A separate modeling approach to noisy displacement prediction of concrete dams via improved deep learning with frequency division-Reference-Cited by-同舟云学术

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A separate modeling approach to noisy displacement prediction of concrete dams via improved deep learning with frequency division

Published:2024-04 Issue: Volume:60 Page:102367
ISSN:1474-0346
Container-title:Advanced Engineering Informatics
language:en
Short-container-title:Advanced Engineering Informatics

Author:

Li Minghao,Ren Qiubing^ORCID,Li Mingchao,Fang Xin,Xiao Lei,Li Heng

Publisher

Elsevier BV

Reference74 articles.

1. A technical review of hydro-project development in China;Jia;Engineering.,2016

2. Modal identification of concrete arch dam by fully automated operational modal identification;Mostafaei;Structures.,2021

3. An optimized combination prediction model for concrete dam deformation considering quantitative evaluation and hysteresis correction;Ren;Advanced Engineering Informatics.,2020

4. Two spatial association-considered mathematical models for diagnosing the long-term balanced relationship and short-term fluctuation of the deformation behaviour of high concrete arch dams;Wang;Structural Health Monitoring.,2020

5. A hybrid model of bald eagle search and relevance vector machine for dam safety monitoring using long-term temperature;Yu;Advanced Engineering Informatics.,2023

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1. A multipoint prediction model for the deformation of concrete dams considering climatic features of high-altitude regions;Engineering Structures;2024-11

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

3. A novel method for settlement imputation and monitoring of earth-rockfill dams subjected to large-scale missing data;Advanced Engineering Informatics;2024-10

4. Fractional-order transfer Q-learning based on modal decomposition and convolutional neural networks for voltage control of smart grids;Advanced Engineering Informatics;2024-10

5. Advanced Predictive Modeling for Dam Occupancy Using Historical and Meteorological Data;Sustainability;2024-09-04

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