# A Laboratory Simulation Experiment to Assess Permeability and Shear Strength of a Gravel Soil Colluvium

^{1}

^{2}

^{*}

## Abstract

**:**

^{3D}numerical calculation of Shuping landslide show that the maximum deformation in the X direction of 145 m and 175 m water level increases by 12% and 42%, and the safety factor decreases by 0.63% and 5% under the combined action of rainfall and the reservoir water level, that is, when considering the variation of parameters along the elevation of the landslide. The research findings provide a better understanding of the spatial parameters in similar colluvium bodies under rainfall action.

## 1. Introduction

## 2. Brief Introduction to Shuping Colluvium Landslide

^{2}, and the average thickness is about 50 m. The slope is mainly composed of colluvial soil and silty clay mixed with gravel, and one of these sections is shown in Figure 1b.

## 3. Test Plan

#### 3.1. Model Test Apparatus

#### 3.2. Model Test Material

#### 3.3. Test Program

#### 3.3.1. Rainfall Design

#### 3.3.2. Sampling Point Layout

#### 3.3.3. Test Scheme

## 4. Analysis of Test Results

#### 4.1. The Deformation and Failure Characteristics of the Landslide Colluvium Model

#### 4.2. The Variation Law of Permeability along the Elevation

^{−3}cm/s at the bottom of the landslide (DW1) to 1.76 × 10

^{−3}cm/s at the top of landslide (DW3). Compared with the initial permeability coefficient of the colluvium particles before the rainfall, the permeability coefficient of the sampling point DW1 decreased significantly by 2.5%, while the permeability coefficient of the sampling point DW3 increased by 10%. In the interior of the landslide, the permeability coefficient increased from 1.59 × 10

^{−3}cm/s to 1.71 × 10

^{−3}cm/s from sampling points DW4 to DW5. The permeability coefficient decreased by 0.6% and remained almost unchanged, while the permeability coefficient of sampling point DW5 increased by 6.9%. Based on the above analysis, rainfall has changed the permeability coefficient of the colluvium. After the rainfall, whether it is on the surface of the colluvium slope or the interior of the slope, with the increase in elevation, the permeability coefficient increases linearly.

_{i}; and h

_{i}is the elevation of the colluvium model.

_{i}; and k

_{0}is the initial permeability coefficient of the colluvium model before the test. The average value of the phase change rate of the permeability coefficient is $2.73\times {10}^{-6}$. ${L}_{k1}=0.0147$ is the rate of change of the permeability coefficient of the colluvium at h

_{i}= 0.

#### 4.3. The Variation Law of Shear Strength Parameters along Elevation

_{i}; ${L}_{ci}$ is the change rate of the internal friction angle at h

_{i}; and h

_{i}is the elevation of the colluvium model.

_{i}of the colluvium model; ${\phi}_{i}$ is the internal friction angle at h

_{i}of the colluvium model; ${c}_{0}$ and ${\phi}_{0}$ are the initial values of the cohesion and the internal friction angle of the colluvium model before the test, respectively; and 0.498 and 0.0357 are the average values of the phase change rate of the cohesion and the internal friction angle, respectively. The change rate of the cohesion and the internal friction angle at h

_{1}= 0 are ${L}_{c1}=1.6353,{L}_{\phi 1}=-0.0729$, respectively.

## 5. Microscopic Analysis of the Variation of Particles in Colluvium along the Elevation

#### 5.1. Variation Characteristics of Element and Mineral Content

_{2}and Al

_{2}O

_{3}, displayed increases of 4.32%, 1.5%, 4.5%, and 10.34%, respectively, in contrast to their initial pre-rainfall levels. These increments exceeded those observed at other sampling points. Conversely, the content of the Fe element and iron minerals were lower at this point in comparison to the other sampling locations. After the rainfall, whether on the slope surface or inside the slope, the content of Si and Al elements and the content of minerals SiO

_{2}and Al

_{2}O

_{3}are higher at the toe slope position, while the content of Fe and iron minerals is bigger at the top slope. Within the colluvium’s raw materials, clay and river sand predominantly comprise Si elements and silicon minerals, whereas gravel is characterized by higher Fe elements and iron minerals; this underscores that rainwater-driven erosion and infiltration lead to the migration of fine-grained clay minerals within the colluvium from the upslope to the lower regions, while the transport impact of coarse gravel minerals through rainwater is either indistinct or minimal.

