Deep Learning Framework for Total Stress Detection of Steel Components-Reference-Cited by-同舟云学术

Deep Learning Framework for Total Stress Detection of Steel Components

Published:2021-01 Issue:1 Volume:26 Page:04020113
ISSN:1084-0702
Container-title:Journal of Bridge Engineering
language:en
Short-container-title:J. Bridge Eng.

Author:

Wang Wei¹,Shi Peng²,Chu Honghu²,Deng Lu³^ORCID,Yan Banfu⁴

Affiliation:

1. Assistant Professor, Key Laboratory for Damage Diagnosis of Engineering Structures of Hunan Province, Hunan Univ., Changsha 410082, China; College of Civil Engineering, Hunan Univ., Changsha, Hunan 410082, China.

2. Graduate Student, College of Civil Engineering, Hunan Univ., Changsha, Hunan 410082, China.

3. Professor, Key Laboratory for Damage Diagnosis of Engineering Structures of Hunan Province, Hunan Univ., Changsha 410082, China; College of Civil Engineering, Hunan Univ., Changsha, Hunan 410082, China (corresponding author). ORCID: .

4. Associate Professor, College of Civil Engineering, Hunan Univ., Changsha, Hunan 410082, China.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Building and Construction,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29BE.1943-5592.0001655

Reference32 articles.

1. Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks

2. FEA of residual stress in cruciform welded joint of hollow sectional tubes

3. Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks

4. Autonomous Structural Visual Inspection Using Region-Based Deep Learning for Detecting Multiple Damage Types

5. Deep Learning for Critical Infrastructure Resilience

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