Training and Integrating a Machine-Learning-Based Shell Element in Reinforced Concrete Simulations
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Author / Producer
Date
2025
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
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Abstract
The finite element analysis (FEA) provides state-of-the-art solutions for any reinforced concrete (RC) shell structure. Emerging surrogate models leveraging physics-based machine learning (ML) may provide viable alternatives by improving speed and enabling differentiability while maintaining physical consistency. This study develops a linear elastic ML-FEA hybrid framework for RC layered shell elements to create a Surrogate 3D-Shell element. A Deep Operator Network based on data generated via local simulation yields the most accurate and generalisable hybrid model among all tested approaches. With a case study of a plate in combined membrane and bending action, the ML-FEA hybrid model is shown to be a promising alternative to traditional FEA.
Permanent link
Publication status
published
External links
Book title
Proceedings of the fib Symposium 2025
Journal / series
Volume
71
Pages / Article No.
2136 - 2145
Publisher
International Federation for Structural Concrete
Event
fib Symposium 2025: Concrete Structures - extend lifespan, limit impacts
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
09469 - Kaufmann, Walter / Kaufmann, Walter
09620 - Coros, Stelian / Coros, Stelian
02219 - ETH AI Center / ETH AI Center
Notes
Conference lecture held on June 16, 2025