#### 5.2. Particle Gradation and Porosity Variation Characteristics

_{2}spectral distribution curve and the pore radius and pore size distribution of the colluvium particles at different elevation sampling points. It can be seen from Figure 9 that there are mainly two relatively obvious peaks in the nuclear magnetic resonance (NMR) of the colluvium particles, and the peaks are around the relaxation times of 1.5 ms and 1000 ms, respectively. The signal amplitude of the DW1 point near the 1 ms relaxation time is larger than that of other points, and the signal peak is reached the fastest, indicating that the number of micro-pores at the toe of the slope is large [28], and the pores are densely distributed. As the elevation of the sampling point increases, the area of the T

_{2}spectral curve increases, indicating that the greater the elevation at the higher points after rainfall, the greater the number of pores inside the colluvium particles. Compared with the sampling point DW1 at the toe of the slope, the T

_{2}spectral curve of the DW2~DW5 sampling point is panned to the right as a whole, and the NMR signal appears again within the relaxation time of 500~1000 ms, indicating that the colluvium particles at each elevation contain larger pore sizes after rainfall, and with the increase in elevation, the longer the relaxation time, the stronger the NMR signal that appears again, and the larger the pores. With an elevated colluvium elevation, the relaxation time proportionally extends, leading to a more robust re-emergence of the NMR signal, indicative of augmented pore size.

#### 5.3. Mechanism Analysis of Variation of Parameters along Elevation

_{2}and Al

_{2}O

_{3}minerals and the porosity of the colluvium body have different effects on the permeability coefficient and shear strength parameters. From DW3 to DW1 at the sampling point, from the top to the bottom of the slope, with the increase in mineral content, the permeability coefficient and internal friction angle decrease, while cohesion increases. On the contrary, with the decrease in porosity, the permeability coefficient and internal friction angle decrease, and the cohesion increases.

_{2}and Al

_{2}O

_{3}, under the action of rainfall, the fine particles of the colluvium are transported to the toe of the slope, while the coarse particles are unchanged. Therefore, there are more and more fine particles of clay at the toe of the slope, the original pores are gradually compacted, and the cementation between particles is stronger, increasing cohesion. On the contrary, due to the migration of fine particles, the content of coarse particles in the colluvium at the top of the slope increases relatively, and the friction resistance and porosity increase, so the internal friction angle and permeability coefficient increase. In summary, under the action of rainfall, the changes in shear strength parameters and the permeability coefficient of colluvium particles along the elevation are mainly due to the effect of water flow, which leads to the spatial migration of fine particles such as clay inside the landslide along the seepage direction and changes the internal particle distribution of the slope.

## 6. Analysis of Landslide Stability of Shuping Colluvium

^{3D}based on relevant geological data. The length, width, and height of the numerical calculation model are 760 m × 50 m × 330 m, respectively. The model grid is divided into 11,776 units and 11,036 nodes. The bottom surface and the vertical surface of the model are set as fixed boundaries, and the slope surface is free boundaries at x and z. The numerical calculation model is shown in Figure 13.

^{3D}, is mainly divided into two stages: (1) seepage calculation—in this stage, the model is cycled to equilibrium to obtain a steady-state seepage field; (2) based on the strength reduction method, the mechanical calculation of the safety factor of the colluvium is automatically performed. The Mohr–Coulomb criterion of the linear elastic–plastic constitutive model is used to characterize the colluvium, and the rainfall effect is simulated by the variation formula of the permeability coefficient and shear strength parameters along the elevation mentioned in Section 4. Since the completion of the Three Gorges Dam, the reservoir water level has changed between 145 m and 175 m. According to the actual situation, this paper set up four (①~④) working conditions for simulation analysis: ① 145 m reservoir water level; ② 175 m reservoir water level; ③ 145 m reservoir water level combined with rainfall; ④ 175 m reservoir water level combined with rainfall.

