基于CEEMDAN-TCN组合模型的围岩质量等级预测评价

Prediction and Evaluation of Safety Grade of Surrounding Rock Using CEEMDAN-TCN Combination Model

为解决TBM在掘进过程中因未知的地质条件导致的机器卡顿、停机及岩体坍塌等问题,以围岩的岩体完整性指数、单轴抗压强度、内摩擦角、黏聚力、变形模量、泊松比、坚固性系数和弹性抗力系数等主要力学参数为依据,利用完全自适应噪声集合经验模态分解(CEEMDAN)和时间卷积神经网络(TCN)结合的方式,遵循“先分解再重构”的原则,提出一种基于CEEMDAN-TCN组合模型的围岩等级预测方法,对隧道掌子面处的围岩等级进行预测评价。结果表明:1)基于CEEMDAN-TCN组合模型的围岩等级预测值与真实值之间的均方误差一般小于0.07,均方根误差一般小于1.67,平均绝对百分比误差一般小于0.45,平均绝对误差一般小于0.14,拟合系数为95.2%;2)CEEMDAN-TCN组合模型具有误差小、拟合效果佳和实用性高等优点,能准确地预测隧道掌子面处的围岩等级,实现围岩类别的智能分类,对实现TBM高效掘进和风险预警有着重要意义。

Jamming and shutdown of tunnel boring machines(TBMs)and rock collapse often occur during tunneling due to unknown geological conditions.Therefore,the main mechanical parameters,such as rock mass integrity index,uniaxial compressive strength,internal friction angle,cohesion,deformation modulus,Poisson′s ratio,robustness coefficient,and elastic resistance coefficient,are considered as references.The fully adaptive noise ensemble empirical mode decomposition(CEEMDAN)and time convolutional neural network(TCN)are used to establish a method for predicting the grade of the surrounding rock according to the principle of"decomposition first and then reconstruction".The results reveal the following:(1)The mean square error between the predicted and actual values of the surrounding rock grade based on the CEEMDAN-TCN combined model is<0.07,the root mean square error is<1.67,the average absolute percentage error is<0.45,and the average absolute error is<0.14,with a fitting coefficient of 95.2%.(2)The CEEMDAN-TCN combination model has the advantages of low error,good fitting effect,and high practicality,accurately predicting the grade of surrounding rock at the tunnel face and achieving intelligent classification of surrounding rock types,which is crucial for efficient TBM tunneling and early risk warning.