Predicting penetration depth in ultra-high-performance concrete targets under ballistic impact: An interpretable machine learning approach augmented by deep generative adversarial network

Predicting penetration depth in ultra-high-performance concrete targets under ballistic impact: An interpretable machine learning approach augmented by deep generative adversarial network

In recent decades, numerous explosions and ballistic attacks have caused significant global loss of life and property. Ultra-high-performance concrete (UHPC) minimizes blast and impact damage to structures and can be applied to protective walls and bunkers. Many researchers have proposed methods to...

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Bibliographic Details
Main Authors: Majid Khan, Muhammad Faisal Javed, Nashwan Adnan Othman, Sardar Kashif Ur Rehman, Furqan Ahmad
Format: Article
Language:English
Published: Elsevier 2025-03-01
Series:Results in Engineering
Subjects:
Ultra-high-performance concrete
Penetration depth
Ballistic impact
Explainable machine learning
Deep generative adversarial network
Online Access:http://www.sciencedirect.com/science/article/pii/S2590123024021522
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