A Data-Driven Based Method for Pipeline Additional Stress Prediction Subject to Landslide Geohazards
Abstract
:1. Introduction
- A multi-parameter integrated monitoring system was developed to monitor the pipeline and landslide conditions in a complex geologic environment.
- The data-driven based predictive model was proposed for additional stress evaluation under landslide movement.
- Field sites were selected for demonstration of the geohazard monitoring system, and the additional stress model was verified based on the on-site data.
2. Pipeline Geohazards Monitoring Implementation
2.1. Selection of Monitoring Sites
2.2. Selection of Monitoring Elements
2.2.1. Pipeline Monitoring
2.2.2. Landslide Deformation Monitoring
2.3. The Implementation of Monitoring System
3. Proposed Methodology
3.1. The Framework of Proposed Method
3.2. Data Preprocessing
3.3. Model Training
3.3.1. Support Vector Regression
3.3.2. Random Forest
3.3.3. Adaptive Boosting
3.3.4. Gradient Boosting Regression Tree
- Weak learner initializationIn Equation (9), N is the number of samples, c is the constant value with the smallest loss function; is the actual target value.
- Iteratively build M boosted treesThe negative gradient for samples is expressed as Equation (10).is the residual, (, ) are used as the training data of the next tree, the corresponding node area of the newly established regression tree is , . The expression of a new learner can be obtained as Equation (11).is the minimum value of the loss function for the m-th tree at the j-th iteration, which is shown in Equation (12). is the characteristic function, c is the constant value with the smallest loss function.
- Final outputThe predictive results are based on the ensemble predictions of the weak learner models as Equation (13).
3.3.5. Extreme Gradient Boosting
3.4. Performance Evaluation
4. Case Study
4.1. Monitoring Sites Description
4.2. Comparative Analysis
4.3. Sensitivity Analysis
5. Conclusions
- The results indicate that the XGBoost model has the highest performance in the prediction of the additional stress, with RMSE of 0.0154 MPa, MAE of 0.0118 MPa, and value of 0.9893.
- The top five factors contributing to the additional stress for the applied dataset are landslide compressive stress, landslide traction stress, landslide surface y-axis displacement, soil pressure, and landslide surface x-axis displacement.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Monitoring Type | Variables | Symbol | Xmin | Xmax | Xmean | Xstd |
---|---|---|---|---|---|---|
Landslide monitoring | Inner x-axis displacement, mm | 0 | 7 | 2.31 | 1.8 | |
Inner y-axis displacement, mm | 0 | 8 | 3.66 | 2.17 | ||
Surface x-axis displacement, mm | 0.05 | 0.25 | 0.16 | 0.03 | ||
Surface y-axis displacement, mm | 0 | 0.25 | 0.16 | 0.03 | ||
Surface z-axis displacement, mm | −0.08 | −0.03 | −0.04 | 0.01 | ||
Anti-slide pile inclination, ° | I | 0 | 1.2 | 0.41 | 0.21 | |
Soil pressure, kPa | P | 100 | 150 | 135.05 | 16.34 | |
Traction stress, kN | 60 | 80 | 72.25 | 4.03 | ||
Compressive stress, kN | 50 | 70 | 61.92 | 3.52 | ||
Pipeline monitoring | Pipe additional stress, MPa | S | 4 | 7 | 5.98 | 0.28 |
Model Type | Data-Driven Models | RMSE | MAE | |
---|---|---|---|---|
Individual | SVR | 0.0910 | 0.0666 | 0.6233 |
Ensemble | RF | 0.0288 | 0.0193 | 0.9623 |
AdaBoost | 0.0217 | 0.0119 | 0.9758 | |
GBRT | 0.0198 | 0.0147 | 0.9821 | |
XGBoost | 0.0154 | 0.0118 | 0.9893 |
Rank (XGBoost) | Monitoring Parameter | Feature Importance (XGBoost) | Rank (RF) | Monitoring Parameter | Feature Importance (RF) |
---|---|---|---|---|---|
1 | Compressive stress | 0.5070 | 1 | Compressive stress | 0.4109 |
2 | Traction stress | 0.2406 | 2 | Traction stress | 0.3867 |
3 | Surface y-axis displacement | 0.0910 | 3 | Surface y-axis displacement | 0.1361 |
4 | Soil pressure | 0.0844 | 4 | Inclination | 0.0266 |
5 | Surface z-axis displacement | 0.0450 | 5 | Soil pressure | 0.0249 |
6 | Inclination | 0.0162 | 6 | Surface z-axis displacement | 0.0081 |
7 | Surface x-axis displacement | 0.0119 | 7 | Surface x-axis displacement | 0.0055 |
8 | Inner x-axis displacement | 0.0039 | 8 | Inner x-axis displacement | 0.0010 |
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Zhang, M.; Ling, J.; Tang, B.; Dong, S.; Zhang, L. A Data-Driven Based Method for Pipeline Additional Stress Prediction Subject to Landslide Geohazards. Sustainability 2022, 14, 11999. https://doi.org/10.3390/su141911999
Zhang M, Ling J, Tang B, Dong S, Zhang L. A Data-Driven Based Method for Pipeline Additional Stress Prediction Subject to Landslide Geohazards. Sustainability. 2022; 14(19):11999. https://doi.org/10.3390/su141911999
Chicago/Turabian StyleZhang, Meng, Jiatong Ling, Buyun Tang, Shaohua Dong, and Laibin Zhang. 2022. "A Data-Driven Based Method for Pipeline Additional Stress Prediction Subject to Landslide Geohazards" Sustainability 14, no. 19: 11999. https://doi.org/10.3390/su141911999