Deep-Learning-Enhanced CT Image Analysis for Predicting Hydraulic Conductivity of Coarse-Grained Soils
Abstract
:1. Introduction
2. Materials and Methods
2.1. Hydraulic Conductivity Calculation Model for Coarse-Grained Soil Based on Equivalent Simplified Model and Poiseuille’s Law
2.2. Materials
2.3. Laboratory Test
- A settlement measurement device was installed at the top of the coarse-grained soil specimens to prevent seepage deformation during the test.
- Aerated water was used to negate the impact of air bubbles on the percolation volume.
- Before the test, the specimen was saturated with bottom-up exhaust under a lower head and soaked for over 8 h to eliminate the influence of non-saturation on the permeability test results.
- The test head from the starting hydraulic slope dropped from 0.05 to 0.30, loaded step by step, with each head level loaded for 20 min before measuring the overflow in the permeameter and recording it.
- The next level of head was loaded only when the overflow in the unit time remained unchanged. This process continued until the test concluded.
2.4. Coarse-Grained Soil CT Image Segmentation Method Based on Convolutional Neural Network
2.4.1. U-Net Structured Convolutional Neural Network
2.4.2. Loss Function
2.4.3. Workflow of Convolutional Neural Network Segmentation Model
2.5. Geometric Characterization of Coarse Soil Particles via CT Image Analysis
2.6. Data Analysis
3. Results
3.1. Accuracy Verification of CT Image-Segmentation Program Based on Convolutional Neural Network
3.1.1. Verification of CT Image-Segmentation Accuracy Based on Convolutional Neural Networks
3.1.2. Comparison of Segmentation Results between CNN Model and Traditional Methods for CT Images of Coarse-Grained Soil
3.2. Equivalent Simplified Model of Ideal Particle Diameter in Coarse-Grained Soil
3.2.1. Three-Dimensional Model Reconstruction of Coarse-Grained Soil Based on CT Image Segmentation Results
3.2.2. Empirical Formula for the Ideal Particle Size of Coarse Soil Particles
3.3. Prediction Formula for Permeability Coefficient of Coarse-Grained Soil Based on CT Image Analysis
3.3.1. Constant-Head Permeability Test Results
3.3.2. Empirical Formula for Discount Factor
3.3.3. Prediction Formula and Accuracy Verification of Hydraulic Conductivity of Coarse-Grained Soil
4. Discussion
5. Conclusions
- The implementation of the CNN model demonstrates unparalleled precision in the segmentation of coarse-grained soil CT images, ascertaining the model’s superiority over traditional segmentation methods. The accuracy of the 3D models reconstructed from these segmented images corroborates the effectiveness of this approach and broadens the prospects of automation and precision in soil particle segmentation.
- We established and validated empirical formulae for the ideal particle size of coarse-grained soil and the discount factor, both predicated on a robust linear correlation found in the study. These novel formulae contribute significantly to understanding the granulometric characteristics of soils and predicting their behavior under various hydraulic gradients, thus providing valuable insights for soil-related engineering and hydraulic applications.
- Our research underlines the strong influence of the granular composition, especially the concentration of fine particles, on the tortuosity of water flow paths and the discount factor. These findings highlight the potential of the CNN model in soil hydrodynamics research and its implications for a variety of fields, including water conservancy and geotechnical engineering.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Sample ID | S1 | S2 | S3 | S4 | S5 | S6 |
---|---|---|---|---|---|---|
Porosity | 40% | 30% | 40% | 40% | 38% | 35% |
Sample ID | S7 | S8 | S9 | S10 | S11 | S12 |
Porosity | 38% | 35% | 35% | 32% | 30% | 35% |
Sample ID | S1 | S2 | S3 | S4 | S5 | S6 |
---|---|---|---|---|---|---|
Cu | 1.65 | 3.05 | 3.58 | 3.66 | 3.68 | 3.05 |
Cc | 1.07 | 1.20 | 0.80 | 1.06 | 0.89 | 1.29 |
Sample ID | S7 | S8 | S9 | S10 | S11 | S12 |
Cu | 2.38 | 3.36 | 3.52 | 3.11 | 2.93 | 3.45 |
Cc | 1.34 | 1.11 | 1.14 | 1.31 | 1.27 | 0.98 |
Metric | Expression [43] | Range |
---|---|---|
IoU | Metric is between 0 and 1, and the closer it is to 1, the better the model performs. | |
Precision | ||
Recall | ||
Accuracy | ||
Specificity |
Sample ID | S1 | S2 | S3 | S4 | S5 | S6 | S7 | S8 | S9 | S10 | S11 | S12 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
deq20 (mm) | 6.26 | 2.71 | 1.72 | 2.21 | 2.01 | 2.46 | 3.29 | 1.19 | 1.24 | 2.30 | 2.90 | 2.30 |
2.31 | 1.93 | 2.03 | 1.95 | 2.04 | 1.86 | 1.99 | 1.98 | 2.03 | 1.93 | 1.92 | 2.00 | |
d20 (mm) | 10.29 | 5.20 | 3.94 | 4.49 | 4.16 | 4.82 | 6.34 | 3.66 | 3.74 | 4.69 | 5.48 | 4.55 |
Sample ID | S1 | S2 | S3 | S4 | S5 | S6 | S7 | S8 | S9 | S10 | S11 | S12 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
K (cm/s) | 3.45 | 2.30 | 4.58 | 4.30 | 5.81 | 1.37 | 2.70 | 2.72 | 2.62 | 0.86 | 0.99 | 3.04 |
α | 162.29 | 48.79 | 39.42 | 52.27 | 36.65 | 77.88 | 87.99 | 26.74 | 28.39 | 76.80 | 79.58 | 48.88 |
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Peng, J.; Shen, Z.; Zhang, W.; Song, W. Deep-Learning-Enhanced CT Image Analysis for Predicting Hydraulic Conductivity of Coarse-Grained Soils. Water 2023, 15, 2623. https://doi.org/10.3390/w15142623
Peng J, Shen Z, Zhang W, Song W. Deep-Learning-Enhanced CT Image Analysis for Predicting Hydraulic Conductivity of Coarse-Grained Soils. Water. 2023; 15(14):2623. https://doi.org/10.3390/w15142623
Chicago/Turabian StylePeng, Jiayi, Zhenzhong Shen, Wenbing Zhang, and Wen Song. 2023. "Deep-Learning-Enhanced CT Image Analysis for Predicting Hydraulic Conductivity of Coarse-Grained Soils" Water 15, no. 14: 2623. https://doi.org/10.3390/w15142623
APA StylePeng, J., Shen, Z., Zhang, W., & Song, W. (2023). Deep-Learning-Enhanced CT Image Analysis for Predicting Hydraulic Conductivity of Coarse-Grained Soils. Water, 15(14), 2623. https://doi.org/10.3390/w15142623