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Article

Application of Capillary Barrier Systems for Slope Stabilization Under Extreme Rainfall: A Case Study of National Highway 10, India

by
Yusen Cheng
1 and
Yangyang Li
1,2,*
1
Suzhou Industrial Park Monash Research Institute of Science and Technology, Monash University, Suzhou 215000, China
2
Department of Civil Engineering, Monash University, 23 College Walk, Clayton, Melbourne, VIC 3800, Australia
*
Author to whom correspondence should be addressed.
Infrastructures 2024, 9(11), 201; https://doi.org/10.3390/infrastructures9110201
Submission received: 24 September 2024 / Revised: 30 October 2024 / Accepted: 8 November 2024 / Published: 10 November 2024
(This article belongs to the Special Issue Sustainable and Resilient Infrastructure: Climate Adaptation through Green Engineering and Low-Carbon Technologies)
Browse Figures
Figure 1
<p>Overview of the study area. (<b>a</b>) The full extent of India’s NH10. (<b>b</b>) Study section of NH10 with a 3 km buffer zone.</p> "> Figure 2
<p>Maps used in GEOtop modeling: (<b>a</b>) DEM; (<b>b</b>) slope angle; (<b>c</b>) aspect.</p> "> Figure 3
<p>Curve fitting for residual soil: conversion from the Gardner equation to the Van Genuchten equation.</p> "> Figure 4
<p>Curve fitting for CBS: conversion from the Fredlund and Xing equation to the Van Genuchten equation. (<b>a</b>). Fine−grained soil. (<b>b</b>). Coarse−grained soil.</p> "> Figure 5
<p>Schematic diagrams of the original slope and CBS slope. The red dots indicate the center of each layer where the PWP values are calculated. (<b>a</b>). Original slope with eight residual soil layers. (<b>b</b>). CBS slope with fine− and coarse−grained soil layers in the top three layers.</p> "> Figure 6
<p>The workflow of the GEOtop model.</p> "> Figure 7
<p>Variations in the average PWP for the original and CBS slopes at various depths.</p> "> Figure 8
<p>Variations in the percentage of pixels for each risk category between the original slope and CBS slope at various depths. (<b>a</b>). Very high risk. (<b>b</b>). High risk. (<b>c</b>). Moderate risk. (<b>d</b>). Low risk.</p> "> Figure 9
<p>Risk distribution at a depth of 0.8 m for original slope and CBS slope at different rainfall stages.</p> "> Figure 10
<p>The location of each landslide point.</p> "> Figure 11
<p>Variations in the FoS for each landslide point. (<b>a</b>). North point. (<b>b</b>). Central point. (<b>c</b>). South point.</p> "> Figure A1
<p>Risk distribution at a depth of 1.25 m for the original slope and CBS slope at different rainfall stages.</p> "> Figure A2
<p>Risk distribution at a depth of 1.75 m for the original slope and CBS slope at different rainfall stages.</p> ">
Versions Notes

Abstract

:
Global warming has led to an increase in extreme rainfall events, which often result in landslides, posing significant threats to infrastructure and human life. This study evaluated the effectiveness of the Capillary Barrier System (CBS) in enhancing slope stability along a vulnerable section of India’s National Highway 10 (NH10) during maximum daily rainfall. The GEOtop model was employed to conduct water balance simulations and obtain the pore–water pressure (PWP), which was then used to calculate the Factor of Safety (FoS). Results showed that CBS effectively delayed the rise in PWP, leading to lower peak values and smaller areas of very high and high risk levels. Spatial distribution mapping further confirmed that CBS minimized very high risk zones. At three historical landslide points, CBS slopes generally maintained FoS values above 1, demonstrating enhanced stability and improved resilience to extreme rainfall. These findings highlight the potential of CBS as a viable strategy for slope reinforcement in regions susceptible to heavy rainfall.

