Real-time prediction of tunnel face conditions using XGBoost Random Forest algorithm

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.

Deep learning characterization of rock conditions based on tunnel boring machine data

Rock condition perception based on tunnel boring machine(TBM)data is of great importance for not only ensuring tunnel boring safety but also improving construction efficiency.The prediction of TBM boring responses(i.e.,torque and total thrust of the cutterhead)largely determines the reliability of rock condition perception.In this paper,a new architecture of a two-dimensional convolutional neural network(2D-CNN)with a dual-input strategy is proposed to predict the TBM responses.The TBM Lot 3 of the Yinsong project in Jilin province,China,is taken as the case study in this paper.Two types of models that follow different learning strategies are compared:one is defined as the point model,which only learns data of the stable phase,and the other is defined as the line model,which learns data from both the loading and stable boring phases.The line model is further improved by the weighted loss function method.The results indicate that the strategy of learning data from both the loading phase and stable boring phase and increasing the weight of samples from the stable phase is shown to be optimal in predicting TBM boring responses.In terms of learning strategies,the line model can learn the influence of active control parameters on passive response parameters,but the point model cannot.In terms of machine learning algorithms,2D-CNN has the best performance,with R2 values of 0.865 and 0.923 for torque and total thrust,respectively.The proposed line model can overcome the problem that the traditional model failed to learn the influence of control parameters.Such a model can provide a solid base for the timely optimization of the control parameters in TBM boring process.