Permeability Characteristics of Improved Loess and Prediction Method for Permeability Coefficient
Abstract
:1. Introduction
2. Materials and Methods
2.1. Test Materials
2.1.1. Malan Loess
2.1.2. Lishi Loess
2.1.3. Argillaceous Sandstone
2.1.4. Loess-like Silt
2.2. Falling Head Permeability Test Method and Test Scheme
3. Results and Analysis
3.1. Test Results and Analysis of Improved Loess with Single Index
3.1.1. Influence of Admixture on Permeability Coefficient
3.1.2. Influence of Dry Density on Permeability Coefficient
3.1.3. Influence of Grain Size Characteristics on Permeability Coefficient
3.1.4. Influence of Grading Characteristics on Permeability Coefficient
3.2. Gray Correlation Analysis of Factors Affecting Permeability Coefficient
3.3. SVM-Based Prediction Model of Improved Loess Permeability
3.3.1. Construction of the Model
3.3.2. Selection of Parameters
3.3.3. Model Prediction Results
3.3.4. Comparison of Permeability Prediction Models
4. Discussion
5. Conclusions
- (1)
- Without considering the effects of admixtures, grain size, and grading characteristics, the coefficient of permeability of the improved loess tends to decrease with increasing dry density, and there is an obvious turning point in the relationship curve.
- (2)
- Under the same grain size, grading characteristics, and dry density, the permeability coefficient of the improved loess decreases with the increase in admixture (cement and lime) content, and the cement admixture has a more significant effect on the permeability coefficient of specimens with a larger grain size and smaller Cu.
- (3)
- The improved loess’s grain size and grading parameters influence the permeability coefficient. There exists a positive linear correlation between the permeability coefficient and three parameters, namely the average grain size, restricted grain size, and the product of the coefficient of curvature and coefficient of uniformity (Cu*Cc).
- (4)
- Gray correlation analysis was conducted on the permeability coefficient of improved loess material and its influencing factors, and it was concluded that the optimal influencing factor of the permeability coefficient was the type of admixture, and Cc and d60 were the secondary influencing factors. According to the importance of the influencing factors, a support vector machine-based prediction model for the permeability of improved loess materials was proposed. The actual prediction results show that the SVM model prediction results are significantly better than the linear regression and the neural network prediction models, and the prediction errors are more diminutive.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Name | Natural Density (g/cm3) | Dry Density (g/cm3) | Maximum Dry Density (g/cm3) | Natural Water Content (%) | Optimum Moisture Content (%) | Specific Weight (Gs) | Liquid Limit (%) | Plastic Limit (%) | Plasticity Index | Void Ratio |
---|---|---|---|---|---|---|---|---|---|---|
Loess-like silt | 1.49 | 1.36 | 1.80 | 9.7 | 11.7 | 2.75 | 26.1 | 18.9 | 7.2 | 0.995 |
Malan loess | 1.52 | 1.42 | 1.88 | 6.9 | 13.2 | 2.73 | 26.5 | 19.1 | 7.4 | 0.901 |
Lishi loess | 1.80 | 1.61 | 1.80 | 11.9 | 14.0 | 2.46 | 28.0 | 19.8 | 8.2 | 0.676 |
Argillaceo-us sandstone | 2.09 | 1.94 | / | 7.8 | / | / | / | / | / | 0.364 |
Sample Name | Content of Admixture (%) | Type of Admixture | d10 (mm) | d50 (mm) | d60 (mm) | Cu | Cc | Dry Density (g/cm3) | Permeability Coefficient (cm/s) |
---|---|---|---|---|---|---|---|---|---|
PL1 | 0 | None | 0.0828 | 0.32 | 0.46 | 5.58 | 0.67 | 1.75 | 2.65 × 10−6 |
PL2 | 0 | None | 0.0807 | 0.30 | 0.43 | 5.31 | 0.71 | 1.86 | 7.97 × 10−7 |
PL3 | 0 | None | 0.0828 | 0.26 | 0.37 | 4.41 | 0.70 | 1.80 | 2.74 × 10−6 |
