Multi-kernel optimized relevance vector machine for probabilistic prediction of concrete dam displacement
Multi-kernel optimized relevance vector machine for probabilistic prediction of concrete dam displacement
复制标题
混凝土坝位移概率预测的多核优化相关向量机
DOI：
10.1007/s00366-019-00924-9
复制
发表时间：
2020-01-11
影响因子：
8.7
通讯作者：
Zhu, Yantao
中科院分区：
文献类型：
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作者：
Chen, Siyu;Gu, Chongshi;Zhu, Yantao
大坝位移观测资料能直观地反映大坝的实际工作性态。因此，建立精确的数据驱动模型，实现准确可靠的大坝变形安全监测十分必要。提出了一种基于优化相关向量机（ORVM）的混凝土坝位移概率预测方法。提出了一种基于并行Jaya算法（PJA）的参数估计的实用优化框架，并对相关向量机（RVM）的各种简单核函数/多核函数进行了测试，以获得最优选择。通过对某混凝土拱坝的径向位移观测，验证了该模型的有效性，并分析了静水压力、季节性和不可逆时间分量对坝体变形的影响。四种算法，包括支持向量回归（SVR），径向基函数神经网络（RBF-NN），极端学习机（ELM）和基于HST的多元线性回归（HST-MLR），被用来与ORVM模型进行比较。仿真结果表明，所提出的多核ORVM模型具有最好的性能，用于预测所使用的测量数据集的范围外的位移。同时，ORVM模型具有概率输出的优点，可以为大坝安全监测提供合理的置信区间。该研究为RVM在大坝健康监测领域的应用奠定了基础。
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.