Seismic damage identification of high arch dams based on an unsupervised deep learning approach-Reference-Cited by-同舟云学术

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Seismic damage identification of high arch dams based on an unsupervised deep learning approach

Published:2023-05 Issue: Volume:168 Page:107834
ISSN:0267-7261
Container-title:Soil Dynamics and Earthquake Engineering
language:en
Short-container-title:Soil Dynamics and Earthquake Engineering

Author:

Cao Xiangyu,Chen Liang,Chen Jianyun,Li Jing,Lu Wenyan,Liu Haixiang,Ke Minyong,Tang Yunqing

Publisher

Elsevier BV

Subject

Soil Science,Geotechnical Engineering and Engineering Geology,Civil and Structural Engineering

Reference48 articles.

1. Free vibration and damage identification of cracked functionally graded plates;Zhu;Compos Struct,2020

2. Structural damage detection with automatic feature-extraction through deep learning;Lin;Comput-Aided Civ Inf,2017

3. Structural damage identification based on autoencoder neural networks and deep learning;Pathirage;Eng Struct,2018

4. Development and application of a deep learning-based sparse autoencoder framework for structural damage identification;Pathirage;Struct Health Monit,2019

5. A new subset based deep feature learning method for intelligent fault diagnosis of bearing;Zhang;Expert Syst Appl,2018

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1. Rapid seismic damage state prediction of the subway station structure using the pre-trained network and convolutional neural network;Soil Dynamics and Earthquake Engineering;2024-10

2. A nonlinear structural pulse-like seismic response prediction method based on pulse-like identification and decomposition learning;Smart Materials and Structures;2024-09-05

3. Maximizing hydropower station safety against earthquake through extreme learning machine-enabled shear waves velocity prediction;Frontiers in Environmental Science;2024-06-18

4. Modal Parameter Recursive Estimation of Concrete Arch Dams under Seismic Loading Using an Adaptive Recursive Subspace Method;Sensors;2024-06-14

5. Structural seismic response reconstruction method based on multidomain feature-guided generative adversarial neural networks;Smart Materials and Structures;2024-04-18

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