An integrated parameter prediction framework for intelligent TBM excavation in hard rock. (December 2021) - Wang, Xin; Zhu, Hehua; Zhu, Mengqi; Zhang, Lianyang; Ju, J. Woody
Highlights: An integrated parameter prediction framework is developed to assist TBM operation. TBM operational parameters, thrust, cutterhead torque and advance rate are estimated. Four machine learning algorithms, SVR, RF, BP-ANN, GBDT are applied. Random forests-based feature importance technique is implemented to select inputs. TBM working phases extraction is conductive for capturing data characteristics. Abstract: The adjustment of TBM operational parameters with regard to different strata significantly affects the safety, time and cost in tunnel construction. To assist TBM operation, this paper develops an integrated parameter prediction framework for hard rock tunneling based on combined pre-construction geological information and TBM operational data. The method involves three steps: extraction of TBM working phases based on operational data, selection of input feature from geological information and operational data, and development of prediction model using four machine learning algorithms. The proposed framework has been demonstrated and verified by applying it to a water conveyance tunnel project in China. The results show that the proposed framework performs well in predicting three critical TBM operational parameters, thrust, cutterhead torque and net advance rate, with the determination coefficient R 2 all exceeding 0.8. A comparison study proves that the introduced TBM working phase extraction method is conductive for capturing data characteristics and making predictions, because it unveils the complex rock-machine interaction information underlying the operational data.