Multi-kernel optimized relevance vector machine for probabilistic prediction of concrete dam displacement

The observation data of dam displacement can reflect the dam’s actual service behavior intuitively. Therefore, the establishment of a precise data-driven model to realize accurate and reliable safety monitoring of dam deformation is necessary. This study proposes a novel probabilistic prediction app...

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Veröffentlicht in:	Engineering with computers Jg. 37; H. 3; S. 1943 - 1959
Hauptverfasser:	Chen, Siyu, Gu, Chongshi, Lin, Chaoning, Zhang, Kang, Zhu, Yantao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.07.2021 Springer Nature B.V
Schlagworte:	Algorithms Arch dams Artificial neural networks CAE) and Design Calculus of Variations and Optimal Control; Optimization Classical Mechanics Computer Science Computer-Aided Engineering (CAD Concrete dams Confidence intervals Control Dam safety Dams Deformation effects Detention dams Displacement Kernel functions Machine learning Math. Applications in Chemistry Mathematical and Computational Engineering Model testing Neural networks Optimization Original Article Parameter estimation Performance prediction Radial basis function Statistical analysis Support vector machines Systems Theory Multi-kernel Jaya optimization algorithm Optimized relevance vector machine Prediction model Dam health monitoring
ISSN:	0177-0667, 1435-5663
Online-Zugang:	Volltext
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