Safety Monitoring Model of a Super-High Concrete Dam by Using RBF Neural Network Coupled with Kernel Principal Component Analysis
Joint Authors
Gu, Chongshi
Song, Jintao
Zhao, Erfeng
Chen, Siyu
Lin, Chaoning
Source
Mathematical Problems in Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-08-30
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Effective deformation monitoring is vital for the structural safety of super-high concrete dams.
The radial displacement of the dam body is an important index of dam deformation, which is mainly influenced by reservoir water level, temperature effect, and time effect.
In general, the safety monitoring models of dams are built on the basis of statistical models.
The temperature effect of dam safety monitoring models is interpreted using approximate functions or the temperature values of a few points of measurement.
However, this technique confers difficulty in representing the nonlinear features of the temperature effect on super-high concrete dams.
In this study, a safety monitoring model of super-high concrete dams is established through the radial basis neural network (RBF-NN) and kernel principal component analysis (KPCA).
The RBF-NN with strong nonlinear fitting capacity is utilized as the framework of the model, and KPCA with different kernels is adopted to extract the temperature variables of the dam temperature dataset.
The model is applied to a super-high arch dam in China, and results show that the Hybrid-KPCA -RBF-NN model has high fitting and prediction precision and thus has practical application value.
American Psychological Association (APA)
Chen, Siyu& Gu, Chongshi& Lin, Chaoning& Zhao, Erfeng& Song, Jintao. 2018. Safety Monitoring Model of a Super-High Concrete Dam by Using RBF Neural Network Coupled with Kernel Principal Component Analysis. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1205770
Modern Language Association (MLA)
Chen, Siyu…[et al.]. Safety Monitoring Model of a Super-High Concrete Dam by Using RBF Neural Network Coupled with Kernel Principal Component Analysis. Mathematical Problems in Engineering No. 2018 (2018), pp.1-13.
https://search.emarefa.net/detail/BIM-1205770
American Medical Association (AMA)
Chen, Siyu& Gu, Chongshi& Lin, Chaoning& Zhao, Erfeng& Song, Jintao. Safety Monitoring Model of a Super-High Concrete Dam by Using RBF Neural Network Coupled with Kernel Principal Component Analysis. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1205770
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1205770