Prognosticating fabric-reinforced cementitious matrix-to-masonry bond and failure mechanisms using novel tabular variational autoencoder-augmented probabilistic model

Fabric-reinforced cementitious matrix (FRCM) composite strengthening has emerged as an environmentally friendly and less invasive solution, and entails material compatibility with masonry substrates, and hence emerges as the sustainable solution for structural restoration. But the performance of the...

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Veröffentlicht in:	Engineering applications of artificial intelligence Jg. 163; S. 113059
Hauptverfasser:	Kumar, Aman, Marani, Afshin, Abbas, Asim, Nehdi, Moncef L.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.01.2026
Schlagworte:	Bayesian neural network Bond strength Fiber-reinforced cementitious matrix Masonry Natural gradient boosting Probabilistic learning Synthetic data Tabular variational autoencoder Fiber-reinforced cementitious matrix Bond strength Natural gradient boosting Tabular variational autoencoder Masonry Probabilistic learning Bayesian neural network Synthetic data
ISSN:	0952-1976
Online-Zugang:	Volltext
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