## 7. Conclusions

^{3D}numerical analysis software. The analysis explores landslide stability utilizing the Shuping landslide as a prototype, considering the impact of reservoir water level and rainfall. Analysis of the results yields the following key research findings:

- (1)
- Amidst intermittent rainwater seepage and runoff, notable alterations in the permeability and shear strength parameters of the colluvium transpire along the elevation gradient. With descent from the slope’s summit to its base, the permeability coefficient (k) and internal friction angle (φ) both manifest a linear decline. In contrast, cohesion (c) undergoes a linear increase. The most pronounced impact is attributable to rainfall on cohesion, succeeded by the permeability coefficient, while the internal friction angle experiences the least influence.
- (2)
- When compared to the initial model parameters, characterized by the absence of rainfall, noteworthy variations emerge. Specifically, the permeability coefficient (k) at the downslope surface decreased by 2.5%, while the cohesion (c) increased by a substantial 111.9%, and the internal friction angle (φ) experienced a reduction of 6.96%. In contrast, the upslope surface exhibited a distinct behavior, with a 10% increase in k, a decrease of 21.1% in c, and a 2.23% rise in φ. In terms of the colluvium body’s overall structure, subsequent to rainfall, the permeability coefficient, cohesion, and internal friction angle at the upper slope demonstrated values 1.13, 0.37, and 1.09 times, respectively, in comparison to those at the lower portion. While the internal modifications within the colluvium body followed a pattern akin to that near the surface, the magnitude of these alterations was comparatively less pronounced.
- (3)
- In contrast to the initial state of the colluvium prior to rainfall, a rise in clay mineral content along elevation is observed, followed by a reduction post-rainfall. Notably, the key constituents Si, Al, and the minerals SiO
_{2}and Al_{2}O_{3}in the clay located at the base of the colluvium model register increments of 4.32%, 1.5%, 4.5%, and 10.34%, respectively. Concurrently, a decline in elevation corresponds to a reduction in both the number and dimensions of pores within the colluvium. This phenomenon underscores that under the influence of rainfall-driven seepage, fine clay particles migrate towards the slope toe, aligning with the seepage direction. Accumulation of fine clay particles at the slope toe leads to the gradual filling of original pores, intensifying particle cementation, resulting in elevated cohesion and diminished permeability coefficient. Simultaneously, the transportation of fine particles triggers a relative surge in coarse particle content upslope, amplifying friction resistance and augmenting the internal friction angle. - (4)
- Considering the combined impact of rainfall and reservoir water level, including the variation of parameters along the landslide elevation, in contrast to scenarios solely involving the effect of reservoir water level at the same elevation, the maximum deformation of the Shuping landslide increased by 12.81% and 42.52% in the X direction at the water levels of 145 m and 175 m, respectively. Nonetheless, the safety factor experienced reductions of 0.63% and 5.13%, respectively. This highlights the significance of accounting for the variability in the physical and mechanical parameters of the landslide along the elevation during numerical calculations. Ignoring this variability can result in an overestimation of the calculated safety factor, subsequently leading to an inflated estimation of colluvium stability. Consequently, incorporating the variability of physical and mechanical parameters induced by rainfall in slope engineering design enhances the reliability of the design outcomes.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

