A Machine-Learning-Based Method for Identifying the Failure Risk State of Fissured Sandstone under Water–Rock Interaction
Abstract
:1. Introduction
2. Methodology
2.1. Material Preparation and Experiment Setup
2.1.1. Material Preparation
2.1.2. Uniaxial Compression Test of Fissured Sandstone
2.2. Machine Learning Models
2.2.1. Random Forest
2.2.2. Multilayer Perceptron
2.2.3. AdaBoost
2.2.4. Model Performance Evaluation Metrics
3. Analysis of Instability Precursor Information in Fissured Sandstone
3.1. Failure Stage Division of Fissured Sandstone
3.2. AE Energy and AE Ringing Count Characterization
3.3. Centroid Frequency and Peak Frequency Characterization
3.4. RA Value and AF Value Characterization
3.5. b Value Characterization
4. Development of Instability State Identification Model Based on Precursor Information
4.1. Data Acquisition
4.2. Data Preprocessing
4.3. Dataset Establishment
4.4. Dataset Distribution
4.4.1. Data Distribution of Different States
4.4.2. Data Distribution of Different Instability Risk States
4.5. Dataset Splitting
4.6. Hyperparameter Splitting
4.7. Model Training
5. Performance Analysis and Input Feature Valuation of Machine Learning Models
5.1. Models’ Performance
5.2. Importance Analysis of Model Input Features
5.3. Correlation Analysis of Model Input Features
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Confusion Matrix | Predicted Label | |||
---|---|---|---|---|
0 | 1 | 2 | ||
True label | 0 | 00 | 01 | 01 |
1 | 10 | 11 | 12 | |
2 | 20 | 21 | 22 |
Parameter | Parameter Set for This Model |
---|---|
n_estimators | 5 |
max_depth | 10 |
min_samples_leaf | 1 |
min_samples_split | 2 |
class_weight | balanced |
criterion | gini |
Parameter | Parameter Set for This Model |
---|---|
hidden_layer_sizes | (30,20) |
activation | adam |
solver | adam |
alpha | 0.1 |
max_iter | 400 |
Parameter | Parameter Set for This Model |
---|---|
base_estimator | CART decision tree |
n_estimators | 300 |
learning_rate | 0.6 |
algorithm | SAMME |
random_state | 37 |
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Qu, J.; Song, C.; Bai, J.; Feng, G.; Shi, X.; Ma, J. A Machine-Learning-Based Method for Identifying the Failure Risk State of Fissured Sandstone under Water–Rock Interaction. Sensors 2024, 24, 5752. https://doi.org/10.3390/s24175752
Qu J, Song C, Bai J, Feng G, Shi X, Ma J. A Machine-Learning-Based Method for Identifying the Failure Risk State of Fissured Sandstone under Water–Rock Interaction. Sensors. 2024; 24(17):5752. https://doi.org/10.3390/s24175752
Chicago/Turabian StyleQu, Jinbo, Cheng Song, Jinwen Bai, Guorui Feng, Xudong Shi, and Junbiao Ma. 2024. "A Machine-Learning-Based Method for Identifying the Failure Risk State of Fissured Sandstone under Water–Rock Interaction" Sensors 24, no. 17: 5752. https://doi.org/10.3390/s24175752