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Article

Deformation and Failure Mechanism of Bedding Rock Landslides Based on Stability Analysis and Kinematics Characteristics: A Case Study of the Xing’an Village Landslide, Chongqing

by
Jingyi Zeng
1,2,
Zhenwei Dai
1,*,
Xuedong Luo
2,
Weizhi Jiao
1,2,
Zhe Yang
1,2,
Zixuan Li
1,2,
Nan Zhang
3 and
Qihui Xiong
4
1
Wuhan Center, China Geological Survey (Geosciences Innovation Center of Central South China), Wuhan 430205, China
2
Institute of Geological Survey, China University of Geosciences, Wuhan 430074, China
3
China Institute of Geo-Environment Monitoring, Beijing 100081, China
4
Chongqing Bureau of Geology and Mineral Resources Exploration and Development Nanjiang Hydrogeology Engineering Geology Team, Chongqing 401121, China
*
Author to whom correspondence should be addressed.
Water 2025, 17(5), 767; https://doi.org/10.3390/w17050767
Submission received: 17 January 2025 / Revised: 26 February 2025 / Accepted: 5 March 2025 / Published: 6 March 2025
Browse Figures
Figure 1
<p>The geographical location of Xing’an village landslide.</p> "> Figure 2
<p>(<b>a</b>) Geological plan of the Xing’an landslide, (<b>b</b>) Drilling core samples, and (<b>c</b>) Rear tension cracks and explored grayish-white mudstone.</p> "> Figure 3
<p>Schematic diagram of 2–2′ profile of Xing’an landslide.</p> "> Figure 4
<p>Cataclastic rock mass in the landslide.</p> "> Figure 5
<p>Groundwater seepage was observed during excavation at the toe of the landslide.</p> "> Figure 6
<p>Tension cracks at the rear edge of the potential instability zone.</p> "> Figure 7
<p>Schematic diagram of the final morphology of the bedding slope.</p> "> Figure 8
<p>(<b>a</b>) Temporal variation in velocities at M1, and (<b>b</b>) Displacement-based variation in velocities at M2.</p> "> Figure 9
<p>(<b>a</b>) Variation in landslide velocity and thickness over time at M3, and (<b>b</b>) variation in landslide velocity and thickness over time at M4.</p> "> Figure 10
<p>Morphological characteristics of landslide deposits.</p> "> Figure 11
<p>Thickness variation in the unstable landslide.</p> "> Figure 12
<p>(<b>a</b>) Pre-deformation stage, (<b>b</b>) Slope-toe excavation, (<b>c</b>) Rainfall and slope-toe excavation induce lower collapse, forming tensile cracks at the rear, and (<b>d</b>) The lower collapse triggers overall failure in the rear potential instability zone.</p> ">
Versions Notes

Abstract

:
Bedding rock landslides, characterized by their distinct geological structure, are widely distributed and highly susceptible to sliding under external disturbances, resulting in catastrophic events. This study aims to unravel the geomechanical mechanisms governing rainfall-induced instability through an integrated investigation of a representative landslide in Xing’an Village, Chongqing. Employing multidisciplinary approaches, including field monitoring, geotechnical testing, and dynamic numerical modeling, we systematically revealed two critical failure zones: a front failure zone and a rear potential instability zone. Under rainstorm conditions, the safety factor for both zones was 1.02, indicating a marginally unstable state. The DAN-W simulations indicate that the potential instability zone at the rear of the landslide experienced complete failure within 12 s under heavy rainfall, with a maximum run-out distance of 20 m, a maximum velocity of 4.32 m/s, and a maximum deposition thickness of 8.3 m, which could potentially bury the buildings at the toe of the landslide. The low strength and permeability of the mudstone-dominated Badong Formation, characterized by interbedded mudstone, siltstone, and sandstone within the Middle Triassic geological system, provides a fundamental prerequisite for the landslide. Rainwater infiltration into the mudstone layers degraded its mechanical properties, and excavation at the slope base ultimately triggered the landslide initiation. These findings can provide theoretical support for preventing and managing similar bedding rock landslides with similar geological backgrounds.

