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<p>Accurately characterising the spatial variability of rock-mass properties is essential for tunnel stability analysis and risk control. However, the absence of physical constraints and the inherent smoothing of traditional interpolation techniques make it challenging to transform high-frequency, one-dimensional measurement-whilst-drilling (MWD) data into three-dimensional heterogeneous numerical models. This study proposes a data- and mechanism-driven intelligent modelling framework for refined characterisation of tunnel rock masses. A multi-stage theoretical inversion scheme based on mechanical specific energy (MSE) and the Hoek–Brown criterion is first developed to derive equivalent Mohr–Coulomb parameters (<i>E</i>, <i>c</i>, <i>φ</i>) from systematically cleaned MWD data. Subsequently, a hybrid spatial interpolation algorithm integrating sequential Gaussian simulation (SGS) with Hoffman correction is formulated. This approach reconstructs high-fidelity heterogeneous parameter fields that preserve the geostatistical structure whilst remaining strictly conditioned on hard drilling data. Validation against field monitoring shows that the proposed intelligent model reduces the average relative deformation prediction error from 18.4% (conventional mean-value model) to 9.3%. The model captures asymmetric mechanical responses and localised stress concentrations induced by geological heterogeneity, thereby mitigating the limitations associated with mean-value assumptions. In addition, sensitivity analysis reveals that excessively small extrapolation ranges fail to cover the excavation-induced disturbance zone, whereas excessively large ranges may introduce non-physical displacement patterns and stress discontinuities. An extrapolation range of twice the tunnel diameter (2.0D) is identified as mechanically reasonable for the study section, as it effectively balances spatial representation and mechanical consistency. This value should therefore be interpreted as a case-conditioned engineering outcome for the present geological and operational setting, rather than as a universally applicable threshold for all tunnelling environments. By linking cycle-scale field sensing with high-resolution numerical analysis, the proposed framework provides a practical basis for heterogeneity-aware tunnel assessment in complex geological environments.</p>

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Intelligent Modelling and Analysis Method for Tunnel Surrounding Rock Mass Based on Rapid Inversion of Drilling Parameters

  • 期刊论文

  • 发表日期：

    2026年8月3日

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  • Ziquan Chen,
  • Xinghong Zou,
  • Fangming Wei,
  • Jian Yan,
  • Bo Wang,
  • Renjie Yao,
  • Chuan He

摘要

Accurately characterising the spatial variability of rock-mass properties is essential for tunnel stability analysis and risk control. However, the absence of physical constraints and the inherent smoothing of traditional interpolation techniques make it challenging to transform high-frequency, one-dimensional measurement-whilst-drilling (MWD) data into three-dimensional heterogeneous numerical models. This study proposes a data- and mechanism-driven intelligent modelling framework for refined characterisation of tunnel rock masses. A multi-stage theoretical inversion scheme based on mechanical specific energy (MSE) and the Hoek–Brown criterion is first developed to derive equivalent Mohr–Coulomb parameters (E, c, φ) from systematically cleaned MWD data. Subsequently, a hybrid spatial interpolation algorithm integrating sequential Gaussian simulation (SGS) with Hoffman correction is formulated. This approach reconstructs high-fidelity heterogeneous parameter fields that preserve the geostatistical structure whilst remaining strictly conditioned on hard drilling data. Validation against field monitoring shows that the proposed intelligent model reduces the average relative deformation prediction error from 18.4% (conventional mean-value model) to 9.3%. The model captures asymmetric mechanical responses and localised stress concentrations induced by geological heterogeneity, thereby mitigating the limitations associated with mean-value assumptions. In addition, sensitivity analysis reveals that excessively small extrapolation ranges fail to cover the excavation-induced disturbance zone, whereas excessively large ranges may introduce non-physical displacement patterns and stress discontinuities. An extrapolation range of twice the tunnel diameter (2.0D) is identified as mechanically reasonable for the study section, as it effectively balances spatial representation and mechanical consistency. This value should therefore be interpreted as a case-conditioned engineering outcome for the present geological and operational setting, rather than as a universally applicable threshold for all tunnelling environments. By linking cycle-scale field sensing with high-resolution numerical analysis, the proposed framework provides a practical basis for heterogeneity-aware tunnel assessment in complex geological environments.