scholarly journals A factor mining model with optimized random forest for concrete dam deformation monitoring

2021 ◽  
Author(s):  
Hao Gu ◽  
Meng Yang ◽  
Chong-shi Gu ◽  
Xiao-fei Huang
Keyword(s):  
Random Forest ◽  
Deformation Monitoring ◽  
Concrete Dam ◽  
Dam Deformation ◽  
Mining Model

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.

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

scholarly journals Ensemble of bagged regression trees for concrete dam deformation predicting

2019 ◽  
Vol 376 ◽  
pp. 012040 ◽  
Author(s):  
Jiemin Xie ◽  
Jun Zhang ◽  
Xuan Xie ◽  
Zhiwei Bi ◽  
Zhuoheng Li
Keyword(s):  
Regression Trees ◽  
Concrete Dam ◽  
Dam Deformation

scholarly journals Random-Forest Machine Learning Approach for High-Speed Railway Track Slab Deformation Identification Using Track-Side Vibration Monitoring

2021 ◽  
Vol 11 (11) ◽  
pp. 4756
Author(s):  
Gaoran Guo ◽  
Xuhao Cui ◽  
Bowen Du
Keyword(s):  
Random Forest ◽  
High Speed ◽  
Recognition Rate ◽  
Operation Time ◽  
Deformation Monitoring ◽  
Vibration Monitoring ◽  
Railway Track ◽  
Ballastless Track ◽  
Train Operation ◽  
Track Slab

High-speed railways (HSRs) are established all over the world owing to their advantages of high speed, ride comfort, and low vibration and noise. A ballastless track slab is a crucial part of the HSR, and its working condition directly affects the safe operation of the train. With increasing train operation time, track slabs suffer from various defects such as track slab warping and arching as well as interlayer disengagement defect. These defects will eventually lead to the deformation of track slabs and thus jeopardize safe train operation. Therefore, it is important to monitor the condition of ballastless track slabs and identify their defects. This paper proposes a method for monitoring track slab deformation using fiber optic sensing technology and an intelligent method for identifying track slab deformation using the random-forest model. The results show that track-side monitoring can effectively capture the vibration signals caused by train vibration, track slab deformation, noise, and environmental vibration. The proposed intelligent algorithm can identify track slab deformation effectively, and the recognition rate can reach 96.09%. This paper provides new methods for track slab deformation monitoring and intelligent identification.

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.

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