Zonal intelligent inversion of viscoelastic parameters of high arch dams using an HEST statistical model-Reference-Cited by-同舟云学术

Zonal intelligent inversion of viscoelastic parameters of high arch dams using an HEST statistical model

Published:2021-11-22 Issue:1 Volume:12 Page:207-223
ISSN:2190-5452
Container-title:Journal of Civil Structural Health Monitoring
language:en
Short-container-title:J Civil Struct Health Monit

Author:

Wang Shaowei^ORCID,Xu Cong,Liu Yi,Xu Bo

Funder

national key r&d program of china

national natural science foundation of china

china postdoctoral science foundation

belt and road special foundation of the state key laboratory of hydrology-water resources and hydraulic engineering

open research fund of key laboratory of construction and safety of water engineering of the ministry of water resources, china institute of water resources and hydropower research

Self-Topic Fund of State Key Laboratory of Simulation & Regulation of Water Cycle in River Basin

Publisher

Springer Science and Business Media LLC

Subject

Safety, Risk, Reliability and Quality,Civil and Structural Engineering

Link

https://link.springer.com/content/pdf/10.1007/s13349-021-00538-0.pdf

Reference31 articles.

1. Wu SY, Cao W, Zheng J (2016) Analysis of working behavior of Jinping-I arch dam during initial impoundment. Water Sci Eng 9(3):240–248. https://doi.org/10.1016/j.wse.2016.11.001

2. Li HK, Wang G, Wei BW, Zhong YW, Zhan LH (2019) Dynamic inversion method for the material parameters of a high arch dam and its foundation. Appl Math Model 71:60–76. https://doi.org/10.1016/j.apm.2019.02.008

3. Su HZ, Fu ZQ, Wen ZP (2019) SFPSO algorithm-based multi-scale progressive inversion identification for structural damage in concrete cut-off wall of embankment dam. Appl Soft Comput J 84:105679. https://doi.org/10.1016/j.asoc.2019.105679

4. Salazar F, Morán R, Toledo MA, Oñate E (2017) Data-based models for the prediction of dam behaviour: a review and some methodological considerations. Arch Comput Methods Eng 24(1):1–21. https://doi.org/10.1007/s11831-015-9157-9

5. Tatin M, Briffaut M, Dufour F, Simon A, Fabre JP (2018) Statistical modelling of thermal displacements for concrete dams: influence of water temperature profile and dam thickness profile. Eng Struct 165:63–75. https://doi.org/10.1016/j.engstruct.2018.03.010

Cited by 15 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A deep learning method for predicting the displacement of concrete arch dams considering the effect of cracks;Advanced Engineering Informatics;2024-10

2. Displacement observation data-based structural health monitoring of concrete dams: A state-of-art review;Structures;2024-10

3. Dam deformation prediction model based on the multiple decomposition and denoising methods;Measurement;2024-10

4. Residual correction-based dam deformation monitoring method via data dimension reduction and optimized Bi-LSTM algorithm;Structural Health Monitoring;2024-09-03

5. A missing data processing method for dam deformation monitoring data using spatiotemporal clustering and support vector machine model;Water Science and Engineering;2024-08