1. Introduction

In recent times, global warming has significantly altered weather patterns, leading to an increase in both the frequency and intensity of extreme precipitation events worldwide [1,2]. Such extreme rainfall events have been identified as a significant trigger for slope failures and landslides in many countries, including Australia [3], China [4,5], India [6,7], Italy [8,9], and Singapore [10,11], especially in mountainous and hilly regions. When rainfall infiltrates the soil, it decreases the matric suction, which refers to the difference between pore–air pressure and pore–water pressure (PWP) in unsaturated soils and contributes to the soil’s shear strength. The decrease in matric suction leads to a reduction in shear strength and an increase in PWP, making the slope more susceptible to failure. The impact of such events is expected to become more severe due to ongoing climate change [12]. Consequently, there is an increasing need for effective slope stabilization measures, particularly in areas of critical importance, such as around highways and residential zones, to prevent catastrophic failures and protect human life and infrastructure.
Traditional slope stabilization techniques, such as retaining walls, soil nailing, and drainage systems, have been employed to reinforce slopes. While effective in some scenarios, these methods can be expensive, environmentally disruptive, and may not always provide satisfactory protection under extreme rainfall conditions [13]. The Capillary Barrier System (CBS) is a geotechnical engineering technique designed to reduce water infiltration and enhance slope stability [14,15]. It consists of a fine−grained soil layer placed over a coarse−grained soil layer. Under unsaturated conditions, the coarse−grained layer exhibits lower permeability than the fine−grained layer, creating a barrier effect that restricts water flow only in fine−grained soil. During rainfall, the infiltrated water can be retained within the fine−grained soil or, depending on the slope of the terrain, flow laterally toward the toe of the slope [16]. The accumulated water can be removed through processes such as evaporation, transpiration, and lateral drainage [17]. This mechanism prevents excessive rainwater from infiltrating into the residual soil of the slope, thereby enhancing slope stability and reducing the risk of landslides. However, once the fine−grained soil becomes saturated, the capillary barrier effect disappears, leading to a breakthrough where water infiltrates the coarse−grained and residual soil layers [15].
Many studies have demonstrated the effectiveness of CBS in reducing water infiltration during rainfall events, thereby contributing to slope stabilization [18,19,20]. Scarfone et al. [21] modeled the behavior of a 6−meter−high slope, demonstrating that CBS effectively maintains higher soil suction and lower saturation levels, thus preventing rainfall−induced slope instability. Gao et al. [22] employed the Green−Ampt model and the Janbu method to analyze the infiltration process and stability of inclined capillary barrier covers under rainfall conditions, validating via a case study using an experimental slope model measuring 270 cm in length and 60 cm in width. In addition, some researchers have compared CBS with other slope stabilization methods to further demonstrate its effectiveness. For instance, Rahardjo et al. [23] conducted a comparative study between CBS and vegetative slopes, showing that CBS maintained lower PWP within the slope during both low− and high−intensity rainfall events, thereby maintaining higher soil suction and stability than vegetated slopes. Similarly, Li et al. [24] compared CBS with vegetative covers using Vetiver grass, indicating that the Factor of Safety (FoS) for CBS slopes was significantly higher than that of slopes reinforced with vegetation.
These studies have provided valuable insights into the mechanisms and effectiveness of CBS, but they predominantly focus on small−scale applications at specific locations, as well as laboratory experiments and numerical modeling based on idealized slope models. However, research on CBS’s application at a regional scale remains relatively scarce, despite its importance for ensuring the stability and safety of large infrastructure projects, such as roadside slopes along critical highways. Few studies have evaluated the impact of CBS on slope stability across larger and more complex terrains. Furthermore, there is a limited focus on the spatial characteristics of slope stability variations with CBS implementation. Therefore, this study aims to evaluate CBS’s performance in a regional context under extreme rainfall. By utilizing physical modeling and simulating maximum daily rainfall events, this research analyzes the variations in PWP and FoS, as well as further investigating the spatial distribution of slope stability with and without CBS application along a critical section of the selected highway. The outcomes are expected to provide valuable insights into the broader application of CBS for regional slope stabilization and landslide risk mitigation.

2. Study Area

India’s National Highway 10 (NH10) is a crucial transportation route, extending approximately 174 km and connecting cities such as Siliguri in West Bengal and Gangtok in Sikkim. NH10 plays a significant role in the regional economy and transportation network. This study focuses on a specific section of NH10, extending from the Kalimpong Dansong Forest in the north to the Sitong Forest in the south, with a distance of around 29.5 km alongside the Tista River. A 3 km buffer zone around this segment has been established as the study area (Figure 1). This 3 km buffer zone was determined based on a global rainfall−induced landslide catalog [25,26]. This selected buffer can effectively include most of the documented landslide points along the NH 10 segment (Figure 1), allowing for the slope stability assessment by considering the highway’s surrounding slopes that have historically been susceptible to landslides.
The study area mainly encompasses a complex mountainous region. The elevation in this area is highly varied, ranging from around 100 m to 1410 m above sea level, increasing from south to north. The geomorphology is characterized by rugged mountains, steep scarp faces, and river valleys that deepen into steep gorges towards the trunk rivers [27]. The geological feature of the study area is defined by the Daling Group, including the Reyang and Gorubathan Formations, which are mainly composed of metamorphic rocks like phyllites, quartzites, and schists [28]. According to the Köppen climate classification [29], the study area falls under the subtropical highland climate (Cwb). The annual precipitation ranges from 2000 to 4000 mm, mostly during the monsoon season between June and September [30]. The intense and prolonged rainfall during the monsoon periods frequently triggers landslides, highlighting the necessity for effective slope stabilization measures to maintain the stability and reliability of this vital transportation corridor.

3. Methods

3.1. Rainfall Patterns and Topographical Data

Extreme rainfall refers to rainfall events that exceed the typical levels for a particular region over a specific period. Many studies have used daily precipitation that exceeds a certain statistical threshold to represent extreme daily rainfall [31,32]. Due to the challenges of precisely determining this threshold, this study used the maximum daily rainfall of 304 mm/day, equivalent to 12.67 mm/h, recorded in 1968, as a representative measure to simulate extreme rainfall events [33]. According to the India Climate & Energy Dashboard [34], the Darjeeling region, where the study area is located, typically exhibits a shallow water level between 2 and 5 m below ground level (mbgl) during both the pre−monsoon and post−monsoon seasons. In this study, we selected the median value of 3.5 m and assumed a spatially homogeneous groundwater table to represent the initial conditions. Topographical data for the study area were obtained from the ASTER Global Digital Elevation Model (GDEM) V003 dataset [35]. A Digital Elevation Model (DEM) map with a resolution of 30 m was created within the study area, along with corresponding slope and aspect layers (Figure 2).