- Jian, W.; Wang, Z.; Yin, K. Mechanism of the Anlesi landslide in the three gorges reservoir, China. Eng. Geol.
**2009**, 108, 86–95. [Google Scholar] [CrossRef] - Wang, J.; Su, A.; Xiang, W.; Yeh, H.F.; Xiong, C.; Zou, Z.; Liu, Q. New data and interpretations of the shallow and deep deformation of Huangtupo No. 1 riverside sliding mass during seasonal rainfall and water level fluctuation. Landslides
**2016**, 13, 795–804. [Google Scholar] [CrossRef] - Yang, B.; Yin, K.; Xiao, T.; Chen, L.; Du, J. Annual variation of landslide stability under the effect of water level fluctuation and rainfall in the Three Gorges Reservoir, China. Environ. Earth Sci.
**2017**, 76, 564. [Google Scholar] [CrossRef] - Dai, Z.; Chen, S.; Li, J. Physical model test of seepage and deformation characteristics of shallow expansive soil slope. Bull. Eng. Geol. Environ.
**2020**, 79, 4063–4078. [Google Scholar] [CrossRef] - He, C.; Hu, X.; Tannant, D.D.; Tan, F.; Zhang, Y.; Zhang, H. Response of a landslide to reservoir impoundment in model tests. Eng. Geol.
**2018**, 247, 84–93. [Google Scholar] [CrossRef] - He, C.C.; Hu, X.L.; Xu, C.; Wu, S.S.; Zhang, H.; Liu, C. Model test of the influence of cyclic water level fluctuations on a landslide. J. Mt. Sci.
**2020**, 17, 191–202. [Google Scholar] [CrossRef] - Wang, L.H.; Yin, S.J.; Xu, X.L. Model test of deformation failure mode of the bank slope deformation of the colluvium under the action of rainfall. Sci. Tech.
**2018**, 18, 77–82. (In Chinese) [Google Scholar] - Xiong, X.; Shi, Z.; Xiong, Y.; Peng, M.; Ma, X.; Zhang, F. Unsaturated slope stability around the Three Gorges Reservoir under various combinations of rainfall and water level fluctuation. Eng. Geol.
**2019**, 261, 105231. [Google Scholar] [CrossRef] - Lukić, T.; Bjelajac, D.; Fitzsimmons, K.E.; Marković, S.B.; Basarin, B.; Mlađan, D.; Micić, T.; Schaetzl, J.R.; Gavrilov, M.B.; Milanović, M. Factors triggering landslide occurrence on the Zemun loess plateau, Belgrade area, Serbia. Environ. Earth Sci.
**2018**, 77, 519. [Google Scholar] [CrossRef] - Li, Q.; Huang, D.; Pei, S.; Qiao, J.; Wang, M. Using Physical Model Experiments for Hazards Assessment of Rainfall-Induced Debris Landslides. J. Earth Sci.
**2021**, 32, 1113–1128. [Google Scholar] [CrossRef] - Morar, C.; Lukić, T.; Basarin, B.; Valjarević, A.; Vujičić, M.; Niemets, L.; Telebienieva, I.; Boros, L.; Nagy, G. Shaping Sustainable Urban Environments by Addressing the Hydro-Meteorological Factors in Landslide Occurrence: Ciuperca Hill (Oradea, Romania). Int. J. Environ. Res. Public Health
**2021**, 18, 5022. [Google Scholar] [CrossRef] - Griffiths, D.V.; Fenton, G.A. Probabilistic slope stability analysis by finite elements. J. Geotech. Geoenviron.
**2004**, 130, 507–518. [Google Scholar] [CrossRef] - Schieber, J. Reverse engineering mother nature-Shale sedimentology from an experimental perspective. Sediment. Geol.
**2011**, 238, 1–22. [Google Scholar] [CrossRef] - Huang, J.; Lyamin, A.V.; Griffiths, D.V.; Krabbenhoft, K.; Sloan, S.W. Quantitative risk assessment of landslide by limit analysis and random fields. Comput. Geotech.
**2013**, 53, 60–67. [Google Scholar] [CrossRef] - Xiao, H.; Guo, G.; Chen, L. Research on dynamic evaluation model of slope risk based on improved VW-UM. Math. Probl. Eng.
**2019**, 2019, 5813217. (In Chinese) [Google Scholar] [CrossRef] - Hua, Y.; Wang, X.; Li, Y.; Xu, P.; Xia, W. Dynamic development of landslide susceptibility based on slope unit and deep neural networks. Landslides
**2021**, 18, 281–302. [Google Scholar] [CrossRef] - Yang, H.Q.; Zhang, L.; Pan, Q. Bayesian estimation of spatially varying soil parameters with spatiotemporal monitoring data. Acta Geotech.
**2021**, 16, 263–278. [Google Scholar] [CrossRef] - Rana, H.; Sivakumar Babu, G.L. Probabilistic back analysis for rainfall-induced slope failure using MLS-SVR and Bayesian analysis. Georisk Assess. Manag. Risk Eng. Syst. Geohazards
**2022**, 1–14. [Google Scholar] [CrossRef] - Jiang, S.H.; Papaioannou, I.; Straub, D. Bayesian updating of slope reliability in spatially variable soils with in-situ measurements. Eng. Geol.
**2018**, 239, 310–320. [Google Scholar] [CrossRef] - Zhang, W.G.; Meng, F.S.; Chen, F.Y.; Liu, H.L. Effects of spatial variability of weak layer and seismic randomness on rock slope stability and reliability analysis. Soil Dyn. Earthq. Eng.
**2021**, 146, 106735. [Google Scholar] [CrossRef] - Mori, H.; Chen, X.; Leung, Y.F.; Shimokawa, D.; Lo, M.K. Landslide hazard assessment by smoothed particle hydrodynamics with spatially variable soil properties and statistical rainfall distribution. Can. Geotech. J.
**2020**, 57, 1953–1969. [Google Scholar] [CrossRef] - Li, J.; Luo, W.; Tian, Y.; Wang, Y.; Cassidy, M.J. Modeling of large deformation problem considering spatially variable soils in offshore engineering. Mar. Georesour. Geotec.
**2021**, 39, 906–918. [Google Scholar] [CrossRef] - Chen, X.; Li, D.; Tang, X.; Liu, Y. A three-dimensional large-deformation random finite-element study of landslide runout considering spatially varying soil. Landslides
**2021**, 18, 3149–3162. [Google Scholar] [CrossRef] - Ding, Y.N.; Li, D.Q.; Zarei, C.; Yi, B.L.; Liu, Y. Probabilistically quantifying the effect of geotechnical anisotropy on landslide susceptibility. Bull. Eng. Geol. Environ.
**2021**, 80, 6615–6627. [Google Scholar] [CrossRef] - Xia, Y.X.; Cheng, P.; Liu, M.M.; Hu, J. Numerical Modeling of 3D Slopes with Weak Zones by Random Field and Finite Elements. Appl. Sci.
**2021**, 11, 9852. [Google Scholar] [CrossRef] - Luo, X.Q.; Bi, J.F. Theory and Application of Geomechanical Model Test; Shanghai Jiaotong University Press: Shanghai, China, 2011; pp. 90–101. (In Chinese) [Google Scholar]
- Zhi, Y.Y. Experimental Study and Mechanism of MICP Reinforcement for Fractured Rock Mass. Master’s Thesis, China Three Gorges University, Yichang, China, 2020. (In Chinese). [Google Scholar]
- Grunewald, E.; Knight, R. A laboratory study of NMR relaxation times and pore coupling in heterogeneous media. Geophysics
**2009**, 74, 215–221. [Google Scholar] [CrossRef] - Zhang, K.G.; Liu, S.G. Soil Mechanics, 3rd ed.; China Architecture and Construction Press: Beijing, China, 2010; pp. 188–210. (In Chinese) [Google Scholar]
- Kozlowski, T.; Ludynia, A. Permeability coefficient of low permeable soils as a single-variable function of soil parameter. Water
**2019**, 11, 2500. [Google Scholar] [CrossRef]