1. Introduction

With continuous economic development in China, numerous infrastructure projects have been initiated, especially in the complex geological environments of the southwestern mountainous regions [1]. Bedding rock landslides, slope failures characterized by rock masses sliding along bedding planes with particular susceptibility when layer orientation aligns with the slope direction, are influenced by geological structures and weak interlayers, frequently occurring in these regions and leading to severe disasters. In 2003, the Qianjiangping large bedding rock landslide occurred in Shazhen, Zigui County, China. In 2010, Zhouqu County in the Gansu Province experienced devastating debris flows triggered by heavy rains. In 2017, Xinmo Village in Mao County, Aba Tibetan and Qiang Autonomous Prefecture, Sichuan Province, suffered a high-position collapse triggered by heavy rainfall, steep topography, weak lithology, and intense tectonic activity, resulting in numerous casualties. Then, in 2020, a massive landslide struck Chicang Township, Penge Village, Chagou Group, Shuitai County, Liupanshui City, Guizhou Province, burying a large number of houses and causing a severe loss of life. These landslides are characterized by their large scale, sudden occurrence, and significant destructive impact, posing severe threats to human lives and property. Therefore, studying the formation and failure mechanisms of such landslides is crucial for their prevention and early warning.
Over the past decade, substantial research efforts have been dedicated to understanding the multi-phase evolution of bedding rock landslides, with particular emphasis on their lithological controls, kinematic behavior, and quantitative stability assessment methodologies. The scientific community has progressively established sophisticated frameworks for analyzing these structurally controlled slope failures through interdisciplinary approaches combining geomechanical modeling, in situ monitoring, and advanced geostatistical analysis. Yu et al. [2] and Zhao et al. [3]. Regarding the influencing factors of landslides, some researchers have focused on specific factors, such as rainfall, to explore the instability of bedding rock landslides. Cho [4] investigated the interaction between air and water flow and its impact on slope stability. Chang [5] summarized and categorized the macroscopic criteria for the reactivation and instability of debris accumulation under extreme rainfall, drainage, and permeability conditions.
Regarding instability mechanisms, Liu et al. [6] analyzed the causes of landslide initiation, emphasizing that the softening of sliding zone soil due to water infiltration significantly reduces shear strength, leading to slope failure. Liu et al. [7] investigated the characteristics of bedding rock landslides and performed a stability control analysis, highlighting the role of weak structural planes and lithological contrasts in controlling landslide behavior. Furthermore, Yin et al. [8], Zuo et al. [9], and Tang et al. [10] utilized numerical modeling to simulate landslide failure processes, revealing that factors such as steep topography, tectonic stress, and hydrological conditions collectively contribute to the instability of mountain structures. Contemporary geotechnical investigations employing a multivariate analysis and mechanistic modeling have systematically revealed that the synergistic relationships among lithostructural discontinuities (e.g., fault systems, joint networks, and stratigraphic interfaces) and environmental forcings (particularly hydro-meteorological perturbations and tectonic impulses) constitute fundamental determinants in deciphering the multiscale failure processes inherent to slope instability phenomena.
In the realm of stability analysis, several scholars have adopted diverse methodologies. Specifically, Ausilio et al. [11] carried out an in-depth study on the stability of slopes reinforced with piles. They chose to utilize a kinematic approach that was firmly grounded in a limit analysis. This particular method allowed them to comprehensively understand how piles influence slope stability from a kinematic perspective. On the other hand, Michalowski [12] put forward a different yet related idea. He elaborated on a kinematic method for evaluating slope stability when cracks are present. Similar to Ausilio et al., his method was also based on a limit analysis. By doing so, Michalowski was able to account for the complex impact of cracks on slope stability through a kinematic-based limit analysis framework. Kang et al. [13] evaluated the applicability and reliability of the SHALSTAB model for shallow landslide stability analysis in loess gully regions. These research efforts have provided valuable insights into analyzing changing trends and stability in unstable bedding rock slopes. However, these studies primarily focus on back-analyses of already occurred landslides, while predictive studies of unstable bedding rock slopes remain insufficient.
Recent advancements in landslide prediction methodologies demonstrate diverse technical approaches with varying operational constraints. Building upon a conventional slope stability analysis, Chen et al. [14] proposed a modified tangent angle algorithm that enables phase segmentation of slope movements and establishes multi-threshold warning criteria. In the realm of computational intelligence, Zhang et al. [15] introduced an innovative hybrid model integrating metaheuristic optimization (Water Cycle Algorithm) with artificial neural networks, achieving enhanced pattern recognition capabilities for dynamic systems. Nevertheless, the model’s performance remains data-dependent, requiring extensive training datasets with spatiotemporal continuity to ensure reliable parameter calibration. From a geospatial perspective, Casagli et al. [16] systematically evaluated the implementation of earth observation technologies across different geological settings, identifying critical application-specific limitations, including spatial resolution requirements and deformation detection thresholds. This technological diversity underscores the necessity for context-specific selection criteria, particularly considering Korup and Stolle’s [17] demonstration that advanced data mining architectures can maintain prediction accuracy exceeding 85% when processing multivariate geological datasets containing over 10⁴ feature dimensions, albeit requiring the rigorous preprocessing of heterogeneous data sources. However, the vast and growing array of methods makes it challenging for users to decide which model to trust or adopt. Despite significant progress in understanding the spatial patterns of landslides, challenges remain in the regional prediction of large-scale landslides, timing prediction of landslide movements, and slope collapses. These methods have provided substantial experience in landslide prediction and simulation. However, given the complex geological conditions, the combined effects of multiple factors, and resource limitations, improving the precision of predicting unstable landslide hazard zones and simplifying the simulation of landslide evolution for practical and accurate outcomes remain critical challenges.
Numerical simulation is currently a key method for predicting landslide evolution. Hungr and McDougall [18] developed the DAN-W dynamic simulation software (1.0 Beta, 2009) based on the Lagrangian analytical solution of the Saint–Venant equations, introducing frictional and Voellmy rheological models. Santo et al. [19] confirmed these models’ effectiveness in simulating and predicting landslide debris flow. For example, Cheng et al. [20] used DAN-W to simulate debris-flow movement from rock collapse. Hungr et al. [21] back-analyzed many real landslide cases with these models, showing their predictive potential. Gao et al. [22] indicated that DAN-W’s flexibility enables real-landslide simulations under practical disaster and risk assessment constraints.
On 26 July 2020, cracks appeared on the rear slope of the resettlement area of Xing’an Village, Yangshi Town, Fengjie County, Chongqing, and the cracks widened rapidly, severely threatening the lives and property of 29 households and 120 residents and the safety of traffic on Provincial Road S105.
This study integrates comprehensive datasets and systematic field surveys to delineate the geological context of the landslide, with particular emphasis on its deformation patterns and kinematic behavior. Stability assessments under both natural and rainfall conditions were conducted through a dual-method framework: (1) the limit equilibrium theory was applied to quantify the driving-resisting force equilibrium along hypothetical failure surfaces, while (2) the transfer coefficient method addressed geometric complexities inherent to the slope profile. To simulate post-failure dynamics, the DAN-W software was employed, enabling predictions of kinematic trajectories and depositional extents. Through this integrated analytical approach, the failure mechanism was rigorously interrogated. The results contribute actionable insights for risk mitigation strategies targeting bedding rock landslides exhibiting analogous geological configurations and deformation signatures.

2. Geological Conditions and Landslide Features

2.1. Geological Conditions of the Landslide

The landslide is located in Xing’an Village of Yangshi Town, Fengjie County—a northeastern mountainous county in Chongqing Municipality, China. It is approximately 95 km southwest of Fengjie County. With geographical coordinates of 30°48′43.58″ N and 109°3′18.13″ E, as shown in Figure 1. The region exhibits a central subtropical humid monsoon climate, featuring thermally contrasting seasons with mild winter conditions and pronounced summer heat. Annual precipitation totals are substantial (approximately 1565.2 mm evaporation rate) yet demonstrate marked intra-annual variability, with approximately 70% of hydrological activity concentrated during the May–September pluvial phase. This temporal precipitation pattern coexists with persistently elevated atmospheric moisture levels, as evidenced by mean monthly relative humidity values consistently near 70%. The climatic regime is further characterized by clearly demarcated wet–dry cycles that align with seasonal temperature fluctuations.
The study area exhibits a northwest–southeast trending low-relief mountainous terrain (slope angles: 15–18°), with elevation decreasing systematically from 327 m at the northern crown to 287 m at the southern toe of the landslide, creating a 40 m topographic gradient (Figure 2a). This elevation differential establishes a geomorphological framework conducive to gravitational mass movement, particularly along the northwest–southeast oriented slip surface identified through borehole surveys.

2.2. Hydrogeological and Geotechnical Conditions

2.2.1. Material Composition

Figure 2 shows the geological plan of the landslide and the core revealed by the boreholes, and the schematic diagram for the 2–2′ profile of the Xing’an landslide in Figure 3, four monitoring points (M1 to M4) are set separately in the profile diagram. The stratigraphic lithology of the landslide from top to bottom consists of the Quaternary Holocene artificial fill layer (Q4ml), Quaternary landslide deposits (Q4del), Quaternary residual slope layer (Q4el+dl), and the 240-million-year-old mudstone layer (T2b2) from the second section of the Badong rock formation, formed during the Middle Triassic period. The purplish-red mudstone (T2b2-Ms), primarily composed of clay minerals, is exceptionally soft and constitutes the main rock layer in the area. As illustrated in Figure 2c, distinct grayish-white mudstones (T2b2-Ms) were found at the rear of the landslide, where prominent basal striations are observable. The gray-white mudstone is also predominantly composed of clay minerals, has a high montmorillonite content, and occurs in the site as interlayers or lenticular bodies. Borehole ZK3 indicated that the thickness of the gray mudstone varies from 0 to 2.0 m. Integrated with borehole logging data, these findings confirm the slip zone is localized within the fractured belt adjacent to the lithological interface between grayish-white and purplish-red mudstones. The rock mass at the base of this layer is predominantly fractured, with localized clay interlayers observed (Figure 2b).
The strata exhibit a monocline structure characterized by a gently inclined, a continuous sequence of rock layers with slightly disordered orientations. This structural configuration can promote the accumulation of gravitational stress and create potential sliding surfaces along weak bedding planes, thereby contributing to landslide instability. Two main sets of structural fractures are developed within the rock mass:
(1)
The first set of fractures has an orientation of 30° < 78°, with visible extension lengths of 1.5–4.0 m, spacing of 0.5–2.0 m, slight openings, no fillings, and poor bonding.
(2)
The second set has an orientation of 310° < 81°, with visible extension lengths of 0.5–2.0 m, spacing of 0.3–1.5 m, slight openings, and no fillings.
These fractures further weaken the rock mass by providing pathways for water infiltration and reducing overall cohesion, exacerbating the potential for slope failure.
The slip zone is located at the fractured zone near the boundary between the grayish-white mudstone and the purplish-red mudstone in the second member of the Middle Triassic Badong Formation (T2b2). The grayish-white mudstone, which has a high montmorillonite content, rapidly loses its mechanical properties upon exposure to water, making it prone to forming weak interlayers at the boundary with the purplish-red mudstone. In the landslide area, distinct cataclastic rock masses are readily observable in Figure 4. The sliding bed consists of purplish-red mudstone from the second member of the Middle Triassic Badong Formation (T2b2). This mudstone has a muddy texture and a medium-to-thick bedding structure. The primary mineral composition of this material is clay minerals, and it displays exceptionally soft rock characteristics while maintaining a relatively intact rock mass.

2.2.2. Groundwater

The groundwater system in the study area consists of two principal categories: Quaternary pore water and bedrock-weathered fissure water. Quaternary pore water specifically denotes aqueous phases occupying interstitial spaces within Quaternary-period sedimentary deposits. During intense precipitation events, such hydrological constituents exhibit rapid accumulation in surficial strata, triggering dual destabilization mechanisms through both interparticle friction diminution and hydrostatic compression effects. The lubrication-induced reduction in soil shear strength combines synergistically with matrix densification processes, collectively elevating susceptibility to slope failure events, including landslides and sudden ground subsidence phenomena.
Bedrock-weathered fissure water refers to groundwater stored in cracks and fractures within weathered bedrock layers. It is primarily replenished by atmospheric precipitation and flows southeastward toward lower elevations, eventually draining out. Due to the good drainage conditions of the groundwater in the area, this type of soil has low water content.
Based on the above observations and water level monitoring during drilling, it can be concluded that the study area has scarce groundwater resources with simple hydrogeological conditions. After the landslide initiation, a test pit excavated at the front edge of the landslide (Figure 5) revealed minor seepage at its base. This seepage demonstrates two critical processes:
(1)
The mudstone impermeable layer prevents vertical drainage, forcing rainwater to accumulate laterally along the sliding zone.
(2)
The high hydraulic gradient (enhanced by gravity and topographic relief) accelerates pore water transmission toward the sliding surface.
These combined effects enable the rapid saturation of the shear zone during rainfall, reducing effective stress through pore pressure elevation. The immediate strength degradation explains the landslide’s rapid response to rainfall.

3. Methods

3.1. Engineering Geological Investigation

The study adopted drilling and trench excavation methods to comprehensively obtain the geological conditions and deformation characteristics of the Xing’an landslide. The drilling method was selected to achieve a high core recovery rate, ensuring the identification of slip surfaces and detecting weak interlayers. Loose deposits were advanced by no more than 1.0 m per increment, weak interlayers by 0.3 m, and bedrock by 2.0 m per increment. Core retrieval methods were applied to rock cores longer than 35 cm, and the cores were combined with the previous increment for recovery calculation. The recovery rates exceeded 85% for sliding body cores, 95% for weak interlayers, and 90% for bedrock cores. Two exploratory trenches were excavated in the front section of the potential instability zone to identify the material composition and the potential sliding surface. In addition, the trenches were used to collect soil and rock samples, which were then subjected to physical and mechanical property tests according to the “Standard for Test Methods of Engineering Rock Mass” (GB/T 50266-2013) [23].

3.2. Stability Calculation Using the Transfer Coefficient Method

The limit equilibrium theory is one of the earliest and most widely used deterministic analysis methods for soil slope stability. It offers advantages such as simplicity in modeling, concise calculation formulas, applicability to various complex profile shapes, and the ability to account for different loading conditions. Moreover, it could calculate the slope stability safety factor directly, which helps this research to judge the stability of the slope intuitively and quickly and enhance the reliability of the research results.
Employing the transfer coefficient method within a limit equilibrium framework, this investigation conducted stability assessments and computed thrust parameters for slope failure mechanisms. The analytical protocol implemented geometric simplification protocols, approximating both terrain profiles and hypothetical failure surfaces as polylinear configurations. Computational parameters adopted a unit width configuration (1 m) for the sliding mass within a two-dimensional analytical framework.
The equation for calculating the landslide stability coefficient Fs is
F s = ∑ i = 1 n − 1 R i ∏ j = i n − 1 Φ j + R n ∑ i = 1 n − 1 T i ∏ j = i n − 1 Φ j + T n
where
  • Fs is the landslide stability coefficient;
  • Φi stands for the transfer coefficient of the residual sliding force from block i to block i + 1;
  • Ri denotes the anti-sliding force of the sliding body in the i-th calculation segment;
  • Ti represents the sliding force of the sliding body in the i-th calculation segment.

3.3. Landslide Motion Characteristics Analysis

The debris flow simulation employed DAN-W, a numerical computational framework developed by Hungr [24], to model complete kinematic progression and analyze dynamic behavior. This specialized software implements the Dynamic Analysis (DAN) methodology, grounded in the Lagrangian formulation of Saint–Venant equations. The analytical approach discretizes the failure mass into multiple rigid segments while preserving mass integrity through motion. Through the sequential resolution of momentum equilibrium equations for each discrete element using Lagrangian finite difference techniques, the numerical implementation yields quantitative outputs, including velocity profiles, run-out distances, and depositional geometry characteristics.
The DAN model, as the theoretical foundation of DAN-W, conceptualizes the landslide as an equivalent fluid. The landslide’s kinematics process is subsequently inverted and obtained by setting the motion path and direction and selecting appropriate rheological models. While the DAN model provides the theoretical framework and mathematical formulation, DAN-W implements these principles through a user-friendly interface, enabling efficient numerical simulations and the visualization of results. This distinction highlights the complementary roles of the DAN model as a theoretical tool and DAN-W as its practical computational implementation.
Through systematic numerical experimentation, Hungr and McDougall [17], alongside Sosio et al. [25], established that both frictional and Voellmy rheological models effectively capture the kinematic behavior and depositional patterns of slope failure masses. The dual-model framework demonstrates particular efficacy in simulating shear resistance mechanisms through Coulomb-type friction parameters while concurrently modeling velocity-dependent drag effects via turbulent flow components, thereby achieving a comprehensive representation of mass movement dynamics across varied geotechnical conditions. Yin et al. [26] applied these two models in their research. The specific rheological relationship for the frictional model is expressed as follows.
The resistance expression for the frictional model is as follows:
τ = σ 1 − γ μ tan φ
where
  • τ is the resistance at the base of the sliding body (N);
  • σ stands for the total stress perpendicular to the sliding path (Pa);
  • γμ denotes the pore pressure coefficient, i.e., the ratio of pore pressure to total stress;
  • Φ represents the internal friction angle (°).
The resistance expression for the Voellmy model is as follows:
τ = f σ + γ v 2 ξ
where
  • f is the friction coefficient of the sliding body;
  • γ stands for the unit weight of the material (N/m3);
  • v denotes the sliding velocity (m/s);
  • ξ represents the turbulent coefficient (m/s2).

4. Results

4.1. Deformation Characteristics of the Landslide

Based on the deformation characteristics, the landslide can be classified into a frontal failure zone and a rear potential instability zone. The process of failure in the collapse zone can be split into two stages.
At 12:00 on 26 July 2020, the first stage of the landslide took place. At this time, cracks emerged in the landslide area and quickly expanded. Around 17:00, the lower part of the slope gave way and collapsed. As a result, a landslide was formed that had an approximate length of 20 m, an average width of 80 m, and an average thickness of 6 m. This landslide spread over an area of 16,000 m2, and its volume was roughly 96,000 m3. It mainly slid in the direction of 120°. The maximum distance it slid was 3.7 m, and after the sliding, a rear scarp with a height ranging from 5 to 7 m was formed. Field investigations have uncovered a tensile crack zone at the rear of the collapsed area, measuring approximately 70 m in length. The width of this crack zone typically ranges between 2.0 and 3.7 m, while the visible depth extends from 4.0 to 6.5 m, as depicted in Figure 6.
The second stage of failure occurred on 28 July 2020. The collapsed body continued to slide downward, with a maximum sliding distance of approximately 3.9 m. In some areas, bulging of the ground beneath the houses was observed, with a general uplift height of 20 to 30 cm, causing damage to nine houses. Due to the obstruction by frontal buildings, the deformation rate of the landslide slowed but did not cease entirely.
According to on-site surveys and drilling results, up to now, no obvious deformation signs have been detected. Nevertheless, given that the deformation at the front edge of the sliding mass is still ongoing and no effective preventive measures have been taken, the rear edge of the landslide might keep deforming, which leads to its identification as a potential instability area.
The potential instability zone has an average width of approximately 70 m, a length of about 90 m, and a total area of 6300 m2. The sliding body has an average thickness of about 4.5 m, with a total volume of 28,400 m3.

4.2. Stability Analysis of the Landslide

According to the GB/T 50266-2013 Standard for Test Methods of Engineering Rock Mass, core samples were drilled in the potential instability zone and failure zone of the Xing’an Village landslide for laboratory testing. The mechanical properties of the rock samples were determined using the direct shear test method. Additionally, comparisons have been made with data from previous studies, and our results fall within the same range, indicating the reliability and consistency of the data [27]. The test results are summarized in Table 1.
A computational model was established using the data from Profile 2–2′. The potential sliding surface was the contact surface between the clay with gravel and the mudstone, with slight undulations, and it was modeled as a straight-line type for calculation. The summarized calculation results are shown in Table 2.
Back-calculation results demonstrate that under heavy rainfall conditions, the failure zone exhibits a safety factor of 1.02, and the current condition registers 1.04, both characteristic of marginally unstable states. The potential instability zone is characterized by a safety factor of 1.02 with 38 kN residual sliding force under equivalent precipitation intensity, confirming its marginally unstable status through dual parameter verification. All the calculation results indicate that heavy rain significantly increases the sliding force of the landslide, thereby reducing the safety factor of the landslide. To ensure the safety of residents threatened by the unstable slope and the unimpeded passage of Provincial Highway S105, while avoiding potential socio-economic issues from relocation and rerouting, comprehensive measures such as front retaining of potential slump zones, clearing of slump areas, and surface water drainage can be implemented to effectively manage the unstable slope.

4.3. Model Establishment and Parameter Selection

The results obtained from the DAN-W software are closely related to the chosen motion models. The frictional model is a model based on a constant friction coefficient, which is suitable for dry and coarse debris flow or low-speed and thin-layer flow. However, the Voellmy model combines friction and turbulent resistance and may be more suitable for debris flows or avalanches with high water content and fast velocity since turbulent resistance is considered, especially when turbulent resistance dominates the flow process. Based on the topographic features of the gully and bedding slope kinematics observed in both the potential instability zone and the failure zone, the frictional model and Voellmy model were applied to simulate the landslide’s dynamic behavior.
Previous research indicates that the frictional model is better suited for landslide masses with large residual particle sizes and is commonly used in source areas. In contrast, the Voellmy model is suitable for simulating debris flows along paths with significant liquefaction layers and is widely applied in debris flow and deposition zones. Therefore, the frictional model was used for the source region, and the Voellmy model was applied for the propagation region, leading to two possible model combinations: Frictional–Voellmy–Voellmy and Frictional–Voellmy–Frictional. Through trial-and-error analysis of these two combinations, it was found that the F-V-F model provided results that more closely matched the motion process of the bedding slope. The inverse part of the F-V-V model, such as the sliding distance of the failure area, is too far from the actual moving distance. Therefore, the F-V-F rheological model was selected for the simulation.
The frictional model employs the internal friction angle as its primary governing factor, with φ values being modulated by variations in pore water pressure within the geotechnical matrix. Conversely, the Voellmy rheological framework incorporates a dual-parameter system where the Coulomb-type basal friction coefficient and velocity-dependent turbulent resistance parameter synergistically dictate flow dynamics. A comprehensive tabulation of these calibrated parameters, including their hydrological dependencies and terrain-specific adjustments, is provided in Table 3.