3.2. Soil Properties and CBS Settings

The soil properties for this study were collected through a comprehensive literature review, focusing on previous research conducted within or near the study area [36,37]. The soil water characteristic curve (SWCC) used in the referenced paper is characterized by the Gardner equation [38] (Formula (1)). However, since the GEOtop model used in this study for water balance simulations employs the Van Genuchten equation [39] (Formula (2)), a curve−fitting procedure was performed to convert the soil properties from the Gardner format to the Van Genuchten format (Figure 3). This conversion was conducted using a non−linear least square fitting method via the curve_fit function from the SciPy library in Python. The procedure optimized the parameters αVG and nVG by minimizing the difference between the VWC values calculated by the Gardner and Van Genuchten equations. The data used for the fitting corresponded to matric suction values ranging from 10−2 kPa to 103 kPa. While there may be differences between the two equations, the close alignment of the two curves ensures the overall behavior of soil properties is well represented. The fitting performance was evaluated using R2 and RMSE, with values of 0.9949 and 0.0099, respectively, indicating a strong fit between the curves. The residual soil properties, assumed to be homogeneous, used in this study are presented in Table 1.
θ = θ r + θ s − θ r e α G ψ
where θ represents the volumetric water content (VWC) under a specific soil suction ψ , θ r is the residual volumetric water content, θ s is the saturated volumetric water content, and α G is a shape parameter.
θ = θ r + θ s − θ r 1 + α V G ψ n V G m V G
where α V G is related to the inverse of the air entry suction, n V G is related to the pore size distribution of the roil, and m V G = 1 − 1 / n V G .
The shear strength of unsaturated soil can be calculated by Formula (3) [40].
τ = c ′ + σ − u a tan ⁡ ϕ ′ + u a − u w tan ⁡ ϕ b
where τ is the shear strength, c ′ is the effective cohesion, ϕ ′ is the effective friction angle, ϕ b is the angle of shearing resistance with respect to matric suction, σ − u a represents the net normal stress, and u a − u w represents the matric suction.
In addition, the selection of CBS parameters is critical in the numerical analysis to simulate the reinforced slope and assess its effectiveness. In this study, a comprehensive literature review was conducted, and CBS parameters were selected based on the research by Rahardjo et al. [16]. The selected CBS consisted of 60 cm thick soil, with 40 cm of fine−grained soil and 20 cm of coarse−grained soil. According to unsaturated soil mechanics principles, this CBS configuration can theoretically create an effective capillary barrier effect due to the difference between the hydraulic properties of fine− and coarse−grained soils. The fine−grained layer possesses higher unsaturated permeability than the coarse−grained layer within the relevant matric suction range. This difference in permeability facilitates lateral rainwater flow within the fine−grained layer at the top, limiting water infiltration into the underlying coarse−grained layer and residual soil, thereby enhancing slope stability. This CBS design aligned well with the study’s aim to mitigate water infiltration during rainfall, which is critical for maintaining slope stability along the vulnerable section of NH10. Notably, the SWCC parameters of CBS in the reference study were described using the Fredlund and Xing equation (Formula (4)) [41].
θ = C ψ ⋅ θ s ln ⁡ e + ψ a F X n F X m F X
where C ψ is a correction factor typically set to 1 [42] and a F X , n F X , and m F X are fitting parameters related to the air−entry value of soil.
To ensure consistency with the GEOtop model, a curve−fitting procedure similar to that for residual soil was also applied to convert these parameters to the Van Genuchten format (Figure 4). For fine−grained soil, the R2 was 0.9994 and the RMSE was 0.0045. For coarse−grained soil, the R2 was 0.9951 and the RMSE was 0.0085. The CBS properties used in this study are also summarized in Table 1.

3.3. GEOtop Model

GEOtop is a physically based distributed hydrological model used for water balance simulation that incorporates topographical, meteorological, and soil data [43]. It employs a three−dimensional (3D) grid system to represent the spatial variability within the study area, enabling the simulation of water movement through multiple heterogeneous soil layers.
The core of GEOtop’s water balance simulation is based on the 3D Richards’ equation (Formula (5)) [44], which models unsaturated water flow in the soil.
∂ θ ( x ,   t ) ∂ t = ∇ · ( K ∇ ( z + ψ ) ) + S ( x ,   t )
where x represents the position, t represents the time, θ is the volumetric water content, K is the hydraulic conductivity, z is the gravitational head, ψ is the soil water pressure head, ∇ · ( K ∇ ( z + ψ ) ) represents the flux divergence of water per unit volume, and S represents the water exchanges between atmosphere and soil, including evaporation and transpiration processes.
In this study, the GEOtop model was configured with eight soil layers. A field survey conducted by Gupta and Chattaraj [27] in the Kalimpong Dansong Forest, situated in the southern part of the study area along NH10, identified very hard rock at a depth of approximately 5 to 8 m below the surface. Based on this observation, layer 8 (located at a depth of 5 m and below) was set as an impermeable rock layer to represent a boundary that prevents further vertical water movement (Figure 5). This configuration also simplified the simulations by assuming that deeper layers do not significantly contribute to water flow, thereby allowing for a focus on surface and subsurface water movement, where shallow landslides typically occur. The CBS was used to replace the top three layers (layer 1 to layer 3) of the original slope. The simulation covered a 48 h time series with a 1 h time step. It started with a 24 h maximum daily rainfall event with an intensity of 12.67 mm/h, followed by another 24 h dry period to observe water drainage and recovery processes within the soil layers. Using the modified CBS parameters, the water balance simulation was repeated under the same rainfall patterns. Pore–water pressure maps were generated on an hourly basis under two scenarios (i.e., with and without CBS) at the center of each soil layer throughout the simulation (Figure 6). Details regarding the configuration of soil and precipitation parameters can be found in Appendix A.

3.4. Factor of Safety (FoS) Calculation

To quantitatively assess the slope stability, the Factor of Safety (FoS) was calculated. Given that extreme rainfall events may lead to ponding at the slope surface, the FoS calculation distinguished between ponding and non−ponding conditions. The ponding was determined by comparing the PWP to the calculated soil depth; if the PWP exceeds the soil layer’s thickness, the condition was identified as ponding; otherwise, it was identified as the non−ponding condition.
For non−ponding conditions, the FoS was calculated using Formula (6) [45,46].
F o S = tan ⁡ ϕ ′ tan ⁡ δ + c ′ − ψ Z ,   t γ w tan ⁡ ϕ ′ γ Z sin ⁡ δ cos ⁡ δ
where ϕ ′ is the effective friction angle, c ′ is the effective cohesion, ψ Z ,   t is the PWP at depth Z and time t , γ and γ w are the unit weights of soil and water, respectively, and δ is the slope angle.
For ponding conditions, Formula (7) [47] was used.
F o S = c ′ + Z γ sat − γ w cos 2 ⁡ β tan ⁡ ϕ ′ γ sat Z sin ⁡ β cos ⁡ β
where γ sat is the saturated unit weight of soil.