**Figure 1.**Geological condition map of the Shuping landslide: (

**a**) topography and Shuping landslide-covered area; (

**b**) cross section of the Shuping landslide with lithostratigraphy.

**Figure 2.**Test model of colluvium landslide: (

**a**) schematic diagram of the model; (

**b**) photo of the model.

**Figure 6.**The relationship between shear strength parameters and elevation: (

**a**) the relationship between colluvium c and the elevation; (

**b**) the relationship between φ and the elevation.

**Figure 7.**Variation trend of particle element and mineral content at sampling points at different elevations: (

**a**) changes in the content of elements; (

**b**) changes in mineral content.

**Figure 11.**Relationship between minerals content and physical and mechanical parameters of colluvium.

**Figure 14.**The plastic deformation zone of the landslide under different working conditions: (

**a**) Condition 1; (

**b**) Condition 2; (

**c**) Condition 3; (

**d**) Condition 4.

**Figure 15.**The safety factor of landslides under different working conditions: (

**a**) Condition 1; (

**b**) Condition 2; (

**c**) Condition 3; (

**d**) Condition 4.

**Figure 16.**Maximum displacement and stability coefficient of landslide under different working conditions.

**Table 1.**Physical and mechanical parameters of test landslide colluvium and Shuping landslide rock mass.

Soil Sample Category | Density ρ/g·cm ^{−3} | Content of Stone/% | Water Ratio/% | c/kPa | $\mathit{\phi}/$° | k/cm·s^{−1} | Coefficient of Nonuniformity | Coefficient of Curvature |
---|---|---|---|---|---|---|---|---|

