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Discrimination of dump slope stability state based on TrAdaBoost-GBDT model
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Song JIANG1, 2, Tao LI1, Jinyuan LI1, Yanbo LI1, Cunliang ZHANG3, Lijie ZHANG4
China Safety Science Journal | 2024, 34(11) : 89 - 98
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China Safety Science Journal | 2024, 34(11): 89-98
Safety engineering technology
Discrimination of dump slope stability state based on TrAdaBoost-GBDT model
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Song JIANG1, 2, Tao LI1, Jinyuan LI1, Yanbo LI1, Cunliang ZHANG3, Lijie ZHANG4
Affiliations
  • 1 School of Resources Engineering,Xi'an University of Architecture and Technology,Xi'an Shaanxi 710055,China
  • 2 Xi'an Key Laboratory of Perceptive Computing and Decision for Intelligent Industry,Xi'an Shaanxi 710055,China
  • 3 Inner Mongolia Huineng Coal and Electricity Group Co.,Ltd.,Ordos Inner Mongolia 017000,China
  • 4 Tangshan Jidong Cement Co.,Ltd.,Tangshan Hebei 063000,China
Published: 2024-11-28 doi: 10.16265/j.cnki.issn1003-3033.2024.11.0124
Outline
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In view of the difficulties in obtaining instability data of open-pit mine dump and the small amount of sample data,a discrimination model of slope stability state of open-pit mine dump based on migration learning algorithm was proposed. According to the actual geological conditions and rainfall conditions of the dump slope of F open-pit mine in Shaanxi Province,a similar simulation test scheme of slope with different soil-rock mixing ratio was designed under the condition of rainfall. The data of water content,earth pressure and pore water pressure of the slope model were collected and processed. Considering the influence of small sample data set on the classification accuracy of GBDT model,using the idea of transfer learning,the sample weight of source domain data set and target domain data set was iteratively updated by TrAdaBoost algorithm,and the GBDT model was used as the weak learner for data sample training. Finally,according to the classification result of the weak learner,the weighted majority voting method was used to generate a TrAdaBoost-GBDT dump slope stability discrimination model based on transfer learning to improve the discrimination accuracy of small sample data label categories. The results show that the proposed dump slope stability state discrimination model has a better performance in judging the stable state than other algorithm models,and the values of accuracy,precision,recall and area under curve(AUC) are 93.3%,87.5%,100% and 93.8%,respectively. Compared with other algorithm models,this model can improve the accuracy of slope stability discrimination of small sample data sets,and make up for the low accuracy of machine learning classification results of small sample data sets.

dump slope  /  stability state discrimination  /  transfer adaptive boosting-gradient boosting decision tree (TrAdaBoost-GBDT)  /  transfer learning  /  small samples
Song JIANG, Tao LI, Jinyuan LI, Yanbo LI, Cunliang ZHANG, Lijie ZHANG. Discrimination of dump slope stability state based on TrAdaBoost-GBDT model[J]. China Safety Science Journal, 2024 , 34 (11) : 89 -98 . DOI: 10.16265/j.cnki.issn1003-3033.2024.11.0124
Year 2024 volume 34 Issue 11
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doi: 10.16265/j.cnki.issn1003-3033.2024.11.0124
  • Receive Date:2024-06-19
  • Online Date:2025-07-09
  • Published:2024-11-28
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  • Received:2024-06-19
  • Revised:2024-08-26
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Affiliations
    1 School of Resources Engineering,Xi'an University of Architecture and Technology,Xi'an Shaanxi 710055,China
    2 Xi'an Key Laboratory of Perceptive Computing and Decision for Intelligent Industry,Xi'an Shaanxi 710055,China
    3 Inner Mongolia Huineng Coal and Electricity Group Co.,Ltd.,Ordos Inner Mongolia 017000,China
    4 Tangshan Jidong Cement Co.,Ltd.,Tangshan Hebei 063000,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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