Evaluating the triaxial strength of misis fault breccia using artificial neural networks analysis
DOI:
https://doi.org/10.17159/Abstract
Falling into the weak rocks category, fault breccias have extremely poor engineering properties. These pebbles typically cause issues with slopes, subterranean construction, and building projects. Professionals will benefit from the creation of some predictive models for fault breccia triaxial strength, as smooth specimen preparation is typically challenging and time-consuming. The purpose of this study is to develop some predictive models for the differential stress (Δσ) based on physical and textural properties. Artificial neural networks (ANNs) were used to analyze data related to Misis Fault breccia. Initially, models with modest correlation coefficients were created using multiple regression analysis. After that, the regression models and three distinct ANN models were contrasted. Regression models are weaker and less trustworthy than ANNs models, as demonstrated by this comparison. Pointed out is the practicality and ease of use of the ANNs model with S-wave velocity and VBP. Ultimately, it can be concluded that ANNs analysis provides a reliable indirect method for predicting the Δσ of Misis Fault Breccia.
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Copyright (c) 2026 Sair Kahraman, Michael Alber, Osman Gunaydin, Mustafa Fener
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