4. Results

4.1. Simulation Results of Pore–Water Pressure (PWP)

Over the elapsed time, differences in PWP between the original slope and the CBS slope were observed. The depths of 0.8 m, 1.25 m, and 1.75 m represented the central points of soil layers 4 to 6 (Figure 5), as well as the depths in which the GEOtop model outputs simulated PWP results. Layers 1 to 3 corresponded to the CBS design, while layers 7 and 8 were below the groundwater table and assumed to be saturated. Therefore, the variations in PWP were analyzed specifically at the depths of 0.8 m, 1.25 m, and 1.75 m (Figure 7) to capture the impact of CBS.
As rainfall began, the PWP in the original slope rapidly increased and reached close to 0 kPa at depths of 0.8 m and 1.25 m, indicating the approach of the saturated condition. At 1.75 m, while the increase was also obvious, it did not reach saturation within the simulation period due to the gradual infiltration of rainfall through the upper layers before impacting the deeper soil. In contrast, the CBS slope showed a delayed response across all depths, with a few hours’ lags before the PWP rises, especially noticeable in the deeper layers, including 1.25 m and 1.75 m. This difference indicated the CBS’s effectiveness in impeding water infiltration and mitigating the increasing rate of PWP.
After the end of the rainfall, the PWP at the depths of 1.25 m and 1.75 m continued to increase. This increase can be attributed to the continued water infiltration from the upper layers, as the extreme rainfall event typically has a prolonged effect on the deeper soil layers. In the CBS slope, a similar upward trend was found in these two layers, and a noticeable phenomenon was that the post−rainfall increase in PWP was more significant than that of the original slope at 1.25 m. This was due to the delaying effect of CBS on water infiltration, making the water reach this layer mainly after the end of rainfall. However, although the increase was more apparent after the end of rainfall, the overall PWP increase in the CBS slope throughout the simulation period remained smaller than that of the original slope across all depths. In addition, for the shallower layer (0.8 m), the PWP in both types of slopes showed a decreased tendency during the post−rainfall period due to the continuous water infiltration, which aided in gradually returning the shallow soil to the unsaturated condition.

4.2. Simulation Results of Slope Stability

In this study, the slope stability was categorized into four distinct categories based on the FoS values (Table 2), following the guidelines provided by Rahardjo et al. [48]. In engineering practice, a FoS of 1.5 or higher is generally required to ensure safety and is considered to represent a low−risk condition. Conversely, a FoS value smaller than 1 means that the driving force exceeds the resisting force, indicating instability and a very high risk. In addition, the values between 1 and 1.5 typically define high risk and moderate risk levels, allowing for a more detailed assessment of slope stability. These risk categories are applicable to a wide range of slopes, especially in regions where rainfall significantly threatens slope stability like the study area. Figure 8 illustrates the percentage of pixels in each category varies with time and depth for both the original slope and CBS slope (A more detailed table can be found in Appendix B).
In the very high risk category (Figure 8a), the percentage of pixels in the original slope increased rapidly, particularly at depths of 0.8 m and 1.25 m. At the 0.8 m depth, the original slope showed a steep rise at around the 18 h mark after the rainfall started, peaking at approximately 24 h with a percentage over 12%. This rapid increase indicated the quick response of shallow soil layers to rainfall infiltration, leading to evident instability conditions. At the 1.25 m depth, however, the rise in very high risk pixels occurred later, mainly after the maximum daily rainfall event had ended (around the 24 h mark). This delayed increase, which eventually reached nearly 14%, was driven by the continued water infiltration from the upper layers, demonstrating the prolonged effects of rainfall on the deeper soil layers even after the rainfall had stopped. For the deeper 1.75 m layer, an increase in very high risk pixels did not become apparent until 12 h after the end of the rainfall (around the 36 h mark), indicating a more delayed response. By contrast, the CBS slope showed a noticeably later increase and a lower maximum percentage of very high risk pixels across all depths. Especially at the 1.25 m and 1.75 m depths, the CBS slope did not exhibit a significant rise.
For the high−risk category, the trend in the original slope was similar to the very high risk category in terms of growth pattern. The CBS also demonstrated its effectiveness in delaying the increase in high−risk pixels and maintaining lower peak values, but the maximum percentage of pixels in this category was higher across all depths compared to the very high risk category (Figure 8b). Additionally, the increase in the high−risk category occurred earlier and reached its maximum much faster. After reaching the peak, a downward trend was observed; this was also found in the moderate−risk category (Figure 8c). This decline was characterized by a sharp decline followed by a more gradual decrease. The sharp decline could be attributed to specific areas that have transited into higher risk categories as the rainfall continues, driven by increased PWP. After the end of rainfall, the variations in risk became more complex. For the shallower soil, especially at 0.8 m depth, the drainage process and continued water infiltration to deeper layers led to a reduction in its PWP, thereby enhancing slope stability in the upper layers. This can be confirmed by Figure 8d, where an increase in the proportion of low−risk pixels was observed about 3 h after the end of rainfall. In contrast, for deeper soils such as the 1.75 m depth, the continued infiltration after the end of rains can still lead to increased risk. However, this increase was less pronounced in the CBS slope than in the original slope, indicating that the CBS effectively slowed the risk increase. In addition, the CBS slope retained a larger proportion of low−risk areas, particularly for the 1.25 m and 1.75 m depths, showing almost no sharp reduction in low−risk areas during the rainfall event.

4.3. Spatial Risk Distribution

To analyze the spatial distribution of each slope stability category, this study mapped the risk distribution at a depth of 0.8 m for the original slope and CBS slope at four different rainfall stages (Figure 9). At 12 h of rainfall, almost the entire study area, whether for the original or the CBS slope, was classified as low risk. However, at 24 h of rainfall, the differences between the two types of slopes became evident. Due to the CBS’s ability to delay the increase in PWP and reduce water infiltration, only a minimal portion of the CBS slope exhibited very high risk. In contrast, the original slope had a widespread distribution of very high risk areas throughout the study region, indicating a transition to unstable conditions under extreme rainfall. As the rainfall and infiltration continued, the CBS slope showed an increase in very high risk areas, gradually expanding in spatial distribution. Despite this growth, the extent of very high risk areas in the CBS slope remained smaller compared to the original slope. The spatial pattern of these high−risk areas became similar between the two types of slopes, but the CBS’s mitigating effect was evident in the reducing regions affected by the rainfall. The CBS slope allowed areas that would otherwise be classified as very high risk and high risk under extreme rainfall conditions to remain in the moderate−risk or even low−risk categories. After 24 h of rainfall end, the spatial distribution of risk showed little visual difference from the pattern observed 12 h earlier. This character aligns with the results in Figure 8, where reductions in high risk and very high risk categories, as well as increases in low−risk areas, were highly gradual during this period, suggesting that the recovery of the slope stability after extreme rainfall was a slow process.
While the risk distribution at 1.25 m showed a similar trend to that of 0.8 m, the distribution at 1.75 m exhibited a more significant delayed risk increase, with more apparent differences between the CBS slope and the original slope emerging at 12 to 24 h after the end of rainfall. The detailed risk distribution maps for the 1.25 m and 1.75 m depths can be found in Appendix C.

5. Discussion

5.1. Effectiveness of CBS in Enhancing Slope Stability

The results of this study, including statistical analysis and spatial risk mapping, have demonstrated the effectiveness of the CBS in reducing the extent and severity of slope instability risk, thereby enhancing slope stability under extreme rainfall. It significantly reduced the proportion of areas categorized as very high and high−risk, with the CBS slope showing lower peak proportions and a more gradual increasing rate. In contrast, the CBS slope maintained a higher proportion of the low−risk area after extreme rainfall across all depths, indicating a more stable slope condition throughout the event. These findings align with the theoretical basis of CBS, which relies on the differing hydraulic properties of fine− and coarse−grained soils to create a barrier effect. By impeding water infiltration, CBS can mitigate rapid increases in PWP, as demonstrated in Section 4.1, thus maintaining higher FoS values and reducing areas categorized as high−risk or very high risk.
To further validate the effectiveness of the CBS, three historical landslide points were selected in the northern, central, and southern parts of the study area (Figure 10 and Table 3) based on the global rainfall−induced landslide catalog [25,26] for detailed analysis. For each point, a 100 m radius buffer zone was established, and the variations in the mean FoS within these buffers were calculated (Figure 11). The three landslide points demonstrated similar patterns in the FoS variations across different depths and rainfall timesteps.
In general, the CBS slope demonstrated an obvious stabilization effect for all three selected historical landslide points, as indicated by consistently higher minimum FoS values compared to the original slope across all depths (Table 3). Specifically, at the depths of 0.8 m and 1.25 m, the original slopes of all three landslide points exhibited a notable decrease in the FoS during the rainfall event, with values dropping below 1, indicating a very high risk. In contrast, the CBS−reinforced slopes demonstrated significantly improved slope stability. At these depths, the FoS for the CBS slopes mostly remained above 1, illustrating that the CBS effectively prevented the slopes from reaching critical risk categories under extreme rainfall conditions. Furthermore, the implementation of the CBS led to smaller variation ranges of FoS values, meaning that the difference between the maximum and the minimum of FoS values was smaller than that of the original slopes. This reduced variability indicated a more stable slope condition and a more controlled and resilient response to extreme rainfall events. At the 1.75 m depth, however, both the original and CBS slopes across the three landslide points showed no significant differences in FoS.
These findings further confirmed the CBS’s effectiveness in enhancing slope stability under extreme rainfall conditions. The improvement in FoS values across different depths demonstrated its ability to mitigate landslide risks, particularly at sites that are susceptible to failure.

5.2. Recommendations for Future Work

Despite the overall findings demonstrating that the selected CBS design significantly enhanced slope stability, the analysis revealed certain aspects that require further investigation. First, at depths of 1.25 m and 1.75 m, a slightly higher initial percentage of very high risk pixels in the CBS slope compared to the original slope at this depth was observed in each risk category (Figure 8). This unusual phenomenon might be attributed to the CBS properties rather than the negligible differences in matric suction. Specifically, the CBS material had a higher unit weight compared to the residual soil, contributing to a lower initial FoS value as the FoS is influenced by the product of unit weight and depth (Formulas (6) and (7)). The deeper layers, as well as the higher unit weight of the CBS material compared to the residual soil, may initially exhibit lower FoS values, even before significant infiltration occurs. This reflects the CBS’s material properties and influences on risk distribution regarding soil depth, highlighting the importance of appropriately selecting CBS material configurations to avoid introducing new, human−induced risks.
In addition, for the three historical landslide points, although CBS showed obvious stabilization effect, the FoS at these landslide points still did not exceed 1.5 (the threshold for low risk) even after CBS implementation. This outcome may also be attributed to the CBS design, where the selected parameters and layering settings may not be optimal.
However, this study did not include a comparative study of different CBS designs. The primary aim was to assess the effectiveness and general impact of CBS on slope stability rather than to optimize CBS designs, which would involve more extensive simulations and require a broader range of CBS parameters to comprehensively evaluate different designs. Future research can expand on these findings to explore more tailored solutions within this area. Researchers have shown that CBS performance can vary significantly depending on the soil properties used in CBS design. For instance, Qian et al. [49] evaluated different material combinations and concluded that the hydraulic conductivity and grain size distribution of the soil layers play a significant role in determining the CBS’s ability to prevent water infiltration. Vishnu and Bharat [50] developed a numerical model to assess the impact of soil hydraulic characteristics on CBS performance, emphasizing the importance of selecting optimal material combinations to maximize the CBS’s effectiveness. Therefore, selecting optimal CBS material and adjusting layers’ combinations could maximize the effectiveness of CBS under different conditions. Future work could focus on optimizing CBS material properties and configuration to suit specific study areas, thereby further enhancing slope stability and mitigating any potential initial risk caused by higher unit weight or other CBS materials’ properties.
Moreover, employing CBS on a regional scale also needs to consider the economic and environmental issues. For example, Rahardjo et al. explored the use of recycled crushed concrete in CBS and found that this material could effectively reduce rainwater infiltration, providing an environmentally sustainable solution [51]. Future research could also explore the application of sustainable materials to make CBS eco−friendly and cost−effective.

6. Conclusions

This study focused on a section of India’s NH10 highway to evaluate the effectiveness of the CBS in enhancing slope stability on a regional scale. By employing the GEOtop model for water balance simulation, PWP was obtained and utilized for FoS calculation. Then, the statistical analysis was conducted, and the spatial distribution of slope stability categories was mapped to assess the stabilization effect of the CBS. Three historical landslide points in the northern, central, and southern parts of the study area were selected for further case analysis of the FoS variations at different depths.
Based on the results, several key findings were revealed. The CBS slope effectively modulated PWP variations during and after extreme rainfall events by slowing the water infiltration process, leading to a gentler increase in PWP and lower peak values, especially in the layers of 1.25 m and 1.75 m, where the CBS prevented rapid soil saturation and mitigated the prolonged effects of rainfall. Additionally, the CBS significantly reduced the area classified as very high risk and high−risk regarding the slope stability category, retaining a greater proportion of the area in the low−risk category. Spatial distribution mapping further supported this finding, as after 24 h of rainfall, the original slope showed widespread very high risk zones, while the CBS slope exhibited only minimal areas in this category, facilitating the transition of more regions to moderate− and low−risk categories. Furthermore, the analysis of the FoS variations at the three selected historical landslide points highlighted that the original slope all experiences a drop in FoS values below 1, indicating a high likelihood of slope failure. In contrast, the CBS slope maintained FoS values generally above 1, as well as a smaller range of FoS variation, demonstrating enhanced stability and a more resilient response to rainfall. These findings emphasized the CBS’s capacity to enhance slope stability and mitigate landslide risks under extreme rainfall conditions along the NH10.
As this study did not include a comparative analysis of different CBS designs or alternative stabilization methods, future research could explore the performance of varying CBS settings, including different soil parameters and layering designs, to assess their effectiveness under diverse conditions. This would enable a more comprehensive assessment of the practical feasibility of various CBS designs, identifying the most cost−effective and applicable solutions for large and complex regions.

Author Contributions

Conceptualization, Y.C. and Y.L.; methodology, Y.C. and Y.L.; software, Y.C. and Y.L.; validation, Y.C. and Y.L.; formal analysis, Y.C.; investigation, Y.C. and Y.L.; resources, Y.C. and Y.L.; data curation, Y.C.; writing—original draft preparation, Y.C.; writing—review and editing, Y.C. and Y.L.; visualization, Y.C.; supervision, Y.L.; project administration, Y.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.

Acknowledgments

The authors would like to thank to Monash University, Australia, for the invaluable support provided throughout this research. The author would also like to acknowledge Saranya Rangarajan from the School of Civil and Environmental Engineering, Nanyang Technological University, Singapore, for her assistance with the software used in this research.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Configuration of soil parameters in the SoilParFile, a file providing the soil parameters, used for the GEOtop simulation. The parameters are described in Section 3.1 and Section 3.2. Layers 1 to 3 were set as residual soil properties and CBS properties, respectively, to obtain two groups of simulation outcomes for comparison.
Table A1. Configuration of soil parameters in the SoilParFile, a file providing the soil parameters, used for the GEOtop simulation. The parameters are described in Section 3.1 and Section 3.2. Layers 1 to 3 were set as residual soil properties and CBS properties, respectively, to obtain two groups of simulation outcomes for comparison.
Dz
(Layer Thickness, in mm)		Kh
(Lateral Hydraulic Conductivity, in mm/s)		Kv
(Normal Hydraulic Conductivity, in mm/s)		res
(Residual Water Content)		sat
(Saturated Water Content)		a
(Alpha Parameter of Van Genuchten)		n
(N parameter of Van Genuchten)		v
(M parameter of Van Genuchten)
2001.2 × 10−31.2 × 10−30.0178940.3870.0008783.6713540.727621
2001.2 × 10−31.2 × 10−30.0178940.3870.0008783.6713540.727621
200440.0105750.4370.0450423.8868010.742719
4001.32 × 10−21.32 × 10−20.07790.39620.0088991.9760900.493950
5001.32 × 10−21.32 × 10−20.07790.39620.0088991.9760900.493950
5001.32 × 10−21.32 × 10−20.07790.39620.0088991.9760900.493950
30001.32 × 10−21.32 × 10−20.07790.39620.0088991.9760900.493950
50001.32 × 10−51.32 × 10−50.07790.39620.0088991.9760900.493950
Table A2. Configuration of meteorological parameters in the MeteoFile, a file providing the meteo forcing data, used for the GEOtop simulation. These data mainly involve hourly precipitation, described in Section 3.1. The “Date” field can be set arbitrarily but must represent a continuous 48 h time series.
Table A2. Configuration of meteorological parameters in the MeteoFile, a file providing the meteo forcing data, used for the GEOtop simulation. These data mainly involve hourly precipitation, described in Section 3.1. The “Date” field can be set arbitrarily but must represent a continuous 48 h time series.
DatePrecipitation (mm/h)DatePrecipitation (mm/h)DatePrecipitation (mm/h)DatePrecipitation (mm/h)
16/10/2013 07:0012.6716/10/2013 19:0012.6717/10/2013 07:00017/10/2013 19:000
16/10/2013 08:0012.6716/10/2013 20:0012.6717/10/2013 08:00017/10/2013 20:000
16/10/2013 09:0012.6716/10/2013 21:0012.6717/10/2013 09:00017/10/2013 21:000
16/10/2013 10:0012.6716/10/2013 22:0012.6717/10/2013 10:00017/10/2013 22:000
16/10/2013 11:0012.6716/10/2013 23:0012.6717/10/2013 11:00017/10/2013 23:000
16/10/2013 12:0012.6717/10/2013 00:0012.6717/10/2013 12:00018/10/2013 00:000
16/10/2013 12:0012.6717/10/2013 01:0012.6717/10/2013 12:00018/10/2013 01:000
16/10/2013 14:0012.6717/10/2013 02:0012.6717/10/2013 14:00018/10/2013 02:000
16/10/2013 15:0012.6717/10/2013 03:0012.6717/10/2013 15:00018/10/2013 03:000
16/10/2013 16:0012.6717/10/2013 04:0012.6717/10/2013 16:00018/10/2013 04:000
16/10/2013 17:0012.6717/10/2013 05:0012.6717/10/2013 17:00018/10/2013 05:000
16/10/2013 18:0012.6717/10/2013 06:0012.6717/10/2013 18:00018/10/2013 06:000

