A Novel Identification Approach Using RFECV–Optuna–XGBoost for Assessing Surrounding Rock Grade of Tunnel Boring Machine Based on Tunneling Parameters

Author:

Shi Kebin1,Shi Renyi1ORCID,Fu Tao1,Lu Zhipeng1,Zhang Jianming1ORCID

Affiliation:

1. College of Water Resources and Civil Engineering, Xinjiang Agricultural University, Urumqi 830000, China

Abstract

In order to solve the problem of the poor adaptability of the TBM digging process to changes in geological conditions, a new TBM digging model is proposed. An ensemble learning prediction model based on XGBoost, combined with Optuna for hyperparameter optimization, enables the real-time identification of surrounding rock grades. Firstly, an original dataset was established based on the TBM tunneling parameters under different surrounding rock grades based on the KS tunnel. Subsequently, the RF–RFECV was employed for feature selection and six features were selected as the optimal feature subset according to the importance measure of random forest features and used to construct the XGBoost identification model. Furthermore, the Optuna framework was utilized to optimize the hyperparameters of XGBoost and validated by applying the established TBM dataset of the KS Tunnel. In order to verify the applicability and efficiency of the proposed model in surrounding rock grade identification, the prediction results of five commonly used machine learning models, Optuna–XGBoost, Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Decision Tree (DT), XGBoost, and PSO–XGBoost, were compared and analyzed. The main conclusions are as follows: the feature selection method based on RF–RFECV improved the accuracy by 8.26%. Among the optimal feature subset, T was the most essential feature for the model’s input, while PR was the least important. The Optuna–XGBoost model proposed in this paper had higher accuracy (0.9833), precision (0.9803), recall (0.9813), and F1 score (0.9807) than other models and could be used as an effective means for the lithological identification of surrounding rock grade.

Publisher

MDPI AG

Reference65 articles.

1. Xinhua News Agency (2021). The 14th Five-Year Plan for National Economic and Social Development of the People’s Republic of China and the Outline of the long-range goals for 2035. China Water Resour., 6, 1–38. (In Chinese).

2. Development and thinking of tunnels and underground engineering in China in recent 2 years (from 2019 to 2020);Hong;Tunn. Constr.,2021

3. Progresses, challenges and countermeasures for TBM construction technology in China;Du;Tunn. Constr.,2017

4. A study on key construction techniques for tunnel boring machines adopted in super-long tunnels;Liu;Mod. Tunn. Technol.,2005

5. Deere, D.U., Hendron, A.J., Patton, F.D., and Cording, E.J. (1966, January 15–17). Design on surface and near surface construction in rock. Proceedings of the 8th U.S. Symposium on Rock Mechanics (USRMS), Minneapolis, MI, USA.

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