Next Article in Journal
Evolutionary Algorithm-Based Complete Coverage Path Planning for Tetriamond Tiling Robots
Previous Article in Journal
Voltammetry at Hexamethyl-P-Terphenyl Poly(Benzimidazolium) (HMT-PMBI)-Coated Glassy Carbon Electrodes: Charge Transport Properties and Detection of Uric and Ascorbic Acid
Open AccessArticle

External Deformation Monitoring and Improved Partial Least Squares Data Analysis Methods of High Core Rock-Fill Dam (HCRFD)

1
State Key Laboratory of Surveying, Mapping and Remote Sensing Information Engineering, Wuhan University, Wuhan 430072, China
2
Key Laboratory of Geo-Environmental Surveillance in the Maritime and Marine Zones, National Mapping and Geographic Information Bureau, Shenzhen University, Shenzhen 518061, China
3
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China
4
State Key Laboratory of Geodesy and Earth’s Dynamics, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(2), 444; https://doi.org/10.3390/s20020444
Received: 25 November 2019 / Revised: 8 January 2020 / Accepted: 10 January 2020 / Published: 13 January 2020
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
External deformation monitoring of high core rock-fill dams (HCRFDs) is an important and difficult part of safety monitoring. The traditional method of external deformation monitoring and data analysis for HCRFDs is to use a total station for small angle observations and establish a regression model to analyze the results. However, the small angle method has low accuracy and a low automation degree, and there is multicollinearity between the independent variables, which affects the parameter estimation and leads to the failure of model establishment. The angle forward intersection method is adopted in this paper for observation, and an improved partial least squares method (IPLS) is proposed to eliminate the multicollinearity of the independent variables. Compared to the traditional method, the improved observation method exhibits high accuracy and a high automation degree. The new data analysis method can not only eliminate multicollinearity but also improve the interpretation ability of the model. The data from the initial stage of water storage shows that the displacement increases with the increase in the upstream water level and time, and the speed of water storage is proportional to the displacement. The water level and time are the main influencing factors. This conclusion provides a theoretical basis for reservoir management departments to control water levels and gate opening and closing. The method in this paper can be applied to arch dams, gravity dams, and other types of waterpower engineering systems. View Full-Text
Keywords: angle forward intersection method; high core rock-fill dams (HCRFD); improved partial least squares method; machine learning; geodetic control network; total station angle forward intersection method; high core rock-fill dams (HCRFD); improved partial least squares method; machine learning; geodetic control network; total station
Show Figures

Graphical abstract

MDPI and ACS Style

Cheng, X.; Li, Q.; Zhou, W.; Zhou, Z. External Deformation Monitoring and Improved Partial Least Squares Data Analysis Methods of High Core Rock-Fill Dam (HCRFD). Sensors 2020, 20, 444.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop