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Tunnel rockburst prediction based on LLE-DBSCAN-SMOTE data processing
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Chengqiang FAN, Yuanyou XIA**, Hongwei ZHANG, Jian HUANG
China Safety Science Journal | 2024, 34(12) : 140 - 148
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China Safety Science Journal | 2024, 34(12): 140-148
Safety engineering technology
Tunnel rockburst prediction based on LLE-DBSCAN-SMOTE data processing
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Chengqiang FAN, Yuanyou XIA**, Hongwei ZHANG, Jian HUANG
Affiliations
  • School of Civil Engineering and Architecture,Wuhan University of Technology,Wuhan Hubei 430070,China
Published: 2024-12-28 doi: 10.16265/j.cnki.issn1003-3033.2024.12.1917
Outline
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To address issues of correlation prediction indicators,outliers,and data imbalance in original data in rockburst prediction,a rockburst prediction method based on LLE-DBSCAN-SMOTE for data processing was proposed. Firstly,the maximum tangential stress of surrounding rock σ θ,uniaxial compressive strength of rock σ c,uniaxial tensile strength of rock σ t,elastic strain energy index W e t,brittle coefficient σ c / σ t,stress coefficient σ θ / σ c,and stress concentration value β characterizing the stress gradient of surrounding rock were selected to construct a rockburst prediction indicator system. Secondly,the LLE algorithm was used for data dimensionality reduction to eliminate the cross-correlation effect between indicators,and the DBSCAN algorithm was introduced to remove outliers. Then,the SMOTE technology was introduced for data balancing. Finally,three types of rockburst prediction models were proposed using Decision Tree (DT),Random Forest (RF),and Gradient Boosting Decision Tree (GBDT) algorithms. The prediction accuracy of the data training models before and after processing was compared and analyzed. Moreover,engineering verification was performed through the measurement in the diversion tunnel of Jiangbian Hydropower Station. The results show that the prediction accuracy of the three types of algorithm models which reduce the prediction index from the 7 dimensions of the original data to the 4 dimensions and adopt the graded outlier processing is the highest among the similar models. The rockburst prediction of the Jiangbian Hydropower Station demonstrates that the proposed model significantly improves prediction accuracy compared to similar models using original rockburst data.

local linear embedding (LLE)  /  density-based spatial clustering of applications with noise (DBSCAN)  /  synthetic minority over-sampling technique (SMOTE)  /  data processing  /  rockburst prediction
Chengqiang FAN, Yuanyou XIA, Hongwei ZHANG, Jian HUANG. Tunnel rockburst prediction based on LLE-DBSCAN-SMOTE data processing[J]. China Safety Science Journal, 2024 , 34 (12) : 140 -148 . DOI: 10.16265/j.cnki.issn1003-3033.2024.12.1917
Year 2024 volume 34 Issue 12
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doi: 10.16265/j.cnki.issn1003-3033.2024.12.1917
  • Receive Date:2024-07-14
  • Online Date:2025-07-09
  • Published:2024-12-28
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  • Received:2024-07-14
  • Revised:2024-09-19
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    School of Civil Engineering and Architecture,Wuhan University of Technology,Wuhan Hubei 430070,China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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