Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization

Zhou, Jian and Qiu, Yingui and Zhu, Shuangli and Armaghani, Danial Jahed and Khandelwal, Manoj and Mohamad, Edy Tonnizam (2021) Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization. Underground Space, 6 (5). pp. 506-515. ISSN 2096-2754, DOI

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The advance rate (AR) of a tunnel boring machine (TBM) under hard rock conditions is a key parameter in the successful implementation of tunneling engineering. In this study, we improved the accuracy of prediction models by employing a hybrid model of extreme gradient boosting (XGBoost) with Bayesian optimization (BO) to model the TBM AR. To develop the proposed models, 1286 sets of data were collected from the Peng Selangor Raw Water Transfer tunnel project in Malaysia. The database consists of rock mass and intact rock features, including rock mass rating, rock quality designation, weathered zone, uniaxial compressive strength, and Brazilian tensile strength. Machine specifications, including revolution per minute and thrust force, were considered to predict the TBM AR. The accuracies of the predictive models were examined using the root mean squares error (RMSE) and the coefficient of determination (R-2) between the observed and predicted yield by employing a five-fold cross-validation procedure. Results showed that the BO algorithm can capture better hyper-parameters for the XGBoost prediction model than can the default XGBoost model. The robustness and generalization of the BO-XGBoost model yielded prominent results with RMSE and R-2 values of 0.0967 and 0.9806 (for the testing phase), respectively. The results demonstrated the merits of the proposed BO-XGBoost model. In addition, variable importance through mutual information tests was applied to interpret the XGBoost model and demonstrated that machine parameters have the greatest impact as compared to rock mass and material properties.

Item Type: Article
Funders: National Natural Science Foundation of China (NSFC) [41807259], Innovation-Driven Project of Central South University [2020CX040], Shenghua Lieying Program of Central South University, China
Uncontrolled Keywords: TBM performance; Advance rate; XGBoost; Bayesian optimization; Predictive modeling
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering
Depositing User: Ms Zaharah Ramly
Date Deposited: 04 Apr 2022 02:27
Last Modified: 04 Apr 2022 02:27

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