Study on strategies for reducing training samples for accurate estimation of wind-induced structural response of LSTM networks-Reference-Cited by-同舟云学术

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Study on strategies for reducing training samples for accurate estimation of wind-induced structural response of LSTM networks

Published:2023-07 Issue: Volume:238 Page:105421
ISSN:0167-6105
Container-title:Journal of Wind Engineering and Industrial Aerodynamics
language:en
Short-container-title:Journal of Wind Engineering and Industrial Aerodynamics

Author:

Li Lixiao,Huang Xigui,Chen Shangxin,Wu Teng,Mei Liu,Long Wujian,Xiao Yiqing

Publisher

Elsevier BV

Subject

Mechanical Engineering,Renewable Energy, Sustainability and the Environment,Civil and Structural Engineering

Reference55 articles.

1. Extreme value distribution and peak factor of crosswind response of flexible structures with nonlinear aeroelastic effect;Chen;J. Struct. Eng.,2014

2. An automated classification method of thunderstorm and non-thunderstorm wind data based on a convolutional neural network;Chen;J. Wind Eng. Ind. Aerod.,2020

3. Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization;Chen;Energy Convers. Manag.,2018

4. Structural nonlinear damage identification algorithm based on time series ARMA/GARCH model;Chen;Adv. Struct. Eng.,2013

5. A two-layer nonlinear combination method for short-term wind speed prediction based on ELM, ENN, and LSTM;Chen;IEEE Internet Things J.,2019

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1. Intelligent evaluation of interference effects between tall buildings based on wind tunnel experiments and explainable machine learning;Journal of Building Engineering;2024-11

2. Air pollutant removal performance using a BiLSTM-based demand-controlled ventilation method after tunnel blasting;Journal of Wind Engineering and Industrial Aerodynamics;2024-10

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