PL4 | 0 | None | 0.0828 | 0.24 | 0.33 | 4.03 | 0.73 | 1.71 | 2.00 × 10−6 |
PL5 | 0 | None | 0.0828 | 0.23 | 0.30 | 3.68 | 0.72 | 1.99 | 2.29 × 10−7 |
PL6 | 0 | None | 0.0828 | 0.21 | 0.28 | 3.35 | 0.75 | 1.76 | 6.44 × 10−8 |
PL7 | 0 | None | 0.0987 | 0.44 | 0.62 | 6.33 | 0.80 | 1.85 | 6.16 × 10−8 |
PL8 | 0 | None | 0.1037 | 0.49 | 0.69 | 6.64 | 0.91 | 1.88 | 1.07 × 10−5 |
PL9 | 0 | None | 0.1032 | 0.44 | 0.61 | 5.89 | 0.89 | 1.85 | 4.72 × 10−7 |
PL10 | 0 | None | 0.0828 | 0.36 | 0.54 | 6.57 | 0.66 | 1.84 | 1.09 × 10−7 |
PL11 | 0 | None | 0.0828 | 0.36 | 0.54 | 6.57 | 0.66 | 1.61 | 4.86 × 10−7 |
PL12 | 0 | None | 0.0848 | 0.42 | 0.65 | 7.68 | 0.65 | 1.90 | 4.67 × 10−7 |
PL13 | 0 | None | 0.0848 | 0.42 | 0.65 | 7.68 | 0.65 | 1.82 | 4.85 × 10−7 |
PL14 | 0 | None | 0.0848 | 0.49 | 0.78 | 9.20 | 0.65 | 1.90 | 1.04 × 10−7 |
PL15 | 0 | None | 0.0848 | 0.49 | 0.78 | 9.20 | 0.65 | 1.82 | 7.12 × 10−7 |
PL16 | 0 | None | 0.0848 | 0.49 | 0.78 | 9.20 | 0.65 | 1.77 | 5.12 × 10−6 |
PL17 | 0 | None | 0.0848 | 0.49 | 0.78 | 9.20 | 0.65 | 1.69 | 9.12 × 10−6 |
PL18 | 9 | Cement | 0.091 | 0.50 | 0.76 | 8.30 | 0.65 | 1.62 | 2.14 × 10−6 |
PL19 | 12 | Cement | 0.091 | 0.50 | 0.76 | 8.30 | 0.65 | 1.62 | 7.09 × 10−7 |
PL20 | 15 | Cement | 0.091 | 0.50 | 0.76 | 8.30 | 0.65 | 1.63 | 2.78 × 10−7 |
PL21 | 9 | Cement | 0.091 | 0.42 | 0.64 | 7.03 | 0.64 | 1.64 | 2.02 × 10−7 |
PL22 | 12 | Cement | 0.091 | 0.42 | 0.64 | 7.03 | 0.64 | 1.62 | 3.85 × 10−6 |
PL23 | 15 | Cement | 0.091 | 0.42 | 0.64 | 7.03 | 0.64 | 1.65 | 1.78 × 10−7 |
PL24 | 6 | Lime | 0.085 | 0.49 | 0.78 | 9.20 | 0.65 | 1.78 | 8.07 × 10−7 |
PL25 | 9 | Lime | 0.085 | 0.49 | 0.78 | 9.20 | 0.65 | 1.81 | 6.93 × 10−7 |
PL26 | 12 | Lime | 0.085 | 0.49 | 0.78 | 9.20 | 0.65 | 1.81 | 6.23 × 10−7 |
PL27 | 5 | Cement | 0.085 | 0.49 | 0.78 | 9.20 | 0.65 | 1.81 | 3.59 × 10−7 |
PL28 | 9 | Cement | 0.085 | 0.49 | 0.78 | 9.20 | 0.65 | 1.80 | 2.58 × 10−7 |
PL29 | 9 | Cement | 0.085 | 0.49 | 0.78 | 9.20 | 0.65 | 1.48 | 6.29 × 10−8 |
PL30 | 12 | Cement | 0.085 | 0.49 | 0.78 | 9.20 | 0.65 | 1.62 | 7.09 × 10−7 |
PL31 | 15 | Cement | 0.085 | 0.49 | 0.78 | 9.20 | 0.65 | 1.63 | 5.23 × 10−6 |
PL32 | 5 | Cement | 0.083 | 0.36 | 0.54 | 6.57 | 0.65 | 1.81 | 3.72 × 10−7 |
PL33 | 5 | Cement | 0.085 | 0.42 | 0.65 | 7.68 | 0.65 | 1.82 | 3.64 × 10−7 |
PL34 | 15 | Cement | 0.085 | 0.49 | 0.78 | 9.20 | 0.65 | 1.65 | 8.47 × 10−7 |
PL35 | 15 | Cement | 0.085 | 0.49 | 0.78 | 9.20 | 0.65 | 1.74 | 5.00 × 10−7 |
PL36 | 15 | Cement | 0.085 | 0.49 | 0.78 | 9.20 | 0.65 | 1.80 | 7.60 × 10−8 |
Sample Name | Measured Values | Predicted Values |
---|---|---|
PL33 | 3.64 × 10−7 | 4.16 × 10−7 |
PL34 | 8.47 × 10−7 | 8.09 × 10−7 |
PL35 | 5.00 × 10−7 | 5.73 × 10−7 |
PL36 | 7.60 × 10−7 | 9.19 × 10−7 |
Prediction Models | SVM Regression | Multiple Linear Regression | Neural Network Regression |
---|---|---|---|
RMSE | 1.30 × 10−7 | 2.11 × 10−6 | 1.07 × 10−6 |
R2 | 0.494 | 0.898 | 0.982 |
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Ran, G.; Zhu, Y.; Yang, X.; Huang, A.; Chen, D. Permeability Characteristics of Improved Loess and Prediction Method for Permeability Coefficient. Appl. Sci. 2024, 14, 8072. https://doi.org/10.3390/app14178072
Ran G, Zhu Y, Yang X, Huang A, Chen D. Permeability Characteristics of Improved Loess and Prediction Method for Permeability Coefficient. Applied Sciences. 2024; 14(17):8072. https://doi.org/10.3390/app14178072
Chicago/Turabian StyleRan, Guoliang, Yanpeng Zhu, Xiaohui Yang, Anping Huang, and Dong Chen. 2024. "Permeability Characteristics of Improved Loess and Prediction Method for Permeability Coefficient" Applied Sciences 14, no. 17: 8072. https://doi.org/10.3390/app14178072