基于TBM施工数据进行围岩感知对保障TBM施工安全、提高施工效率至关重要，其中TBM掘进参数预测的准确率是检验围岩感知效果的重要依据.为此，以吉林引松工程TBM四标段为研究对象，选取TBM上升段破岩数据为输入特征X1，选择两个施工控制参数（刀盘转速和推进速度）为输入特征X2，构建卷积神经网络机器学习模型，对TBM掘进响应参数Y（刀盘扭矩和总推力）进行预测.按照学习对象的不同，分别构建了只学习稳定段掘进响应行为的点预测模型和同时学习上升段和稳定段掘进响应行为的线预测模型，结果表明：点预测模型无法描述控制参数对掘进响应参数的影响；线预测模型虽然可以描述控制参数对掘进响应参数的影响，但是对稳定段的掘进响应预测数值偏低.考虑到上述局限性的原因是稳定段行为样本数量只占总样本数量的9%，提出了一种通过调节损失函数的方法来提高稳定段行为样本的权重，显著提高了线预测模型的预测精度.改进后的结果表明：在TBM掘进参数预测中，应对整个掘进段的行为进行学习，并提高稳定段行为的权重，以便获得高精度的掘进响应参数预测模型.获得的模型能够为进一步的围岩感知和控制参数优化提供基础.

Surrounding rock perception based on TBM construction data is essential to ensuring the safety of TBM construction and improving its construction efficiency, in which the accuracy of TBM tunnelling parameter prediction is crucial to testing the effect of surrounding rock perception. Therefore, in this paper it takes Jilin Yin-Song’s project TBM4 bid section as the research object, selects the characteristic parameters of the surrounding rock from the rock breaking data of the loading phase of the TBM as input feature X1, and selects two construction control parameters (the rotation speed and penetration rate) as input feature X2, and constructs a convolutional neural network machine learning model to predict the TBM tunnelling response parameters Y (cutterhead torque and total thrust). According to the different learning objects, the point prediction model that only learns the response behavior of the stable boring phase and the line prediction model that simultaneously learns the response behavior of the loading phase and the stable boring phase are constructed, respectively. The improved results show that the point prediction model cannot describe the influence of control parameters on tunnelling response parameters. Although the line prediction model can describe the influence of control parameters on tunnelling response parameters, the prediction value of driving response in the stable boring phase is low. Considering that the low predictive value of the line prediction model in the stable boring phase is because the number of behavior samples in the stable boring phase only accounts for 9% of the total number of samples, in this paper, a method of adjusting loss function is proposed to improve the weight of behavior samples in the stable boring phase, which significantly improves the prediction accuracy of the line prediction model. The results show that the behavior of the loading boring phase should be studied, and the weight of the behavior of the stable boring phase should be increased to obtain a high-precision prediction model of tunnelling response parameters. The model obtained in this paper can provide a basis for further surrounding rock perception and control parameter optimization.