This study presents significant advancements in evaluating slope stability, particularly under varying soil conditions, focusing on the impacts of soft soil and silty clay foundations. Traditional deterministic methods often misrepresent the stability of slopes, leading to potentially unsafe engineering designs. By integrating probabilistic analysis techniques like the Limit Equilibrium Method (LEM) and Monte Carlo Simulation (MCS), this research enhances the reliability of slope stability assessments through improved modeling of soil variability.
Geotechnical engineering commonly relies on factors of safety (FS) to predict slope stability. While these factors provide initial insights, they frequently overlook the natural variability of soil characteristics influenced by factors such as erosion and sedimentation. The current research highlights the importance of factoring this variability by employing random fields, which offer greater accuracy by acknowledging the real-world conditions of soil.
Employing LEM alongside MCS and integrating random fields provides new insight. The study’s methodology examines two common foundation types: soft soil and silty clay. Results indicate marked differences between the failure mechanisms of slopes under these foundations. For example, slopes with soft soil foundations exhibit deep and varied failure slip surfaces, indicating low correlation between different failure paths. This suggests traditional analysis may significantly underestimate slope instability.
Conversely, slopes supported by silty clay foundations show higher stability, with more consistent failure mechanisms. The research concludes, "the sensitivity of internal friction angle to slope stability is much higher than... cohesive under different scales of fluctuation." This observation aids engineers by pinpointing which soil characteristics most affect slope stability.
By illustrating the different behaviors of slope stability across foundational soil types, this study provides actionable insights for engineers. The findings indicate the necessity for advanced probabilistic analysis methods to avoid the catastrophic failures often associated with overestimations of slope stability. The research enriches the existing literature by documenting the statistical properties of soil variability and proposing improved assessments for engineering practices.
This research lays the groundwork for future studies intended to incorporate additional factors such as external forces on slopes, including rainfall and seismic activity, which were outside the scope of the present findings. It also suggests methodology improvements for even more accurate assessments of soil variability under other conditions.
These findings are expected to influence future geotechnical engineering practices significantly. By emphasizing the adoption of probabilistic stability assessments, they advocate for preemptively addressing slope failures before occur, potentially saving lives and resources.
Overall, this study offers pivotal insights necessary for improving slope stability analysis encompassing natural soil variability. The methodologies proposed here could transform approaches to geotechnical stability assessments and lead to more resilient engineering designs.