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

• Published in: Engineering with computers Vol. 37; no. 3; pp. 1943 - 1959
• Main Authors: Chen, Siyu, Gu, Chongshi, Lin, Chaoning, Zhang, Kang, Zhu, Yantao
• Format: Journal Article
• Language: English
• Published: London Springer London 01.07.2021; Springer Nature B.V
• Subjects: 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

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...