Shuping landslide | 2.01 | 68 | 23.4 | 20.7 | 23.5 | 1.02 × 10^{−2} | 30.36 | 1.21 |

Test landslide colluvium | 2.0 | 68 | 8.0 | 33.66 | 36.78 | 1.60 × 10^{−3} | 28.0 | 1.90 |

Point Number | Elevation h/cm | k/cm·s^{−1} | ${\mathit{L}}_{\mathit{i}}$ | $\mathbf{\Delta}{\mathit{L}}_{\mathit{i}}$/k·cm^{−1} | |
---|---|---|---|---|---|

Surface | DW1 | 16 | 1.56 × 10^{−3} | −0.025 | / |

DW2 | 55 | 1.62 × 10^{−3} | 0.0125 | 1.5 × 10^{−6} | |

DW3 | 94 | 1.76 × 10^{−3} | 0.10 | 3.6 × 10^{−6} | |

Interior | DW4 | 16 | 1.59 × 10^{−3} | −0.006 | / |

DW5 | 55 | 1.71 × 10^{−3} | 0.069 | 3.1 × 10^{−6} |

Elevation | 16 cm | 55 cm | 94 cm |
---|---|---|---|

Average value | 0.0155 | 0.041 | 0.1 |

Point Number | Points | c kPa/cm | ${\mathit{L}}_{\mathit{i}}$ of c | $\mathbf{\Delta}{\mathit{L}}_{\mathit{i}}$ c kPa/cm | φ/° | ${\mathit{L}}_{\mathit{i}}$ of φ | $\mathbf{\Delta}{\mathit{L}}_{\mathit{i}}$ φ °/cm |
---|---|---|---|---|---|---|---|

Surface | DW1 | 71.3 | 1.119 | / | 34.22 | −0.0696 | / |

DW2 | 48.08 | 0.428 | −0.595 | 35.75 | −0.028 | 0.039 | |

DW3 | 26.56 | −0.211 | −0.552 | 37.6 | 0.0223 | 0.047 | |

Interior | DW4 | 65.03 | 0.932 | / | 34.99 | −0.0487 | / |

DW5 | 51.48 | 0.529 | −0.347 | 36.13 | −0.0177 | 0.021 |

Parameter | 16 cm | 55 cm | 94 cm |
---|---|---|---|

Internal friction angle φ/° | 1.026 | 0.479 | −0.211 |

Cohesion c/kPa | −0.060 | −0.023 | 0.022 |

Category | ${\mathit{\rho}}_{0}$/g·cm^{−3} | $\mathbf{k}$/cm·s^{−1} | $\mathit{E}$/MPa | v | $\mathit{K}$/Pa | $\mathit{G}$/Pa | $\mathit{c}$/kPa | $\mathit{\phi}$ |
---|---|---|---|---|---|---|---|---|

Landslide | 2.01 | 1.02 × 10^{−2} | 300 | 0.255 | 2.04 × 10^{8} | 1.19 × 10^{8} | 20.7 | 23.5 |

Sliding zone | - | / | 300 | 0.45 | 1 × 10^{9} | 1.03 × 10^{8} | 19.2 | 20.4 |

Bedrock | 2.61 | / | 5000 | 0.22 | 2.98 × 10^{9} | 2.05 × 10^{9} | 3.38 × 10^{3} | 46 |

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Xu, X.; Zhang, J.; Ji, E.; Wang, L.; Huang, P.; Wang, X.
A Laboratory Simulation Experiment to Assess Permeability and Shear Strength of a Gravel Soil Colluvium. *Water* **2023**, *15*, 3089.
https://doi.org/10.3390/w15173089

**AMA Style**

Xu X, Zhang J, Ji E, Wang L, Huang P, Wang X.
A Laboratory Simulation Experiment to Assess Permeability and Shear Strength of a Gravel Soil Colluvium. *Water*. 2023; 15(17):3089.
https://doi.org/10.3390/w15173089

**Chicago/Turabian Style**

Xu, Xiaoliang, Jiafu Zhang, Enyue Ji, Lehua Wang, Peng Huang, and Xiaoping Wang.
2023. "A Laboratory Simulation Experiment to Assess Permeability and Shear Strength of a Gravel Soil Colluvium" *Water* 15, no. 17: 3089.
https://doi.org/10.3390/w15173089