4.4. Numerical Simulation Results

The DAN-W software was used to simulate the collapse process of the bedding slope in Xing’an Village. The landslide’s velocity, thickness, and morphology changes over time were derived using the original terrain as the basis for simulation and prediction. The predicted data are all from the running results of DAN-W software, which can accurately reflect the actual process of complete collapse of the landslide. Figure 7 shows the final morphology of the landslide at the end of the collapse process, clearly demonstrating that the landslide poses a significant threat to nearby structures.

4.4.1. Motion Characteristics of the Landslide

Four monitoring points (M1~M4) were arranged at both the front and trailing of the landslide, respectively, to obtain the movement velocities and run-out distances at different positions, as shown in Figure 3. Of these, M1 is situated at the forefront of the slope within the original terrain, while M2 is positioned at the very rear of the slope in the same topography. Through the DAN-W simulation software running results, it can be observed that the total time from initiation to complete failure of the landslide was about 12 s (Figure 8a). Based on the velocity variations, the motion process can be divided into an acceleration phase and a deposition halt phase. During the acceleration phase, the velocity at M1 rapidly increased between 0 and 2 s, reaching a maximum of 3.8 m/s at 2 s. As the landslide continued sliding downslope and approached the lower building area, its motion encountered resistance, leading to a decrease in velocity, transitioning the landslide into a deposition state. By 9 s, the velocity had reduced to 0 m/s, and the motion stopped at a horizontal distance of 102 m (Figure 8b). In contrast, due to the steeper gradient and greater gravitational potential energy at M2, the velocity increased sharply within the 0 to 2 s in the acceleration phase. Afterward, the velocity gradually decreased, then increased again at 4.8 s, reaching a maximum of 4.32 m/s at 7.6 s. The velocity of M2 exhibited a characteristic pattern of initially increasing, then decreasing, followed by another increase and subsequent reduction, showing oscillatory and irregular variations. Based on field observations, it is hypothesized that the movement of the front generated a tension crack zone, forming a free face that accelerated the landslide’s motion. The velocity decreased once this crack was filled by the sliding mass from the trailing edge. Subsequently, the landslide moved, entering the deposition halt phase. The velocity gradually decreased to 0 m/s, with a maximum horizontal displacement of 49.5 m at 12 s.
In particular, monitoring point three (M3) and monitoring point four (M4) were set at the place where the landslide first touched the houses and at the rear edge of the tensile crack to analyze the relationship between the velocity and thickness of the landslide (Figure 9). The velocity at M3 decreased during the first 2 s due to the formation of the tensile crack zone (Figure 9a). Subsequently, the rear landslide mass filled the tensile crack zone, causing the velocity at this point to increase over time and reach a maximum of 2.04 m/s at 5.18 s. By 7 s, as the landslide began to deposit in front of the buildings, the velocity at M3 gradually decreased, eventually reaching 0 m/s by 12 s. However, the landslide reached M4 at 1.27 s with a velocity of 3 m/s (Figure 9b). A significant velocity change was observed during the formation of the tensile crack zone, with a maximum velocity of 3.18 m/s at 2.08 s. Subsequently, the velocity at M4 showed a general decreasing trend until the landslide motion ceased.
In terms of thickness, from 0 to 12 s, the landslide passing through M3 was almost identical to the original ground thickness, with a maximum thickness of 5.2 m, which remained relatively stable.
The deposition thickness at M4 exhibited a continuous increase throughout the motion process, reaching a maximum thickness of 5.3 m when the landslide stopped. Therefore, it can be concluded that the landslide will eventually reach M4 and impact structures such as residential houses.

4.4.2. Morphological Characteristics of Landslide Deposits

The evolution of the deposition pattern of the landslide is presented in Figure 10, capturing its progression every 2 s from initiation to complete failure. During the initial 2 s following the onset of movement, the landslide front advanced toward the base of the house, achieving a maximum horizontal displacement of approximately 10 m. At this stage, the house had not yet impeded the advancing landslide mass. After 2 s, as the landslide continued to move, the material began to accumulate in front of the building, with the horizontal displacement remaining constant. At the same time, the thickness of the accumulation of the body gradually increased until the landslide stopped, and the accumulation thickness reached approximately 4.2 m.

