Monitor the Strength Status of Buildings Using Hybrid Machine Learning Technique-Reference-Cited by-同舟云学术

Monitor the Strength Status of Buildings Using Hybrid Machine Learning Technique

Published:2023 Issue: Volume:11 Page:26441-26458
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

M Vishnu Vardhana Rao¹,Chaparala Aparna²,Jain Praphula Kumar³^ORCID,Karamti Hanen⁴^ORCID,Karamti Walid⁵

Affiliation:

1. Department of Computer Science and Engineering, Vignan’s Institute of Management and Technology for Women, Kondapur, Ghatkesar, Medchal, Telangana, India

2. Department of Computer Science and Engineering, R. V. R. & J. C. College of Engineering, Guntur, Andhra Pradesh, India

3. Department of Computer Engineering and Applications, GLA University, Mathura, Uttar Pradesh, India

4. Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia

5. Department of Computer Science, College of Computer, Qassim University, Buraydah, Saudi Arabia

Funder

Princess Nourah Bint Abdulrahman University Researchers Supporting through Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10049614.pdf?arnumber=10049614

Reference55 articles.

1. Building damage characteristics based on surveyed data and fragility curves of the 2011 Great East Japan tsunami

2. A Study on the Influential Factors on Building Damage in Sri Lanka During the 2004 Indian Ocean Tsunami

3. New Building Damage Assessment Method Based on Numerical Tsunami Simulation and Analytical Force Estimation: Case study of the 2011 Tohoku tsunami

4. Empirical fragility assessment of buildings affected by the 2011 Great East Japan tsunami using improved statistical models

5. Human burn depth and grafting prognosis using ResNeXt topology based deep learning network

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