Key Takeaways & Executive Findings
- •• 3D CEL random analyses capture complex failure modes (lateral retrogression, asynchronous block mobilization) that 2D models miss. • 3D models predict longer runout distances (13.76 m vs 11.92 m), wider influence zones (11.35 m vs 8.73 m), and higher velocities (4.66 m/s vs 3.94 m/s) than 2D. • 3D analyses yield lower variability (COV 0.10 for runout) due to spatial averaging across slope width. • 2D models significantly underpredict near-field failure probabilities (48.8% vs 89.9% at 12 m from toe), underscoring the need for 3D probabilistic hazard assessment.
Abstract
Retrogressive landslides in sensitive clays pose significant risks to nearby infrastructure, as natural toe erosion or localized disturbances can trigger progressive block failures. While prior studies have largely relied on two-dimensional (2D) large-deformation analyses, such models overlook key three-dimensional (3D) failure mechanisms and variability effects. This study develops a 3D probabilistic framework by integrating the Coupled Eulerian–Lagrangian (CEL) method with random field theory to simulate retrogressive landslides in spatially variable clay. Using Monte Carlo simulations, we compare 2D and 3D random large-deformation models to evaluate failure modes, runout distances, sliding velocities, and influence zones. The 3D analyses captured more complex failure modes—such as lateral retrogression and asynchronous block mobilization across slope width. Additionally, the 3D analyses predict longer mean runout distances (13.76 vs. 11.92 m), wider mean influence distance (11.35 vs. 8.73 m), and higher mean sliding velocities (4.66 vs. 3.94 m/s) than their 2D counterparts. Moreover, 3D models exhibit lower coefficients of variation (e.g., 0.10 for runout distance) due to spatial averaging across slope width. Probabilistic hazard assessment shows that 2D models significantly underpredict near-field failure probabilities (e.g., 48.8% vs. 89.9% at 12 m from the slope toe). These findings highlight the limitations of 2D analyses and the importance of multi-directional spatial variability for robust geohazard assessments. The proposed 3D framework enables more realistic prediction of landslide mobility and supports the design of safer, risk-informed infrastructure.
1. Introduction
Retrogressive landslides in sensitive clays represent one of the most critical geohazards to onshore and offshore infrastructure [1]. Initial slope failures typically result from natural toe erosion or localized disturbances—such as deep-sea mining operations—which destabilize slopes and trigger progressive instability [2]. These failures propagate retrogressively, mobilizing large volumes of soil and potentially causing catastrophic damage to nearby structures and pipelines [1,3,4]. Such failures in soft cohesive sediments are characterized by complex, progressive deformation patterns evolving both spatially and temporally.
As shown in Fig. 1, retrogressive landslides commonly feature sequential block collapses, irregular failure surfaces, and pronounced lateral variability—features consistently documented in field observations [5–7]. As geotechnical development increasingly encroaches upon landslide-prone areas, there is a pressing need for advanced numerical tools that can realistically capture the three-dimensional nature of these phenomena and account for spatial variability in soil properties.
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CHEN Xuejian, REN Shunping, GUO Xingsen, WANG Yueying, LIU Fei, NGUYEN Hoang, SOUSA Rita Leal (2025). Comparative modelling of retrogressive landslide runout: 2D and 3D random large-deformation analyses using coupled Eulerian-Lagrangian method. SinoTechIntel Verified Research. https://doi.org/10.1016/j.ijmst.2025.10.002
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Frequently Asked Questions
What is the main advantage of 3D CEL analysis over 2D for retrogressive landslides?
3D CEL analysis captures complex failure modes such as lateral retrogression and asynchronous block mobilization across slope width, which are missed in 2D models. It also predicts longer runout distances, wider influence zones, and higher sliding velocities, providing more realistic hazard assessments.
How does spatial variability affect landslide runout predictions?
Spatial variability in soil properties influences failure mechanisms and runout behavior. 3D models account for multi-directional variability, leading to lower coefficients of variation (e.g., 0.10 for runout distance) due to spatial averaging across slope width, whereas 2D models may overestimate variability.
Why do 2D models underpredict failure probabilities near the slope toe?
2D models neglect lateral spreading and three-dimensional failure mechanisms, resulting in narrower influence zones and lower failure probabilities. For instance, at 12 m from the toe, 2D predicted 48.8% failure probability versus 89.9% from 3D, highlighting the need for 3D probabilistic analysis.
What is the Coupled Eulerian-Lagrangian (CEL) method used for in this study?
The CEL method is used to simulate large-deformation problems like retrogressive landslides, where the soil undergoes extreme distortion. It combines Eulerian and Lagrangian formulations to handle material flow and interface interactions, enabling realistic modeling of progressive failure and runout.
How can the proposed 3D framework aid infrastructure design?
By providing more accurate predictions of landslide runout distances, velocities, and influence zones, the 3D framework supports risk-informed design of infrastructure near slopes, helping to mitigate potential damage and enhance safety.
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