Information Technologies applied to probabilistic simulation of geotechnical stability using Python
DOI:
https://doi.org/10.5281/zenodo.21273130Keywords:
probabilistic simulation, geotechnical stability, PythonAbstract
Information Technology (IT) has transformed geotechnical engineering by enabling the development of computational simulations that overcome the limitations of traditional deterministic methods. In this context, the objective of this study was to design a probabilistic geotechnical stability simulation tool implemented in Python, integrating Monte Carlo simulation (50,000 iterations) and Rosenblueth point estimation (32 combinations) to quantify the uncertainty of the Safety Factor (SF) in underground excavations. Methodologically, the approach is quantitative and projective; the scientific libraries NumPy, SciPy, and Matplotlib were used, defining truncated statistical distributions for the input geomechanical parameters. The results showed that the developed digital tool yields an average SF of 3.602 (σ = 0.577), a reliability index β = 4.512, and a failure probability P (FS<1) ≈ 0.00%, with full agreement between the two methods (discrepancy < 1.5%) and a computation time of just 0.12 seconds. The Kolmogorov-Smirnov test validated the log-normal distribution as the functional fit model (KS = 0.0073). It is concluded that the application of information technology using Python constitutes an effective and accessible digital tool for geotechnical risk management, which optimizes support design without compromising safety.
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