A deep learning-based approach for condition assessment of semi-rigid joint of steel frame-Reference-Cited by-同舟云学术

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A deep learning-based approach for condition assessment of semi-rigid joint of steel frame

Published:2021-02 Issue: Volume:34 Page:101946
ISSN:2352-7102
Container-title:Journal of Building Engineering
language:en
Short-container-title:Journal of Building Engineering

Author:

Paral Animesh,Singha Roy Dilip Kr.,Samanta Amiya K.

Funder

Ministry of Human Resource Development

National Institute of Technology Durgapur

Indian Institute of Engineering Science and Technology, Shibpur

Publisher

Elsevier BV

Subject

Mechanics of Materials,Safety, Risk, Reliability and Quality,Building and Construction,Architecture,Civil and Structural Engineering

Reference58 articles.

1. Effect of damaged circular flange-bolted connections on behaviour of tall towers, modelled by multilevel sub-structuring;Blachowski;Eng. Struct.,2016

2. Proof-of-concept study of monitoring bolt connection status using a piezoelectric based active sensing method;Wang;Smart Mater. Struct.,2018

3. Vision-based detection of loosened bolts using the Hough transform and support vector machines;Cha;Autom. ConStruct.,2016

4. Image registration-based bolt loosening detection of steel joints;Kong;Sensors,2018

5. Bolt looseness detection based on piezoelectric impedance frequency shift;Shao;Appl. Sci.,2016

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1. A novel CNN architecture for robust structural damage identification via strain measurements and its validation via full-scale experiments;Measurement;2025-01

2. Damage identification of honeycomb sandwich structures based on Lamb waves and 1D-CNN;Materials Today Communications;2024-08

3. Addressing data scarcity using audio signal augmentation and deep learning for bolt looseness prediction;Smart Materials and Structures;2024-07-04

4. A comprehensive review on health monitoring of joints in steel structures;Smart Materials and Structures;2024-06-19

5. A Data-Driven Method Based on Bidirectional Convolutional Current Neural Network to Detect Structural Damage;Iranian Journal of Science and Technology, Transactions of Civil Engineering;2024-04-17

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