Deep learning seismic damage assessment with embedded signal denoising considering three-dimensional time–frequency feature correlation-Reference-Cited by-同舟云学术

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Deep learning seismic damage assessment with embedded signal denoising considering three-dimensional time–frequency feature correlation

Published:2023-07 Issue: Volume:286 Page:116148
ISSN:0141-0296
Container-title:Engineering Structures
language:en
Short-container-title:Engineering Structures

Author:

Su Zhe,Yu Jia,Xiao Xiao,Wang Jiajun,Wang Xiaoling

Funder

Tianjin Municipal Natural Science Foundation

Natural Science Foundation of Tianjin Municipal Science and Technology Commission

Publisher

Elsevier BV

Subject

Civil and Structural Engineering

Reference57 articles.

1. Probability density evolution method for seismic liquefaction performance analysis of earth dam;Huang;Earthq Eng Struct Dynam,2017

2. Real-time city-scale time-history analysis and its application in resilience-oriented earthquake emergency responses;Lu;Appl Sci,2019

3. Earthquake disaster simulation of civil infrastructures;Lu,2017

4. Seismic damage assessment based on regional synthetic ground motion dataset: A case study for Erzincan, Turkey;Karimzadeh;Nat Hazards,2018

5. Automated regional seismic damage assessment of buildings using an unmanned aerial vehicle and a convolutional neural network;Xiong;Autom Constr,2020

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3. Feasibility Study of Earthquake‐Induced Damage Assessment for Structures by Utilizing Images from Surveillance Cameras;Structural Control and Health Monitoring;2024-01

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