Uncertainty Analysis and Quantification of Rainfall-Induced Slope Instability in Fine-Grained Clayey Soils
This study investigates rainfall-induced slope instability in fine-grained clayey soils through a probabilistic and sensitivity analysis framework that integrates spatial variability. Moving beyond traditional deterministic methods, Monte Carlo simulations were employed to quantify uncertainty in ge...
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| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Geotechnics |
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| Online Access: | https://www.mdpi.com/2673-7094/5/2/31 |
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| author | Samuel A. Espinosa Fuentes M. Hesham El Naggar |
| author_facet | Samuel A. Espinosa Fuentes M. Hesham El Naggar |
| author_sort | Samuel A. Espinosa Fuentes |
| collection | DOAJ |
| description | This study investigates rainfall-induced slope instability in fine-grained clayey soils through a probabilistic and sensitivity analysis framework that integrates spatial variability. Moving beyond traditional deterministic methods, Monte Carlo simulations were employed to quantify uncertainty in geotechnical parameters—unit weight, cohesion, and friction angle—modeled as random fields with a 1 m spatial resolution. This approach realistically captures natural soil heterogeneity and its influence on slope behavior during rainfall events. Transient seepage and slope stability analyses were performed using SEEP/W and SLOPE/W, respectively, with the Spencer method ensuring full equilibrium. This study examined how slope height, inclination, rainfall intensity and duration, and soil properties affect the factor of safety (FS). The results showed that higher rainfall intensity and longer durations significantly increase failure risk. For example, under 9 mm/h rainfall for 48 h, slopes taller than 10 m at 45° inclination exhibited failure probabilities over 30%. At 20 m, FS dropped to 0.68 with a 100% probability of failure. Sensitivity analysis confirmed cohesion and friction angle as key stabilizing factors, though their impact diminishes with infiltration. A dataset of 9984 slope scenarios was generated, supporting future machine learning applications for risk assessment and climate-resilient slope design. |
| format | Article |
| id | doaj-art-dc9b9d1c2d2a41a6a78c468e03dcbfd9 |
| institution | Kabale University |
| issn | 2673-7094 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Geotechnics |
| spelling | doaj-art-dc9b9d1c2d2a41a6a78c468e03dcbfd92025-08-20T03:27:10ZengMDPI AGGeotechnics2673-70942025-05-01523110.3390/geotechnics5020031Uncertainty Analysis and Quantification of Rainfall-Induced Slope Instability in Fine-Grained Clayey SoilsSamuel A. Espinosa Fuentes0M. Hesham El Naggar1Geotechnical Research Center, Department of Civil and Environmental Engineering, University of Western Ontario, London, ON N6A 5B9, CanadaGeotechnical Research Center, Department of Civil and Environmental Engineering, University of Western Ontario, London, ON N6A 5B9, CanadaThis study investigates rainfall-induced slope instability in fine-grained clayey soils through a probabilistic and sensitivity analysis framework that integrates spatial variability. Moving beyond traditional deterministic methods, Monte Carlo simulations were employed to quantify uncertainty in geotechnical parameters—unit weight, cohesion, and friction angle—modeled as random fields with a 1 m spatial resolution. This approach realistically captures natural soil heterogeneity and its influence on slope behavior during rainfall events. Transient seepage and slope stability analyses were performed using SEEP/W and SLOPE/W, respectively, with the Spencer method ensuring full equilibrium. This study examined how slope height, inclination, rainfall intensity and duration, and soil properties affect the factor of safety (FS). The results showed that higher rainfall intensity and longer durations significantly increase failure risk. For example, under 9 mm/h rainfall for 48 h, slopes taller than 10 m at 45° inclination exhibited failure probabilities over 30%. At 20 m, FS dropped to 0.68 with a 100% probability of failure. Sensitivity analysis confirmed cohesion and friction angle as key stabilizing factors, though their impact diminishes with infiltration. A dataset of 9984 slope scenarios was generated, supporting future machine learning applications for risk assessment and climate-resilient slope design.https://www.mdpi.com/2673-7094/5/2/31slope stabilityprobabilistic studysensitivity studyrainfalllandslide |
| spellingShingle | Samuel A. Espinosa Fuentes M. Hesham El Naggar Uncertainty Analysis and Quantification of Rainfall-Induced Slope Instability in Fine-Grained Clayey Soils Geotechnics slope stability probabilistic study sensitivity study rainfall landslide |
| title | Uncertainty Analysis and Quantification of Rainfall-Induced Slope Instability in Fine-Grained Clayey Soils |
| title_full | Uncertainty Analysis and Quantification of Rainfall-Induced Slope Instability in Fine-Grained Clayey Soils |
| title_fullStr | Uncertainty Analysis and Quantification of Rainfall-Induced Slope Instability in Fine-Grained Clayey Soils |
| title_full_unstemmed | Uncertainty Analysis and Quantification of Rainfall-Induced Slope Instability in Fine-Grained Clayey Soils |
| title_short | Uncertainty Analysis and Quantification of Rainfall-Induced Slope Instability in Fine-Grained Clayey Soils |
| title_sort | uncertainty analysis and quantification of rainfall induced slope instability in fine grained clayey soils |
| topic | slope stability probabilistic study sensitivity study rainfall landslide |
| url | https://www.mdpi.com/2673-7094/5/2/31 |
| work_keys_str_mv | AT samuelaespinosafuentes uncertaintyanalysisandquantificationofrainfallinducedslopeinstabilityinfinegrainedclayeysoils AT mheshamelnaggar uncertaintyanalysisandquantificationofrainfallinducedslopeinstabilityinfinegrainedclayeysoils |