Optimized multi-output LSSVR displacement monitoring model for super high arch dams based on dimensionality reduction of measured dam temperature field
Optimized multi-output LSSVR displacement monitoring model for super high arch dams based on dimensionality reduction of measured dam temperature field
复制标题
基于实测坝体温度场降维的优化多输出LSSVR位移监测模型
DOI：
10.1016/j.engstruct.2022.114686
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发表时间：
2022-10
影响因子：
5.5
通讯作者：
Weinan Chen
中科院分区：
文献类型：
--
作者：
Mingyuan Zhu;Bo Chen;Chongshi Gu;Yan Wu;Weinan Chen
Establishing a reasonable displacement health monitoring model is essential for determining the safety of super high arch dams. However, previous studies only focus on a single measurement point, which leads to low efficiency and accuracy problems in evaluating the overall status of the dam. To solve this issue, a multi-output least square support vector regression (MLSSVR) model that can evaluate and forecast multiple monitoring points is developed in this manuscript. The optimal parameters of the model are determined by particle swarm optimization (PSO), in order to improve the precision and generalization ability of the model. On the other hand, the kernel principal component algorithm (KPCA) is introduced to extract the principal temperature components to construct the model, which brings advantages in revealing the actual temperature displacement accurately compared to the harmonic function and ambient temperature, as well as overcoming the multicollinearity caused by the superabundant of temperature factors. The feasibility and accuracy of the proposed model are tested with long-term measured displacements of a super high arch dam. The results show that the proposed model is superior to the multiple linear regression (MLR) and support vector regression (SVR), based on the hydrostatic-seasonal-time (HST) and hydrostatic-temperature–time (HTT) models. It also has outstanding medium and long-term predictive capacity, which provides a new approach for dam displacement safety monitoring.
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DOI：
10.1016/j.advengsoft.2019.02.005
发表时间：
2019-05
期刊：
Adv. Eng. Softw.
影响因子：
--
作者：
Lifu Yang;H. Su;Z. Wen
通讯作者：
Lifu Yang;H. Su;Z. Wen
DOI：
10.1007/978-3-030-51085-5_48
发表时间：
2020-10
期刊：
Lecture Notes in Civil Engineering
影响因子：
--
作者：
F. Salazar;A. Conde;D. Vicente
通讯作者：
F. Salazar;A. Conde;D. Vicente
影响因子：
6.4
作者：
D. Santillán;E. Salete;M. Toledo
通讯作者：
D. Santillán;E. Salete;M. Toledo
DOI：
10.1007/s11431-011-4339-7
发表时间：
2011-03
期刊：
Science China Technological Sciences
影响因子：
--
作者：
C. Gu;Erfeng Zhao;Yi Jin;H. Su
通讯作者：
C. Gu;Erfeng Zhao;Yi Jin;H. Su
影响因子：
1.6
作者：
Dai, W.;Huang, D.;Liu, B.
通讯作者：
Liu, B.