Predicting the Crest Settlement of Concrete Face Rockfill Dams by Combining Threshold Regression and Support Vector Machine
Publication: International Journal of Geomechanics
Volume 22, Issue 6
Abstract
The design and construction of concrete face rockfill dams (CFRDs) usually require a rapid and accurate prediction of deformation behavior to support dam optimal design and safety evaluation. Deformation prediction and control are key issues faced in the construction of CFRDs. This study collects measured data of 75 CFRD case histories. On the basis of the statistical review of the typical dam crest settlement behavior of CFRDs, a prediction model for dam crest settlement combining threshold regression (TR) and support vector machine (SVM) is established. A mixed weight coefficient is introduced to construct an adaptive hybrid kernel function with good learning ability and generalization performance. The particle swarm intelligent optimization algorithm is adopted to optimize model parameters for establishing an improved SVM prediction model. To further improve the generalization ability and accuracy of the improved SVM model, the multivariate TR theory is used to segment the dam crest settlement data according to the dam height. Then, an improved SVM prediction model is established in each dam height interval. The comparative analyses of the prediction results of different models show that the TR–SVM model effectively weakens the nonlinear mutation characteristics of the case data and achieves high prediction accuracy.
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Acknowledgments
This study was supported by the National Natural Science Foundation of China (Grant Nos. 51909215, 52039008 and 52125904), China Postdoctoral Science Foundation (Program Nos. 2021T140554 and 2020M683527), Natural Science Basic Research Program of Shaanxi (Program No. 2020JQ-641), and Young Talent fund of University Association for Science and Technology in Shaanxi, China (Program No. 20200417).
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Published In
International Journal of Geomechanics
Volume 22 • Issue 6 • June 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Sep 18, 2021
Accepted: Jan 22, 2022
Published online: Apr 6, 2022
Published in print: Jun 1, 2022
Discussion open until: Sep 6, 2022
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