4.4.3. Thickness Variation in the Landslide Deposits

The variation in the thickness of the landslide deposits at 2 s intervals during the landslide’s motion from initiation to stop is shown in Figure 11. In the 0–2 s, the average thickness of the landslide deposits ranged from 4.5 to 5.5 m. As the landslide progressed, the ground thickness at the rear of the landslide rapidly decreased, while the thickness in the middle remained nearly unchanged. At the same time, the thickness of the deposits at the front gradually increased due to the accumulation of sliding materials. Between 0 and 12 s, approximately 20 m of material (horizontal distance between 25 and 45 m) from the rear of the landslide was converted into sliding mass, moving downward. At the horizontal distance of 87 m, the deposit thickness reached its maximum value of 8.3 m. It is also observed that the deposit at the front reached a thickness of 3.8 m within 0–4 s, and after 4 s, the thickness increase slowed down. By 12 s, the maximum thickness of the deposits had reached 5.3 m.

4.5. Failure Mechanism of the Landslide

The Xing’an Village landslide was triggered by the combined effects of internal geological structure, rainfall, and external factors of cutting slope at the toe, with its failure mechanism illustrated in Figure 12. Firstly, the landslide consists of a surface layer of gravelly silty clay and underlying Triassic Baodong Formation mudstone, forming a typical bedding rock landslide. The surface layer of silty clay is loose, while the mudstone has low shear strength and permeability. This unique geological structure creates the preconditions for the occurrence of the landslide. The original slope gradient of the landslide area was between 15° and 18° (Figure 12a). However, excavation at the slope toe for construction, with an angle of about 80°, created an overhanging condition (Figure 12b). This disturbance altered the slope’s stress distribution, concentrating tensile stresses at the toe and enhancing shear stresses at the rear edge, leading to the formation of numerous small cracks in the mudstone layer, which weakened the slope’s stability.
During the heavy rainfall from 26 to 28 July 2020, rainwater infiltrated the loosely compacted gravelly clay layer and seeped into the underlying mudstone. Due to the low permeability of the mudstone, it acted as a barrier, causing rainwater to accumulate and saturate the layer. The high montmorillonite content in the mudstone caused a rapid decline in its mechanical properties, leading to softening and the formation of a weakened layer. Consequently, the front edge of the landslide began sliding, creating a wide tensile crack at the front, while numerous tensile fractures appeared at the rear (Figure 12c), forming a potential instability zone at the rear. As analyzed in Section 3.1, the landslide was still in a slow deformation phase. When another heavy rainfall occurs, rainwater will quickly infiltrate the mudstone through the tensile fractures at the rear, leading to further saturation and the formation of a continuous saturated mudstone layer. This weakened the stability of the entire landslide, triggering its overall failure (Figure 12d).

5. Conclusions

This study examines a bedding rock landslide in Xing’an Village, Chongqing, combining field surveys, laboratory tests, and numerical simulations to analyze deformation, failure characteristics, and stability under heavy rainfall. The complete landslide failure process was numerically simulated using DAN-W dynamic analysis software with the Frictional–Voellmy–Frictional model, encompassing both sliding behavior characterization in the failure zone and evolutionary trend prediction of the potential instability zone. Based on the above work, the failure mechanism of the landslide was analyzed. These findings collectively provide theoretical and methodological support for mitigating bedding rock landslides with comparable geological settings. The main conclusions are as follows:
(1)
Based on the deformation characteristics of the landslide, it can be divided into a frontal failure zone and a rear potential instability zone. The frontal failure zone is still undergoing downward deformation, with buildings at the front slowing the deformation rate. It was calculated that the safety factor of the frontal failure zone under heavy rainfall conditions is 1.02, indicating a state of marginally unstable. The potential instability zone at the rear of the landslide has a safety factor of 1.02 under heavy rainfall conditions, indicating a marginally unstable state.
(2)
The established integrated sliding model for both the frontal failure zone and the potential instability zone could effectively capture the landslide’s motion characteristics and deposition morphology. The frontal failure zone and the potential instability zone will move as a whole, representing a significant hazard to residential structures. The duration of the unstable landslide motion is 12 s, with a maximum velocity of 4.32 m/s, a maximum deposition thickness of 8.3 m, and a maximum movement distance of 20 m.
(3)
The Xing’an Village landslide was triggered by the combined effects of internal geological structure, rainfall, and external factors of cutting slope. The surface layer consists of loose, silty clay. At the same time, the underlying mudstone has low permeability, causing rainwater to accumulate in the mudstone layer and leading to its saturation, forming a weak layer. This and the excavation at the slope toe ultimately triggered the landslide.

Author Contributions

All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication. All authors have read and agreed to the published version of the manuscript.

Funding

The work was supported by a follow-up of the Geological Disaster Prevention and Control Project in the Three Gorges area (Grant No. 000121 2024C C60 001 and Grant No. 000121 2023C C60 001), Qianlong Plan Top Talent Project of Wuhan Center of China Geological Survey (Grant No. QL2022-06).

Data Availability Statement

The data presented in this study are available in the insert article.