Appendix B

Table A3. Comparison of FoS categories between the original slope and CBS slope at various depths and rainfall stages.
Table A3. Comparison of FoS categories between the original slope and CBS slope at various depths and rainfall stages.
Rainfall StagesSlope Stability Categories0.8 m1.25 m1.75 m
Original SlopeCBS SlopeOriginal SlopeCBS SlopeOriginal SlopeCBS Slope
Natural stateVery high risk0.00%0.00%0.00%0.00%0.53%0.67%
High risk0.00%0.00%0.10%0.20%4.71%5.34%
Moderate risk0.00%0.00%1.46%2.09%11.12%11.82%
Low risk100.00%100.00%98.44%97.71%83.64%82.17%
6 h of rainfallVery high risk0.00%0.00%0.00%0.00%1.67%1.91%
High risk0.00%0.00%0.10%0.20%7.36%8.00%
Moderate risk0.00%0.00%1.47%2.10%13.25%13.77%
Low risk100.00%100.00%98.43%97.70%77.72%76.32%
12 h of rainfallVery high risk0.00%0.00%0.00%0.01%2.05%2.31%
High risk0.00%0.00%0.11%0.21%7.88%8.46%
Moderate risk0.00%0.00%1.50%2.14%13.51%14.04%
Low risk100.00%100.00%98.39%97.65%76.56%75.19%
18 h of rainfallVery high risk0.51%0.00%0.00%0.01%2.35%2.58%
High risk10.59%0.14%0.12%0.23%8.18%8.78%
Moderate risk22.35%6.04%1.60%2.22%13.64%14.16%
Low risk66.55%93.82%98.28%97.54%75.83%74.47%
24 h of rainfallVery high risk10.11%0.79%0.08%0.02%2.56%2.80%
High risk21.04%15.00%5.62%0.28%8.42%8.99%
Moderate risk18.50%20.49%18.66%2.52%13.79%14.28%
Low risk50.35%63.71%75.63%97.18%75.23%73.93%
6 h after rainfall endsVery high risk12.00%4.66%5.56%0.07%2.76%3.00%
High risk19.80%18.13%22.54%0.48%9.28%9.18%
Moderate risk17.71%19.54%20.84%5.23%16.39%14.35%
Low risk50.49%57.67%51.06%94.22%71.56%73.47%
12 h after rainfall endsVery high risk11.25%4.67%10.59%0.14%3.21%3.19%
High risk19.30%17.73%22.41%1.30%13.26%9.34%
Moderate risk17.69%19.53%18.60%12.01%20.53%14.42%
Low risk51.77%58.07%48.40%86.54%62.99%73.05%
18 h after rainfall endsVery high risk10.64%4.41%12.77%0.21%4.50%3.34%
High risk18.96%17.26%21.38%2.94%17.63%9.47%
Moderate risk17.70%19.48%18.11%16.28%21.63%14.51%
Low risk52.69%58.85%47.75%80.58%56.24%72.68%
24 h after rainfall endsVery high risk10.16%4.18%13.66%0.29%6.22%3.48%
High risk18.70%16.88%20.91%4.65%20.79%9.58%
Moderate risk17.73%19.41%17.87%18.60%21.14%14.58%
Low risk53.42%59.54%47.56%76.46%51.85%72.37%

