Investigation on the soil-machine interaction based on slurry shield machine operation data learning: A geological conditions recognition model

Real-time perception of the geological condition is of great importance to efficient tunneling and hazard prevention in underwater shield tunneling. This study proposes a geological condition–shield machine mutual feedback perception method to address the issues of insufficient utilization of excavation data, lack of optimization of models, low prediction accuracy and efficiency in the current research on geological condition identification in soft soil shield tunneling. For implementation, first, the database of the slurry shield machine tunneling parameters containing 6 input features related to operation parameters were established, in which 269 tunneling cycles from a river-crossing shield tunnel in China were accommodated. Then, the outlier detection method is carried out to pre-process the data sample set and remove the outliers. Furthermore, the genetic algorithm is adapted to optimize the K-means clustering algorithm to cluster the geological conditions category. Four categories with better clustering performance were obtained. To obtain the identification model of the geological condition category with the best prediction performance, 75 % of the sample data is used for data learning, and the optimal training parameters of each model are determined through 10-fold cross-validation. The remaining 25 % of the data is used for validating the four classifiers’ performance. The accuracy levels of the proposed models were assessed using four statistical indices, i.e., the Accuracy, F1 score, Precision, and Recall. The testing results revealed that the PSO-ELM algorithm can better characterize and predict the geological conditions in SPB shield tunnelling among all three recognition models. Finally, the synthetic minority oversampling technique (SMOTE) was used to process the database to eliminate the impact of category imbalance on the recognition performance and obtain the best prediction effect. The validation results indicated the four models have improved the overall prediction performance of the minority samples (type-III) by about 0–45 %. Moreover, the Accuracy of (increased by 35–44 %), Recall of (increased by 0–31 %), F1 score of (increased by 25–37 %) and Precision of (increased by 35–44 %), respectively, for testing stages of the PSO-ELM model confirmed that this hybrid model is a powerful and applicable technique addressing problems related to shield tunnelling performance with a high level of accuracy using the proposed investigation flow.