影响因子：1.6
DOI码：10.1080/0954898X.2024.2447281
所属单位：船舶工程学院
发表刊物：Network: Computation in Neural Systems
刊物所在地：英国
项目来源：National Key Research and Development Program of China (2019YFB1504403); Heilongjiang Excellent Yout
关键字：Gravity dam; reliability; deep learning network; Monte Carlo method(MC)
摘要：To improve the calculation accuracy of the Monte Carlo (MC) method and reduce the calculation time. Firstly, CNN and LSTM deep learning networks are introduced for designing nonlinear dynamic systems simulating dam stress. Then, spatial feature mining and sequence information extraction of nonlinear data of dam stress are carried out respectively, and a combined prediction model of dam stress depth (DS-FEM-CNN-LSTM) is proposed. Secondly, to solve the problem of a long time and heavy workload for the MC method to calculate a single sample point, the DOE test method is used to design the sample points. The weight factor and the distance to the failure surface are used as screening criteria. The reliability calculation method of the gravity dam (DS-FEM-CNN-LSTM-MC) is established. Finally, numerical results show that the proposed DSFEM- CNN-LSTM-MC method performs better than the existing methods in terms of computational time consumption and accuracy.
备注：National Key Research and Development Program of China (2019YFB1504403); Heilongjiang Excellent Youth Fund Project (YQ2021E015); Key Program for International Scientific and Technological Innovation Cooperation between Governments (2019YFE0102500); National Natural Science Foundation of China (No.51509056)
合写作者：任俊奇,耿敬,黄信博
第一作者：李明伟
论文类型：期刊论文
通讯作者：洪维强
学科门类：工学
文献类型：J
卷号：37
期号：2
页面范围：389-418
ISSN号：0954-898X
是否译文：否
发表时间：2026
收录刊物：SCI
发布期刊链接：https://www.tandfonline.com/doi/full/10.1080/0954898X.2024.2447281