Abstract
Understanding tunneling-induced ground deformation, particularly squeezing behavior, is crucial for safe and efficient underground construction. This study employs Artificial Neural Networks (ANN) and Multivariate Adaptive Regression Splines (MARS) models to predict tunneling squeezing behavior using various rock classification systems, namely rock quality index (Q), rock mass rating (RMR), and geological strength index (GSI). The objective is to assess model performance, evaluate the influence of classification systems, and conduct sensitivity analyses on key parameters. The investigation reveals that both ANN and MARS models exhibit enhanced accuracy as model complexity increases, up to a critical point where overfitting occurs. Comparing model performance, ANN outperforms MARS, and the most accurate ANN model is identified as ANN50-RMR with an R2 of 0.978. This confirms the ANN’s capability to capture non-linear relationships inherent in tunneling-induced ground deformation. Choosing a rock classification system as an input parameter significantly impacts model accuracy. RMR and GSI classification systems exhibit improved performance over the conventional Q-system. In particular, GSI-based models offer more consistent and accurate predictions, emphasizing GSI’s suitability for modeling tunneling squeezing behavior. Variables’ importance analysis elucidates the dependence of parameter relevance on the chosen classification system. Sensitivity analyses on tunnel depth, diameter, and rock mass deformation modulus reveal logical correlations between these parameters and tunnel squeezing behavior, further validating model predictions. By enhancing our understanding of tunneling-induced ground deformation, these models contribute to safer and more efficient underground construction practices.
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Data availability
The datasets used in the current study are available from the corresponding author.
Abbreviations
- GSI:
-
Geological Strength Index
- Q:
-
Rock tunneling quality index
- RMR:
-
Rock Mass Rating
- RQD:
-
Rock quality designation number
- SRF:
-
Stress reduction factor
- UCS:
-
Uniaxial compressive strength
- NLR:
-
Nonlinear regression
- ANN:
-
Artificial Neural Network
- V :
-
The numerical neuron value
- g :
-
Activation function
- b:
-
The bias
- w :
-
Weight matrix
- x :
-
Input matrix
- X :
-
Number of variables
- MARS:
-
Multivariate Adaptive Regression Splines
- SVM:
-
Support Vector Machines
- RMSE:
-
Root Mean Square Error
- R2 :
-
Coefficient of Determination
- SI:
-
Scatter Index
- VAF:
-
Variance Accounted For
- U95:
-
Uncertainty (95% confidence interval)
- Tstat:
-
T-statistic
- qi :
-
Value of i quarter from the dataset
- Std. Dev.:
-
Standard deviation
- π:
-
Archimedes’ Constant (ratio of the circumference of a circle to its diameter) = 22/7
- ε:
-
Normalized tunnel convergence or squeezing strain
- H:
-
Tunnel depth
- D:
-
Tunnel diameter
- K:
-
Tunnel support stiffness
- NEA:
-
Nepal Electric Authority
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Al-Sadoon, Z.A., Alotaibi, E., Omar, M. et al. AI-Driven Prediction of Tunneling Squeezing: Comparing Rock Classification Systems. Geotech Geol Eng 42, 2127–2149 (2024). https://doi.org/10.1007/s10706-023-02665-5
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DOI: https://doi.org/10.1007/s10706-023-02665-5