Real-time perception of rock conditions based on continuously collected data to meet the requirements of continuous Tunnel Boring Machine (TBM) construction presents a critical challenge that warrants increased attention. To achieve this goal, this paper establishes real-time prediction models for fractured and weak rock mass by comparing 6 different algorithms using real-time data collected by the TBM. The models are optimized in terms of selecting metric, selecting input features, and processing imbalanced data. The results demonstrate the following points. (1) The Youden’s index and area under the ROC curve (AUC) are the most appropriate performance metrics, and the XGBoost Random Forest (XGBRF) algorithm exhibits superior prediction and generalization performance. (2) The duration of the TBM loading phase is short, usually within a few minutes after the disc cutter contacts the tunnel face. A model based on the features during the loading phase has a miss rate of 21.8%, indicating that it can meet the early warning needs of TBM construction well. As the TBM continues to operate, the inclusion of features calculated from subsequent data collection can continuously correct the results of the real-time prediction model, ultimately reducing the miss rate to 16.1%. (3) Resampling the imbalanced data set can effectively improve the prediction by the model, while the XGBRF algorithm has certain advantages in dealing with the imbalanced data issue. When the model gives an alarm, the TBM operator and on-site engineer can be reminded and take some necessary measures for avoiding potential tunnel collapse. The real-time predication model can be a useful tool to increase the safety of TBM excavation.

基于连续收集的数据来实时感知岩石状况以满足连续隧道掘进机 (TBM) 施工的要求是一项值得更多关注的严峻挑战。为了实现这一目标，本文利用TBM收集的实时数据，通过比较6种不同的算法，建立了裂隙软弱岩体的实时预测模型。该模型在选择指标、选择输入特征和处理不平衡数据方面进行了优化。结果表明以下几点。(1) Youden指数和ROC曲线下面积( AUC )是最合适的性能指标，XGBoost随机森林(XGBRF)算法表现出优越的预测和泛化性能。(2) TBM加载阶段持续时间短，通常在圆盘滚刀接触掌子面后几分钟内。基于加载阶段特征的模型漏检率为21.8%，能够很好地满足TBM施工的预警需求。随着TBM的持续运行，纳入后续数据采集计算出的特征可以不断修正实时预测模型的结果，最终将失误率降低至16.1%。(3)对不平衡数据集进行重采样可以有效提高模型的预测能力，而XGBRF算法在处理不平衡数据问题上具有一定的优势。当模型发出警报时，可以提醒TBM操作员和现场工程师采取一些必要措施，避免潜在的隧道塌方。实时预测模型可以成为提高TBM挖掘安全性的有用工具。