Prediction of rockhead using a hybrid N-XGBoost machine learning framework

The spatial information of rockhead is crucial for the design and construction of tunneling or underground excavation.Although the conventional site investigation methods(i.e.borehole drilling) could provide local engineering geological information,the accurate prediction of the rockhead position with limited borehole data is still challenging due to its spatial variation and great uncertainties involved.With the development of computer science,machine learning(ML) has been proved to be a promising way to avoid subjective judgments by human beings and to establish complex relationships with mega data automatically.However,few studies have been reported on the adoption of ML models for the prediction of the rockhead position.In this paper,we proposed a robust probabilistic ML model for predicting the rockhead distribution using the spatial geographic information.The framework of the natural gradient boosting(NGBoost) algorithm combined with the extreme gradient boosting(XGBoost)is used as the basic learner.The XGBoost model was also compared with some other ML models such as the gradient boosting regression tree(GBRT),the light gradient boosting machine(LightGBM),the multivariate linear regression(MLR),the artificial neural network(ANN),and the support vector machine(SVM).The results demonstrate that the XGBoost algorithm,the core algorithm of the probabilistic NXGBoost model,outperformed the other conventional ML models with a coefficient of determination(R2)of 0.89 and a root mean squared error(RMSE) of 5.8 m for the prediction of rockhead position based on limited borehole data.The probabilistic N-XGBoost model not only achieved a higher prediction accuracy,but also provided a predictive estimation of the uncertainty.Thus,the proposed N-XGBoost probabilistic model has the potential to be used as a reliable and effective ML algorithm for the prediction of rockhead position in rock and geotechnical engineering.

Prediction of interfaces of geological formations using the multivariate adaptive regression spline method

The design and construction of underground structures are significantly affected by the distribution of geological formations.Prediction of the geological interfaces using limited data has been a difficult task.A multivariate adaptive regression spline(MARS)method capable of modeling nonlinearities automatically was used in this study to spatially predict the elevations of geological interfaces.Borehole data from two sites in Singapore were used to evaluate the capability of the MARS method for predicting geological interfaces.By comparing the predicted values with the borehole data,it is shown that the MARS method has a mean of root mean square error of 4.4 m for the predicted elevations of the Kallang Formation–Old Alluvium interface.In addition,the MARS method is able to produce reasonable prediction intervals in the sense that the percentage of testing data covered by 95% prediction intervals was close to the associated confidence level,95%.More importantly,the prediction interval evaluated by the MARS method had a non-constant width that appropriately reflected the data density and geological complexity.