Geotechnical evaluation of embankment stability in seismic zones using Monte Carlo and subset simulations within an LRFD framework aided by machine learning
This study highlights the importance of thorough risk evaluations to ensure resilient railway and roadway embankments. By integrating Monte Carlo simulation (MCS) and subset simulation (SS) methodologies within the LRFD framework, this study examines the stability and risk assessments of a typical s...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025023928 |
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| Summary: | This study highlights the importance of thorough risk evaluations to ensure resilient railway and roadway embankments. By integrating Monte Carlo simulation (MCS) and subset simulation (SS) methodologies within the LRFD framework, this study examines the stability and risk assessments of a typical soil slope, specifically concentrating on an embankment with a height of 11.693 m and a soil slope angle of 2H:1 V. This study utilizes the ''UPSS 3.0 Add-in'' for MS Excel to conduct the analysis. This evaluation includes seismic scenarios with values of 0.12 and 0.14 for Zones IV and III, respectively, in accordance with the specifications of the Indian zoning map and RDSO guidelines. The study also examines variations in pore water pressure ratios (0.0, 0.05, 0.10) and coefficients of variation (COVs), noting their significant impact on the probability of failure (pf) of the embankment. The results indicate a significant increase in pf with increasing COVs, ru, and kh values, with pf increasing to 40% under certain conditions. Advanced computational modeling using three random forest-based models: RF-YYPO, RF-BWOA, and RF-SMA. The RF-YYPO model outperformed the other models, showing high efficacy, with R2 values of 0.9633 during training and 0.9072 during testing, and achieving the lowest RMSEs. These findings enhance embankment safety in seismic zones and advance geotechnical engineering practice. This study uniquely integrates probabilistic techniques with hybrid machine learning optimization algorithms (RF-YYPO, RF-BWOA, and RF-SMA) under an LRFD framework, which has not been previously implemented for seismic slope stability of railway embankments. |
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| ISSN: | 2590-1230 |