A novel machine learning-based approach for nonlinear analysis and in-situ assessment of masonry

Adaileh, Ahmad; Ghiassi, Bahman; Briganti, Riccardo

Abstract

Predicting the local and global mechanical response of masonry structures or estimating their in-situ properties are critical and challenging for the design or assessment of these structures. This article presents a fast prediction/assessment model, developed using a conditional generative adversarial neural network (cGAN) to address this challenge. For the first time, the model shows to be capable of establishing a relationship between masonry microstructural features and full-field local/global mechanical response, and vice-versa, overpassing the path dependency of the non-linear mechanical problems. The strain maps and reaction forces of masonry panels are predicted at any load level from images of masonry panels, which embedded information regarding the materials properties and loading scenarios only in colours, without having any prior knowledge of the material properties and constitutive laws and without the need to access these fields in previous loading levels. Also, it is shown that the mechanical properties of masonry constituents can be predicted from full-field strain maps and loading scenarios using the developed model. This promises to become a revolutionary metamodel for the expensive finite element simulations of masonry or to support in-situ/laboratory experimental testing in the identification of masonry material properties.

Citation

Adaileh, A., Ghiassi, B., & Briganti, R. (2023). A novel machine learning-based approach for nonlinear analysis and in-situ assessment of masonry. Construction and Building Materials, 408, Article 133291. https://doi.org/10.1016/j.conbuildmat.2023.133291

• Journal Article Type: Article
• Acceptance Date: Sep 7, 2023
• Online Publication Date: Oct 7, 2023
• Publication Date: Dec 8, 2023
• Deposit Date: Oct 10, 2024
• Publicly Available Date: Oct 10, 2024
• Journal: Construction and Building Materials
• Print ISSN: 0950-0618
• Electronic ISSN: 1879-0526
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 408
• Article Number: 133291
• DOI: https://doi.org/10.1016/j.conbuildmat.2023.133291
• Keywords: Deep generative modelling, Machine learning, cGAN, Image-to-image translation, Masonry, Micro-modelling, Full-field mechanical response, In-situ properties estimation
• Public URL: https://nottingham-repository.worktribe.com/output/25956901
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0950061823030088