Acknowledgments

The authors would like to thank China Institute of Geo-Environment Monitoring and Chongqing Bureau of Geology and Mineral Resources Exploration and Development Nanjiang Hydrogerlogy Engineering Geology Team, for the great assistance in field investigation, providing data, and providing software copyright.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The geographical location of Xing’an village landslide.
Figure 1. The geographical location of Xing’an village landslide.
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Figure 2. (a) Geological plan of the Xing’an landslide, (b) Drilling core samples, and (c) Rear tension cracks and explored grayish-white mudstone.
Figure 2. (a) Geological plan of the Xing’an landslide, (b) Drilling core samples, and (c) Rear tension cracks and explored grayish-white mudstone.
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Figure 3. Schematic diagram of 2–2′ profile of Xing’an landslide.
Figure 3. Schematic diagram of 2–2′ profile of Xing’an landslide.
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Figure 4. Cataclastic rock mass in the landslide.
Figure 4. Cataclastic rock mass in the landslide.
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Figure 5. Groundwater seepage was observed during excavation at the toe of the landslide.
Figure 5. Groundwater seepage was observed during excavation at the toe of the landslide.
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Figure 6. Tension cracks at the rear edge of the potential instability zone.
Figure 6. Tension cracks at the rear edge of the potential instability zone.
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Figure 7. Schematic diagram of the final morphology of the bedding slope.
Figure 7. Schematic diagram of the final morphology of the bedding slope.
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Figure 8. (a) Temporal variation in velocities at M1, and (b) Displacement-based variation in velocities at M2.
Figure 8. (a) Temporal variation in velocities at M1, and (b) Displacement-based variation in velocities at M2.
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Figure 9. (a) Variation in landslide velocity and thickness over time at M3, and (b) variation in landslide velocity and thickness over time at M4.
Figure 9. (a) Variation in landslide velocity and thickness over time at M3, and (b) variation in landslide velocity and thickness over time at M4.
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Figure 10. Morphological characteristics of landslide deposits.
Figure 10. Morphological characteristics of landslide deposits.
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Figure 11. Thickness variation in the unstable landslide.
Figure 11. Thickness variation in the unstable landslide.
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Figure 12. (a) Pre-deformation stage, (b) Slope-toe excavation, (c) Rainfall and slope-toe excavation induce lower collapse, forming tensile cracks at the rear, and (d) The lower collapse triggers overall failure in the rear potential instability zone.
Figure 12. (a) Pre-deformation stage, (b) Slope-toe excavation, (c) Rainfall and slope-toe excavation induce lower collapse, forming tensile cracks at the rear, and (d) The lower collapse triggers overall failure in the rear potential instability zone.
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Table 1. Physical and mechanical properties of rock samples from different zones.
Table 1. Physical and mechanical properties of rock samples from different zones.
ZoneStateCohesion
(C) (kPa)		Internal Friction Angle
(Φ) (°)
Failure Area
(Mudstone)		Natural 825
Saturated523
Failure Area
(Clay with Gravel)		Natural 2212
Saturated1810
Potential Instability Zone (Mudstone)Natural 20.512.5
Saturated18.511.5
Potential Instability Zone (Clay with Gravel)Natural 2213
Saturated2012
Table 2. The calculated factor of safety for the landslide.
Table 2. The calculated factor of safety for the landslide.
Calculation ProfileConditionFactor of Safety
(Fs)		Residual Sliding Force (kN/m)Stability State
Profile of 2–2′ Collapsed Zone
(Back Calculation)		Natural Condition1.1326.58Stable
Rainstorm Condition1.02122.01Marginally Unstable
Profile of 2–2′ Collapsed Zone
(Current Condition)		Natural Condition1.430.00Stable
Rainstorm Condition1.04288.89Marginally Unstable
Profile of 2–2′ Potential Collapse ZoneNatural Condition1.500.00Stable
Rainstorm Condition1.0238.00Marginally Unstable
Table 3. Parameters of the F-V-F model.
Table 3. Parameters of the F-V-F model.
ModelFriction
Coefficient		Friction
Angle
(°)		Turbulence Coefficient
(m/s)		Unit
Weight
(kN/m3)		Scraping
Depth
(m)
F-13-25-
V0.4-300253
F-13-25-
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Zeng, J.; Dai, Z.; Luo, X.; Jiao, W.; Yang, Z.; Li, Z.; Zhang, N.; Xiong, Q. Deformation and Failure Mechanism of Bedding Rock Landslides Based on Stability Analysis and Kinematics Characteristics: A Case Study of the Xing’an Village Landslide, Chongqing. Water 2025, 17, 767. https://doi.org/10.3390/w17050767

AMA Style

Zeng J, Dai Z, Luo X, Jiao W, Yang Z, Li Z, Zhang N, Xiong Q. Deformation and Failure Mechanism of Bedding Rock Landslides Based on Stability Analysis and Kinematics Characteristics: A Case Study of the Xing’an Village Landslide, Chongqing. Water. 2025; 17(5):767. https://doi.org/10.3390/w17050767

Chicago/Turabian Style

Zeng, Jingyi, Zhenwei Dai, Xuedong Luo, Weizhi Jiao, Zhe Yang, Zixuan Li, Nan Zhang, and Qihui Xiong. 2025. "Deformation and Failure Mechanism of Bedding Rock Landslides Based on Stability Analysis and Kinematics Characteristics: A Case Study of the Xing’an Village Landslide, Chongqing" Water 17, no. 5: 767. https://doi.org/10.3390/w17050767

APA Style

Zeng, J., Dai, Z., Luo, X., Jiao, W., Yang, Z., Li, Z., Zhang, N., & Xiong, Q. (2025). Deformation and Failure Mechanism of Bedding Rock Landslides Based on Stability Analysis and Kinematics Characteristics: A Case Study of the Xing’an Village Landslide, Chongqing. Water, 17(5), 767. https://doi.org/10.3390/w17050767

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