A rockburst proneness evaluation method based on multidimensional cloud model improved by control variable method and rockburst database

• Published in: Lithosphere Vol. 2021; no. Special 4
• Main Authors: Wang Jiong, Wang Jiong, Liu Peng, Liu Peng, Ma Lei, Ma Lei, He Manchao, He Manchao
• Format: Journal Article
• Language: English
• Published: GeoScienceWorld 2022
• Subjects: brittle deformation; cloud-based methods; compressive strength; CRITIC method; data bases; data processing; deformation; Engineering geology; geologic hazards; intensity; Jiangbian Hydropower Station; multidimensional models; natural hazards; numerical models; one-dimensional models; risk assessment; rock bursts; two-dimensional models
• ISSN: 1941-8264; 1947-4253
• DOI: 10.2113/2022/5354402

Rockbursts are common geological disasters in underground engineering, and rockburst proneness evaluation is an important research subject. In this study, a multidimensional cloud model was used to evaluate the rockburst proneness level, and the control variable method was used to establish 15 multidimensional cloud (MC) models. The key factors affecting the accuracy of multidimensional cloud model evaluation are numerical characteristics, weight, and normalization methods. The optimal numerical characteristics calculation method of a multidimensional cloud model was determined, and an improved CRITIC (IC) weight method was optimised by introducing the relative standard deviation and an improved quantisation coefficient. Six rockburst indexes were used as input for the multidimensional cloud model, including the elastic deformation energy index Wet, maximum tangential stress σθMPa of a cavern, uniaxial compressive strength σcMPa, uniaxial tensile strength στMPa, strength brittleness coefficient B1=σc/στ, and stress coefficient σθ/σc. The model was used to learn 271 groups of complete rockburst cases, and the MC-IC rockburst proneness evaluation method was established. The performance of the proposed MC-IC rockburst proneness method was verified by an 8-fold cross-validation and confusion matrix (precision, recall, F1). The method was tested to evaluate 20 groups of rockburst cases from the Jiangbian Hydropower Station, and the accuracy reaches 95%. The evaluation results were compared with three empirical criteria: four cloud-based methods, three unsupervised learning methods, and four supervised learning methods; the accuracy of the method established in this paper is 93.33%. The results showed that the MC-IC method had an excellent performance in evaluating rockburst proneness and can provide a practical basis for identifying rockburst hazard areas in deep engineering.