Advanced method for estimating the volumetric intensity along tunnels using ANN

ABSTRACT

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.

KEYWORDS:

• Discontinuity intensity
• discontinuity persistence
• rock exposure
• tunnel
• Artificial Neural Network

Acknowledgments

The first author thanks the financial support by the Pró-Reitoria de Inclusão e Pertencimento (PRPI) - USP for research funding (Relocation 202350201307, Grant number nº 22.1.9345.1.2), and the São Paulo Research Foundation (FAPESP; Grant number nº 2023/06123-9).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the Pró-Reitoria de Inclusão e Pertencimento (PRPI) - USP for research funding (Relocation 202350201307, Grant number nº 22.1.9345.1.2), and the São Paulo Research Foundation (FAPESP; Grant number nº 2023/06123-9).