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
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Zusammenfassung: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 approach for concrete dam displacement based on optimized relevance vector machine (ORVM). A practical optimization framework for parameters estimation using the parallel Jaya algorithm (PJA) is developed, and various simple kernel/multi-kernel functions of relevance vector machine (RVM) are tested to obtain the optimal selection. The proposed model is tested on radial displacement measurements of a concrete arch dam to mine the effect of hydrostatic, seasonal and irreversible time components on dam deformation. Four algorithms, including support vector regression (SVR), radial basis function neural network (RBF-NN), extreme learning machine (ELM) and the HST-based multiple linear regression (HST-MLR), are used for comparison with the ORVM model. The simulation results demonstrate that the proposed multi-kernel ORVM model has the best performance for predicting the displacement out of range of the used measurements dataset. Meanwhile, the ORVM model has the advantages of probabilistic output and can provide reasonable confidence interval (CI) for dam safety monitoring. This study lays the foundation for the application of RVM in the field of dam health monitoring.
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ISSN:0177-0667
1435-5663
DOI:10.1007/s00366-019-00924-9