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

By Jian Zhou, Yingui Qiu, Shuangli Zhu, Danial Jahed Armaghani, Manoj Khandelwal, E. T. Mohamad. 2020

240 citations recorded by the source corpus.

Abstract 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 (R2) 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 R2 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.

Subfields: Civil and Structural Engineering, Mechanics of Materials, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/6887dfcc1285f18352c7af2a371fe621d6cee21f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. Jian Zhou

In Mechanics of Materials

1. Chuanqi Li
2. Yingui Qiu
3. Manoj Khandelwal
4. Jian Zhou

In Ocean Engineering

1. Chuanqi Li
2. Yingui Qiu
3. Manoj Khandelwal
4. Jian Zhou

Citations in the corpus

• Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibration (2021, methodology): “Additionally, XGBoost [7], CatB [41], Random Forest [5, 68, 71], and gradient boosting regression (GBR) [12, 69, 70] were taken into consideration and applied for comparison of the developed multiple hybrid-XGBoost model.”