Evaluating the triaxial strength of Misis fault breccia using artificial neural networks analysis

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 timeconsuming. 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 were used to analyse data related to Misis fault breccia. Initially, models with moderate (noticeable, but not good) correlation coefficients were created using multiple regression analysis. After that, the regression models and three distinct artificial neuron network models were contrasted. Regression models are weaker and less trustworthy than artificial neuron network models, as demonstrated by this comparison. Pointed out is the practicality and ease of use of the artificial neuron network model with S-wave velocity and volumetric block proportion. Ultimately, it can be concluded that artificial neuron networks analysis provides a reliable indirect method for predicting the differential stress of Misis fault breccia.