Comprehensive Hybrid Gaussian algorithm for rock mass stability assessment in complex geological formations: A machine learning approach with dynamic kernel optimization

Abstract

Accurate prediction of tunnel face stability in complex geological formations remains a critical challenge in underground engineering, necessitating innovative computational approaches. This research proposes a Comprehensive Hybrid Gaussian (CHG) algorithm for predicting tunnel face stability in complex rock formations. The algorithm introduces a failure penetration probability index (FPPI) as an intermediate variable that establishes a probabilistic mapping between Tunnel Boring Machine parameters and rock mass stability, overcoming limitations of traditional linear mapping approaches. The CHG algorithm integrates feedforward layer normalization from Transformer architectures to enable dynamic kernel function optimization, resolving the manual hyperparameter tuning constraints in conventional Gaussian processes. A multi-scale regularization framework, from Tikhonov regularization to dropout, provides effective complexity control while maintaining expressive capacity. The posterior process incorporates Chebyshev’s inequality to enhance confidence interval estimation and prediction robustness. Validation across three geological points in the Yinsong Project demonstrates an average prediction deviation of 20.453%, with the CHG algorithm (R² = 0.982) significantly outperforming support vector regression (R² = 0.846) and random forest (R² = 0.923). While slightly underperforming the Transformer model (R² = 0.992) statistically in cross-project validation on the Yinchao data set, the CHG algorithm (R² = 0.869) exhibits superior adaptability to geological uncertainties. The synergy between FPPI and dynamic kernel functions establishes an innovative framework for predicting mechanical behavior in heterogeneous geological conditions, particularly in lithological transition zones, providing a theoretically sound and practically applicable decision support system for tunnel stability assessment.

Graphical abstract

Keywords

machine learning / Bayesian inference / neural network / Gaussian process / tunneling automation

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