Predicting the Pore-Pressure and Temperature of Fire-Loaded Concrete by a Hybrid Neural Network

DOI10.1142/s0219876221420111OpenAlexW4221079901MaRDI QIDQ6172999

Unnamed Author, Yiming Zhang, Qi Liu, Unnamed Author

Publication date: 21 July 2023

Published in: International Journal of Computational Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1142/s0219876221420111

zbMATH Keywords

autoencodercoupled analysisfully-connected neural networkfire spalling

Mathematics Subject Classification ID

Mechanics of deformable solids (74-XX) Biology and other natural sciences (92-XX)

Related Items (1)

Virtual displacement based discontinuity layout optimization

Cites Work

• Unnamed Item
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• An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications
• Thermo‐hydro‐mechanical analysis of partially saturated porous materials
• Modelling of hygro-thermal behaviour and damage of concrete at temperature above the critical point of water

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