李守巨

个人信息Personal Information

教授

硕士生导师

性别:男

毕业院校:大连理工大学

学位:博士

所在单位:力学与航空航天学院

学科:工程力学. 计算力学

办公地点:力学楼404

电子邮箱:lishouju@dlut.edu.cn

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Concrete dam deformation prediction model for health monitoring based on extreme learning machine

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论文类型:期刊论文

发表时间:2017-10-01

发表刊物:STRUCTURAL CONTROL & HEALTH MONITORING

收录刊物:Scopus、SCIE、EI

卷号:24

期号:10

ISSN号:1545-2255

关键字:artificial neural networks; dam health monitoring; extreme learning machine; multiple linear regression; stepwise regression

摘要:Structural health monitoring via quantities that can reflect behaviors of concrete dams, like horizontal and vertical displacements, rotations, stresses and strains, seepage, and so forth, is an important method to evaluate operational states of concrete dams correctly and predict the future structural behaviors accurately. Traditionally, statistical model is widely applied in practical engineering for structural health monitoring. In this paper, an extreme learning machine (ELM)-based health monitoring model is proposed for displacement prediction of gravity dams. ELM is one type of s with a single layer of hidden nodes, where the weights connecting inputs to hidden nodes are randomly assigned. The model can produce good generalization performance and learns faster than networks trained using the back propagation algorithm. The advantages such as easy operating, high prediction accuracy, and fast training speed of the ELM health monitoring model are verified by monitoring data of a real concrete dam. Results are also compared with that of the back propagation neural networks, multiple linear regression, and stepwise regression models for dam health monitoring.