AHMAD Furquan, SAMUI Pijush, MISHRA S. S.. 2024: Machine learning-enhanced Monte Carlo and subset simulations for advanced risk assessment in transportation infrastructure. Journal of Mountain Science, 21(2): 690-717. DOI: 10.1007/s11629-023-8388-8
Citation: AHMAD Furquan, SAMUI Pijush, MISHRA S. S.. 2024: Machine learning-enhanced Monte Carlo and subset simulations for advanced risk assessment in transportation infrastructure. Journal of Mountain Science, 21(2): 690-717. DOI: 10.1007/s11629-023-8388-8

Machine learning-enhanced Monte Carlo and subset simulations for advanced risk assessment in transportation infrastructure

  • The maintenance of safety and dependability in rail and road embankments is of utmost importance in order to facilitate the smooth operation of transportation networks. This study introduces a comprehensive methodology for soil slope stability evaluation, employing Monte Carlo Simulation (MCS) and Subset Simulation (SS) with the "UPSS 3.0 Add-in" in MS-Excel. Focused on an 11.693-meter embankment with a soil slope (inclination ratio of 2H: 1V), the investigation considers earthquake coefficients (kh) and pore water pressure ratios (ru) following Indian zoning requirements. The chance of slope failure showed a considerable increase as the Coefficient of Variation (COV), seismic coefficients (kh), and pore water pressure ratios (ru) experienced an escalation. The SS approach showed exceptional efficacy in calculating odds of failure that are notably low. Within computational modeling, the study optimized the worst-case scenario using ANFIS-GA, ANFIS-GWO, ANFIS-PSO, and ANFIS-BBO models. The ANFIS-PSO model exhibits exceptional accuracy (training R2 = 0.9011, RMSE = 0.0549; testing R2 = 0.8968, RMSE = 0.0615), emerging as the most promising. This study highlights the significance of conducting thorough risk assessments and offers practical insights into evaluating and improving the stability of soil slopes in transportation infrastructure. These findings contribute to the enhancement of safety and reliability in real-world situations.
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