Monitoring ◽  
Arch Dam ◽  
Monitoring Data ◽  
Dam Safety ◽  
Network Partition ◽  
Temporal Features ◽  
Dam Safety Monitoring

Many various types of sensors have been installed to monitor the deformation and stress in the dam structure. It is difficult to directly evaluate the operation status of the dam structure based on the massive monitoring data. The sensor network is divided into multiple regions according to the design specifications, simulation data, and engineering experiences. The local results from sub-regions are integrated to achieve overall evaluation. However, it ignores the spatial distribution of sensors and the variation of time series, which cannot meet the real-time evaluation for the dam safety monitoring. If the network partitions can provide the preliminary foundation for analyzing the dynamic change laws of the dam’s working conditions in a real-way, we should consider the similarity of structure and stresses in the local region of the dam and the correlation among the monitoring data. A time-series denoising autoencoder (TSDA) is proposed to represent the spatial and temporal features of the nodes by compressing high-dimensional monitoring data. Then, a network partitioning algorithm (NPA) based on spatial-temporal features based on the TSDA is presented. The NPA ensures that the partition results can support the analysis of the physical change laws by introducing the auxiliary objective variable to optimize the network partition objective function. Experimental results on the public datasets and a real dataset from an arch dam demonstrate that the proposed network partition algorithm NPA can achieve better partition performance than TSDA+K-Means and TSDA+GMM. The NPA can improve the silhouette coefficient by 45.1% and 58.4% higher than the TSDA+K-Means and TSDA+GMM, respectively. The NPA can increase the Calinski-Harabaz Index by 30.8% and 61.6%, respectively.

The Use of MF-DFA Based on Multi-Fractal Theory in the Safety-Monitoring of Dam Seepage

2013 ◽  
Vol 448-453 ◽  
pp. 1072-1075
Author(s):  
Shou Ping Zhang
Keyword(s):  
Prediction Accuracy ◽  
Fractal Theory ◽  
Safety Monitoring ◽  
Time Range ◽  
Dam Safety ◽  
Scaling Exponent ◽  
Original Sequence ◽  
Trend Monitoring ◽  
Dam Safety Monitoring ◽  
Modeling Calculation

To ensure dam safety monitoring prediction accuracy ,How to determine correlation between the sequence of the entire dam safety monitoring at different time-range is one of the key contents of dam safety monitoring research. The paper takes a dam for example using multiple fractal MF-DFA to remove trend monitoring fluctuations of the original sequence and calculates the scaling exponent annually comparing with the corresponding dam seepage hydrograph .The results verify the feasibility of multi-fractal MF-DFA in determining dam seepage to provide a basis for subsequent modeling calculation.

scholarly journals Processing Method of Missing Data in Dam Safety Monitoring

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Wei Wei ◽  
Chongshi Gu ◽  
Xiao Fu
Keyword(s):  
Missing Data ◽  
Short Term Memory ◽  
Safety Monitoring ◽  
Processing Method ◽  
Dam Safety ◽  
Short Term ◽  
Dam Safety Monitoring ◽  
Long Short Term Memory ◽  
Data Missing ◽  
Dam Monitoring

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.

A three-stage online anomaly identification model for monitoring data in dams

2021 ◽  
pp. 147592172110257
Author(s):  
Ying Xu ◽  
Huibao Huang ◽  
Yanling Li ◽  
Jingren Zhou ◽  
Xiang Lu ◽  
...  
Keyword(s):  
Measurement Errors ◽  
Online Monitoring ◽  
Single Point ◽  
Safety Monitoring ◽  
Monitoring And Evaluation ◽  
Temporal Analysis ◽  
Monitoring Data ◽  
Dam Safety ◽  
Environmental Response ◽  
Dam Safety Monitoring

The monitoring of data anomaly identification is an important basis for dam safety online monitoring and evaluation. In this research, a cluster of anomaly identification models for dam safety monitoring data was constructed, and a three-stage online anomaly identification method was proposed to discriminate outliers. The proposed method combined anomaly detection for measured values based on a single-point time series simulation, measurement error reduction based on remote retesting and spatio-temporal analysis, and environmental response mutation recognition. It brought about efficient and accurate detection for data mutation and online classified identification for its inducement. Additionally, problems such as missing outliers, misjudging normal values induced by the environmental response, and difficulty in online identification for measurement errors were effectively solved. The research productions were applied to the online monitoring system for the safety risk of reservoirs and dams in the Dadu River Basin. The results showed that the proposed method could effectively improve the accuracy of anomaly identification and reduce the misjudgment and omission rate to less than 2%. It could also successfully recognize and subtract nonstructural anomalies such as accidental errors, instrument faults, and environmental responses online, which provided reliable data for online dam safety monitoring.

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