A Revolutionary Deep Generative Modelling for Linear and Nonlinear Analysis of Masonry

Adaileh, Ahmad; Ghiassi, Bahman; Briganti, Riccardo

Abstract

The primary challenge in designing and analysing masonry structures is predicting their mechanical response. In particular, fast and direct prediction of masonry mechanical response field is desirable. This motivates the present study to introduce an innovative model using a conditional generative adversarial neural network (cGAN) for this purpose within linear or nonlinear ranges. This model establishes a direct connection between masonry microstructural features and both local and global mechanical responses full-fields, overpassing the path dependency of nonlinear mechanical problems. The model predicts strain maps and reaction forces of masonry panels under different loading scenarios and at any level of loading, solely from masonry panels images that encode material properties and loading scenarios into different shades of colours, without the need for information about material constitutive laws. This revolutionary approach holds the potential to serve as metamodel alternative to computationally expensive finite element (FE) simulations for masonry structures.

Citation

Adaileh, A., Ghiassi, B., & Briganti, R. (2024, July). A Revolutionary Deep Generative Modelling for Linear and Nonlinear Analysis of Masonry. Presented at 18th International Brick and Block Masonry Conference, Birmingham, UK

• Presentation Conference Type: Edited Proceedings
• Conference Name: 18th International Brick and Block Masonry Conference
• Start Date: Jul 21, 2024
• End Date: Jul 24, 2024
• Acceptance Date: Mar 29, 2024
• Online Publication Date: Dec 13, 2024
• Publication Date: Dec 13, 2024
• Deposit Date: Jan 27, 2025
• Publicly Available Date: Dec 14, 2025
• Publisher: Springer Nature
• Peer Reviewed: Peer Reviewed
• Pages: 175-183
• Series Title: Lecture Notes in Civil Engineering
• Series Number: 613
• Series ISSN: 2366-2557
• Book Title: 18th International Brick and Block Masonry Conference: Proceedings of IB2MaC 2024. Volume 1
• ISBN: 9783031733130
• DOI: https://doi.org/10.1007/978-3-031-73314-7_13
• Public URL: https://nottingham-repository.worktribe.com/output/42841162
• Publisher URL: https://link.springer.com/chapter/10.1007/978-3-031-73314-7_13