Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Rockburst Prediction and Evaluation Model for Hard Rock Engineering Based on Extreme Gradient Boosting Ensemble Learning and SHAP Value
Abstract Rockburst prediction is the basis of rockburst prevention and construction guidance. However, the complexity of the rock burst occurrence mechanism and inducing factors and the suddenness and randomness of rock burst behavior make the accurate prediction of rock bursts very difficult. In this study, the eXtreme Gradient Boosting (XGBoost) algorithm is used to learn and predict the rockburst intensity of a database including 341 rockburst cases worldwide. A procedure for parameter optimization of XGBoost combined with grid search and cross validation methods is proposed. It improves the prediction performance, effectively avoids overfitting and also improves the operation efficiency. The model predicted 7 typical rockburst cases that occurred at Jinping II Hydropower Station, and the results showed that the GC-XGBoost model performs well in predicting rockburst intensity. In addition, compared with typical supervised learning models (SVM and RF), the model showed improved prediction performance. SHapley Additive exPlanations (SHAP, a game theoretic approach) was used to study the importance of feature parameters. The SHAP values showed that Wet and $$\sigma_{{\theta }}$$ are the two most important feature parameters for predicting rockburst intensity.
Rockburst Prediction and Evaluation Model for Hard Rock Engineering Based on Extreme Gradient Boosting Ensemble Learning and SHAP Value
Abstract Rockburst prediction is the basis of rockburst prevention and construction guidance. However, the complexity of the rock burst occurrence mechanism and inducing factors and the suddenness and randomness of rock burst behavior make the accurate prediction of rock bursts very difficult. In this study, the eXtreme Gradient Boosting (XGBoost) algorithm is used to learn and predict the rockburst intensity of a database including 341 rockburst cases worldwide. A procedure for parameter optimization of XGBoost combined with grid search and cross validation methods is proposed. It improves the prediction performance, effectively avoids overfitting and also improves the operation efficiency. The model predicted 7 typical rockburst cases that occurred at Jinping II Hydropower Station, and the results showed that the GC-XGBoost model performs well in predicting rockburst intensity. In addition, compared with typical supervised learning models (SVM and RF), the model showed improved prediction performance. SHapley Additive exPlanations (SHAP, a game theoretic approach) was used to study the importance of feature parameters. The SHAP values showed that Wet and $$\sigma_{{\theta }}$$ are the two most important feature parameters for predicting rockburst intensity.
Rockburst Prediction and Evaluation Model for Hard Rock Engineering Based on Extreme Gradient Boosting Ensemble Learning and SHAP Value
Chen, Long (Autor:in) / Wu, Shunchuan (Autor:in) / Jin, Aibing (Autor:in) / Zhang, Chaojun (Autor:in) / Li, Xue (Autor:in)
2023
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
BKL:
57.00$jBergbau: Allgemeines
/
38.58
Geomechanik
/
57.00
Bergbau: Allgemeines
/
56.20
Ingenieurgeologie, Bodenmechanik
/
38.58$jGeomechanik
/
56.20$jIngenieurgeologie$jBodenmechanik
Extreme Gradient Boosting-Based Machine Learning Approach for Green Building Cost Prediction
| DOAJ | 2022