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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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
ISSN:	0177-0667, 1435-5663
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Abstract	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.
AbstractList	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.
Author	Gu, Chongshi Chen, Siyu Lin, Chaoning Zhang, Kang Zhu, Yantao
Author_xml	– sequence: 1 givenname: Siyu orcidid: 0000-0003-1070-9761 surname: Chen fullname: Chen, Siyu organization: State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, College of Water Conservancy and Hydropower Engineering, Hohai University, National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety, Hohai University – sequence: 2 givenname: Chongshi orcidid: 0000-0002-0782-7196 surname: Gu fullname: Gu, Chongshi email: csgu@hhu.edu.cn organization: State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, College of Water Conservancy and Hydropower Engineering, Hohai University, National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety, Hohai University – sequence: 3 givenname: Chaoning orcidid: 0000-0001-5615-308X surname: Lin fullname: Lin, Chaoning email: linchaoning@hhu.edu.cn organization: College of Water Conservancy and Hydropower Engineering, Hohai University – sequence: 4 givenname: Kang surname: Zhang fullname: Zhang, Kang organization: State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, College of Water Conservancy and Hydropower Engineering, Hohai University, National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety, Hohai University – sequence: 5 givenname: Yantao surname: Zhu fullname: Zhu, Yantao organization: State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, College of Water Conservancy and Hydropower Engineering, Hohai University, National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety, Hohai University
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SubjectTerms	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
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Title	Multi-kernel optimized relevance vector machine for probabilistic prediction of concrete dam displacement
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