Application of an Improved Method Combining Machine Learning–Principal Component Analysis for the Fragility Analysis of Cross-Fault Hydraulic Tunnels
Abstract
:1. Introduction
2. A Powerful Dimension-Reduction Method for PCA
3. Machine Learning
3.1. Stepwise Regression
3.2. Support Vector Machine Regression
3.3. Decision Tree Regression
3.4. Random Forest Regression
3.5. Neural Networks Regression
3.6. Gaussian Process Regression
4. Framework for the Fragility Curve Fitting Method Coupling the Predicting Data
5. Pre-Processing of Ground Motions and Integrated IM
6. Numerical Method
7. Analysis Results and Discussion
7.1. Predictive Capability Evaluation of Various ML-PCA Approaches
7.2. Traditional Trend Model and ML-PCA Predicted Model
7.3. Fragility Analysis Utilizing the ML-PCA Model
8. Final Remarks
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Type | Description | Symbol | Definition | Unit | Reference |
---|---|---|---|---|---|
Amplitude | Peak ground acceleration | PGA | g | - | |
Peak ground velocity | PGV | m/s | - | ||
Peak ground displacement | PGD | m | - | ||
Sustained maximum acceleration | SMA | The third-largest absolute peak in the acceleration time history | m/s2 | [38] | |
Sustained maximum velocity | SMV | The third-largest absolute peak in the velocity time history | m/s | [38] | |
Integral | Acceleration root-mean-Square | Arms | g | [39] | |
Velocity RMS | Vrms | m | [40] | ||
Displacement RMS | Drms | m | [40] | ||
Arias intensity | IA | m/s | [41] | ||
Characteristic intensity | IC | - | [28] | ||
Specific energy density | SED | m2/s | [42] | ||
Cumulative absolute velocity | CAV | m/s | [43] | ||
Frequency content | Acceleration spectrum Intensity | ASI | m*s | [44] | |
Velocity spectrum intensity | VSI | m | [44] | ||
Housner intensity | HI | m | [45] |
IMs | Probabilistic Seismic Demand Models | Correlation Index (R) | |
---|---|---|---|
Peak ground acceleration (PGA) | −6.16 + 0.71ln(PGA) | 0.61 | 0.64 |
Principal component analysis (PCA) | −6.61 + 0.24ln(Arms) | 0.42 | 0.85 |
Gaussian process–principal component analysis (GP-PCA) | −6.61 + 0.24ln(Vrms) | 0.39 | 0.87 |
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Xu, Y.; Sun, B.; Deng, M.; Xu, J.; Wang, P. Application of an Improved Method Combining Machine Learning–Principal Component Analysis for the Fragility Analysis of Cross-Fault Hydraulic Tunnels. Buildings 2024, 14, 2608. https://doi.org/10.3390/buildings14092608
Xu Y, Sun B, Deng M, Xu J, Wang P. Application of an Improved Method Combining Machine Learning–Principal Component Analysis for the Fragility Analysis of Cross-Fault Hydraulic Tunnels. Buildings. 2024; 14(9):2608. https://doi.org/10.3390/buildings14092608
Chicago/Turabian StyleXu, Yan, Benbo Sun, Mingjiang Deng, Jia Xu, and Pengxiao Wang. 2024. "Application of an Improved Method Combining Machine Learning–Principal Component Analysis for the Fragility Analysis of Cross-Fault Hydraulic Tunnels" Buildings 14, no. 9: 2608. https://doi.org/10.3390/buildings14092608