Abstract: In horizontal directional drilling surveys, the lithology of the surrounding rock is generally determined by coring or drilling into rock chips. However, owing to the limitation of coring efficiency during drilling, determining the lithology of the surrounding rock throughout the entire borehole line based on rock chips is a tedious process. Therefore, to improve the efficiency of lithology identification, the authors use machine learning algorithms to identify the surrounding rock lithology using drilling parameters. A case study is conducted on a tunnel in Xinjiang, China, and the drilling speed, corrected bottom pressure of the borehole, mud pressure, and feed flow rate are input features to estimate the surrounding rock lithology based on the analysis of the working principle of horizontal directional drilling. Fortyeight hyperparameters are set for each knearest neighbor(KNN) and the random forest algorithms and the average accuracies of the test set are 83.28% and 93.04%, respectively, with no underfitting or overfitting concerns in the models. Furthermore, the five classification issues are transformed into five binary classification problems; the accuracy, precision, recall, and 〖WT５《TNR#I》〗F〖WT５《TNR》〗1 values of both algorithms are above 90.00%. The area undercurve values in the receiver operating characteristic curves are close to 1. The results show that the KNN and random forest algorithms have good robustness and generalization ability after using Smote oversampling; however, the random forest model is more effective in predicting the rock properties of the surrounding rocks when the evaluation metrics are combined.

Key words:

tunnel, geological survey, horizontal directional drilling, lithology identification, knearest neighbor algorithm, random forest algorithm, machine learning