Convolutional Neural Network Approach for Vibration-Based Damage State Prediction in a Reinforced Concrete Building-Reference-Cited by-同舟云学术

Convolutional Neural Network Approach for Vibration-Based Damage State Prediction in a Reinforced Concrete Building

Published:2024-03 Issue:2 Volume:38 Page:
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Whiteman Michael L.¹^ORCID,Marin-Artieda Claudia C.²^ORCID,Tezcan Jale³

Affiliation:

1. Postdoctoral, Dept. of Civil and Environmental Engineering, Howard Univ., Washington, DC 20059 (corresponding author). ORCID: .

2. Professor, Dept. of Civil and Environmental Engineering, Howard Univ., Washington, DC 20059. ORCID: .

3. Professor, Dept. of Civil and Environmental Engineering, Southern Illinois Univ., Carbondale, IL 62901.

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JCCEE5.CPENG-5511

Reference25 articles.

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

2. Statistical analysis of the modal properties of a seismically-damaged five-story RC building identified using ambient vibration data

3. Barthorpe R. J. 2010. “On model and data based approaches to structural health monitoring.” Doctoral dissertation Dept. of Mechanical Engineering Univ. of Sheffield.

4. Optimization Procedure to Correct Stiffness and Flexibility Matrices Using Vibration Tests

5. Buckreis T. W. Allen W. Pengfei B. Scott and S. Jonathan. 2021. Microtremor data collected in Sacramento-San Joaquin Delta region of California. Gainesville FL: DesignSafe-CI. https://doi.org/10.17603/ds2-dk6t-8610v1.

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