Data Preprocessing and Machine Learning Modeling for Rockburst Assessment

Jie Li, Helin Fu, Kaixun Hu and Wei Chen ()
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Jie Li: School of Civil Engineering, Central South University, Changsha 410075, China
Helin Fu: School of Civil Engineering, Central South University, Changsha 410075, China
Kaixun Hu: School of Civil Engineering, Central South University, Changsha 410075, China
Wei Chen: School of Civil Engineering, Central South University, Changsha 410075, China

Sustainability, 2023, vol. 15, issue 18, 1-32

Abstract: Rockbursts pose a significant threat to human safety and environmental stability. This paper aims to predict rockburst intensity using a machine learning model. A dataset containing 344 rockburst cases was collected, with eight inducing features as input and four rockburst grades as output. In the preprocessing stage, missing feature values were estimated using a regression imputation strategy. A novel approach, which combines feature selection (FS), t-distributed stochastic neighbor embedding (t-SNE), and Gaussian mixture model (GMM) clustering, was proposed to relabel the dataset. The effectiveness of this approach was compared with common statistical methods, and its underlying principles were analyzed. A voting ensemble strategy was used to build the machine learning model, and optimal hyperparameters were determined using the tree-structured Parzen estimator (TPE), whose efficiency and accuracy were compared with three common optimization algorithms. The best combination model was determined using performance evaluation and subsequently applied to practical rockburst prediction. Finally, feature sensitivity was studied using a relative importance analysis. The results indicate that the FS + t-SNE + GMM approach stands out as the optimum data preprocessing method, significantly improving the prediction accuracy and generalization ability of the model. TPE is the most effective optimization algorithm, characterized simultaneously by both high search capability and efficiency. Moreover, the elastic energy index W et , the maximum circumferential stress of surrounding rock σ θ , and the uniaxial compression strength of rock σ c were identified as relatively important features in the rockburst prediction model.

Keywords: rockburst; data preprocessing; machine learning; hyperparameter optimization; sensitivity analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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