Deep learning application for monitoring degradation in nuclear safety systems-Reference-Cited by-同舟云学术

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Deep learning application for monitoring degradation in nuclear safety systems

Published:2024-09 Issue: Volume: Page:1-15
ISSN:1964-8189
Container-title:European Journal of Environmental and Civil Engineering
language:en
Short-container-title:European Journal of Environmental and Civil Engineering

Author:

Sandhu Harleen Kaur¹,Sauers Serena²,Bodda Saran Srikanth³,Gupta Abhinav⁴

Affiliation:

1. Postdoctoral Research Scholar, CCEE, North Carolina State University, Raleigh, NC, USA

2. Staff Engineer, WDP & Associates, Manassas, VA, USA

3. Research Faculty, CCEE, NCSU, Raleigh, NC, USA

4. Director Center for Nuclear Energy Facilities and Structures, NCSU, Raleigh, NC, USA

Funder

Center for Nuclear Energy Facilities and Structures at North Carolina State University

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/19648189.2024.2391944

Reference31 articles.

1. A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications

2. Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

3. Barlat F. & Lian J. (1989). Element reference. p. 1388.

4. Bezler P. Hartzman M. & Reich M. (1980). Piping benchmark problems. Technical Report NUREG–1677.

5. Bodda S. (2018). Multi-Hazard Risk Assessment of a Flood Defense Structure. [Master’s thesis]. North Carolina State University.

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