Appendix C

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Figure A1. Risk distribution at a depth of 1.25 m for the original slope and CBS slope at different rainfall stages.
Figure A1. Risk distribution at a depth of 1.25 m for the original slope and CBS slope at different rainfall stages.
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Figure A2. Risk distribution at a depth of 1.75 m for the original slope and CBS slope at different rainfall stages.
Figure A2. Risk distribution at a depth of 1.75 m for the original slope and CBS slope at different rainfall stages.
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Figure 1. Overview of the study area. (a) The full extent of India’s NH10. (b) Study section of NH10 with a 3 km buffer zone.
Figure 1. Overview of the study area. (a) The full extent of India’s NH10. (b) Study section of NH10 with a 3 km buffer zone.
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Figure 2. Maps used in GEOtop modeling: (a) DEM; (b) slope angle; (c) aspect.
Figure 2. Maps used in GEOtop modeling: (a) DEM; (b) slope angle; (c) aspect.
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Figure 3. Curve fitting for residual soil: conversion from the Gardner equation to the Van Genuchten equation.
Figure 3. Curve fitting for residual soil: conversion from the Gardner equation to the Van Genuchten equation.
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Figure 4. Curve fitting for CBS: conversion from the Fredlund and Xing equation to the Van Genuchten equation. (a). Fine−grained soil. (b). Coarse−grained soil.
Figure 4. Curve fitting for CBS: conversion from the Fredlund and Xing equation to the Van Genuchten equation. (a). Fine−grained soil. (b). Coarse−grained soil.
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Figure 5. Schematic diagrams of the original slope and CBS slope. The red dots indicate the center of each layer where the PWP values are calculated. (a). Original slope with eight residual soil layers. (b). CBS slope with fine− and coarse−grained soil layers in the top three layers.
Figure 5. Schematic diagrams of the original slope and CBS slope. The red dots indicate the center of each layer where the PWP values are calculated. (a). Original slope with eight residual soil layers. (b). CBS slope with fine− and coarse−grained soil layers in the top three layers.
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Figure 6. The workflow of the GEOtop model.
Figure 6. The workflow of the GEOtop model.
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Figure 7. Variations in the average PWP for the original and CBS slopes at various depths.
Figure 7. Variations in the average PWP for the original and CBS slopes at various depths.
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Figure 8. Variations in the percentage of pixels for each risk category between the original slope and CBS slope at various depths. (a). Very high risk. (b). High risk. (c). Moderate risk. (d). Low risk.
Figure 8. Variations in the percentage of pixels for each risk category between the original slope and CBS slope at various depths. (a). Very high risk. (b). High risk. (c). Moderate risk. (d). Low risk.
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Figure 9. Risk distribution at a depth of 0.8 m for original slope and CBS slope at different rainfall stages.
Figure 9. Risk distribution at a depth of 0.8 m for original slope and CBS slope at different rainfall stages.
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Figure 10. The location of each landslide point.
Figure 10. The location of each landslide point.
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Figure 11. Variations in the FoS for each landslide point. (a). North point. (b). Central point. (c). South point.
Figure 11. Variations in the FoS for each landslide point. (a). North point. (b). Central point. (c). South point.
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Table 1. Summary of residual soil and CBS (coarse−grained soil and fine−grained soil) properties.
Table 1. Summary of residual soil and CBS (coarse−grained soil and fine−grained soil) properties.
ParametersResidual SoilFine−Grained SoilCoarse−Grained Soil
Saturated hydraulic conductivity (Ks)1.32 × 10−5 m/s1.2 × 10−6 m/s4.0 × 10−3 m/s
Saturated water content (θs)0.39620.3870.437
Residual water content (θr)0.07790.01790.0106
αG0.271 kPa−1−−
aFX−10 kPa0.2 kPa
nFX−56
mFX−1.21.2
αVG0.8899 kPa−18.78 × 10−2 kPa−14.5042 kPa−1
nVG1.97613.67143.8868
mVG0.49400.72760.7427
Effective cohesion (c′)0.65 kPa00
Unit weight (γ)17.24 kN/m319.0 kN/m320.0 kN/m3
Saturated unit weight (γsat)20 kN/m325.393 kN/m327.762 kN/m3
Effective friction angle (φ′)30°34°35°
Table 2. Slope stability categories based on FoS values.
Table 2. Slope stability categories based on FoS values.
Slope Stability CategoriesFoS Range
Very high riskFoS ≤ 1
High risk1 < FOS ≤ 1.25
Moderate risk1.25 < FOS ≤ 1.5
Low riskFOS > 1.5
Table 3. Information of the three selected landslide points.
Table 3. Information of the three selected landslide points.
Landslide PointsLongitudeLatitudeMinimum FoS Value for Each Depth
0.8 m1.25 m1.75 m
OSCBSOSCBSOSCBS
North point88.430827.07130.86 1.00 0.88 1.23 0.87 0.87
Central point88.430127.02570.78 0.97 0.92 1.23 0.78 0.78
South point88.448126.97220.83 0.93 0.82 1.16 0.90 0.91
Notes: OS indicates the original slope. CBS indicates the CBS slope.
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Cheng, Y.; Li, Y. Application of Capillary Barrier Systems for Slope Stabilization Under Extreme Rainfall: A Case Study of National Highway 10, India. Infrastructures 2024, 9, 201. https://doi.org/10.3390/infrastructures9110201

AMA Style

Cheng Y, Li Y. Application of Capillary Barrier Systems for Slope Stabilization Under Extreme Rainfall: A Case Study of National Highway 10, India. Infrastructures. 2024; 9(11):201. https://doi.org/10.3390/infrastructures9110201

Chicago/Turabian Style

Cheng, Yusen, and Yangyang Li. 2024. "Application of Capillary Barrier Systems for Slope Stabilization Under Extreme Rainfall: A Case Study of National Highway 10, India" Infrastructures 9, no. 11: 201. https://doi.org/10.3390/infrastructures9110201

APA Style

Cheng, Y., & Li, Y. (2024). Application of Capillary Barrier Systems for Slope Stabilization Under Extreme Rainfall: A Case Study of National Highway 10, India. Infrastructures, 9(11), 201. https://doi.org/10.3390/infrastructures9110201

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