Prediction of Geological Parameters during Tunneling by Time Series Analysis on In Situ Data

Abstract

A tunnel boring machine (TBM) is a type of heavy load equipment that is widely used in underground tunnel construction. The geological conditions in the tunneling process are decisive factors that directly affect the control of construction equipment. Because TBM tunneling always takes place underground, the acquisition of geological information has become a key issue in this field. This study focused on the internal relationships between the sequential nature of tunnel in situ data and the continuous interaction between equipment and geology and introduced the long short-term memory (LSTM) time series neural network method for processing in situ data. A method for predicting the geological parameters in advance based on TBM real-time state monitoring data is proposed. The proposed method was applied to a tunnel project in China, and the R2 of the prediction results for five geological parameters are all higher than 0.98. The performance of the LSTM was compared with that of an artificial neural network (ANN). The prediction accuracy of the LSTM was significantly higher compared with that of the ANN, and the generalization and robustness of LSTM are also better than those of ANN, which indicates that the proposed LSTM method could extract the sequence properties of the in situ data. The rule of equipment-geology interaction was reflected by increasing the memory structure of the model through the introduction of the “gate” concept, and the accurate prediction of geological parameters during tunneling was realized. Additionally, the influence of time window and distance of prediction on the model is discussed. The proposed method provides a new approach toward obtaining geological information during TBM construction and also provides a certain reference for the effective analysis of the in situ data with sequence properties.

Monitoring and geological advance prediction for tunnel construction: A case study of shangzhuang tunnel

“…Zhang et al [16] successfully developed a program for detecting, characterizing, and predicting geologic conditions based on data from large TBM operations, and their predictive model trained using 20% of the operational data could achieve an average accuracy of 84.4%. Liu et al [17] indicated that the LSTM method can extract the sequence properties of in-situ data for geological parameters, and its prediction accuracy is signi cantly higher than that of Arti cial Neural Network (ANN). Xu et al [18] compared the accuracy of Random Forest (RF), AdaBoost and Support Vector Machine (SVM) for preconstruction geologic prediction of tunnels, and the results showed that the machine learning algorithm is able to e ciently predict the geologic conditions of oversized diameter SPBM tunnels.…”