Advanced method for estimating the volumetric intensity along tunnels using ANN

Simulating realistic scenarios with numerical models often demands substantial computational resources, which can be excessively time-consuming. In complex Discrete Fracture Network (DFN) simulations where mutual influence among fracture parameters is crucial, efficient Artificial Intelligence (AI) algorithms offer a promising solution. This study focuses on the Monte Seco tunnel in Brazil, employing Artificial Neural Networks (ANN) with the Levenberg-Marquardt Algorithm (ANN-LM) to estimate Volumetric discontinuity intensity (P32). Comparative analysis with traditional DFN-based methods reveals superior predictive performance of the ANN model over Multiple Linear Regression (MLR). MATLAB was utilized for implementation, considering the interdependence of geometric parameters across fracture sets to estimate P32 values. Sensitivity analysis identified correlations between F1 parameters (density and trace length) and P32 estimates for F2, aiding in predicting potential tunnel instability. A Graphical User Interface (GUI) was developed to streamline calculations, replacing cumbersome spreadsheet methods.

Mots clés

discontinuity intensity; discontinuity persistence; rock exposure; tunnel; artificial neural netwok