Research on evaluation model of rock failure integrity under complex geological conditions in karst area

Ma Jianbo; Wang Zhongqi; Yang En; Liu Menghua

doi:10.3389/feart.2023.1177459

Research on evaluation model of rock failure integrity under complex geological conditions in karst area

Ma Jianbo
, Wang Zhongqi
, Yang En
, Liu Menghua^*

^*Corresponding author for this work

Beijing Institute of Technology

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

Blasting lumpiness prediction is one of the most important research contents in engineering blasting. Although the traditional KUZ-RAM model is widely used, it often overestimates the size of blasting. Therefore, the KUZ-RAM model was updated or corrected in this paper by simplifying the difficult problem of statistical burst fragmentation in LS-DYNA. Based on the theory of area measurement method, the fitting mechanism of machine learning is used to study the lumpiness of simulation results. The updated KUZ-RAM model adds a coefficient of 0.623 to the original equation of average lumpiness x_m. The linear coefficient R² between the predicted results and the field blasting results increases from −1.99 to 0.97, which significantly improves the prediction of blasting lumpiness.

Original language	English
Article number	1177459
Journal	Frontiers in Earth Science
Volume	11
DOIs	https://doi.org/10.3389/feart.2023.1177459
Publication status	Published - 2023
Externally published	Yes

Keywords

KUZ-RAM model
area measurement method
average lumpiness
numerical simulation calculation
step blasting

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10.3389/feart.2023.1177459

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@article{7369c271e9bb4cf89b9d8d8bad14016b,

title = "Research on evaluation model of rock failure integrity under complex geological conditions in karst area",

abstract = "Blasting lumpiness prediction is one of the most important research contents in engineering blasting. Although the traditional KUZ-RAM model is widely used, it often overestimates the size of blasting. Therefore, the KUZ-RAM model was updated or corrected in this paper by simplifying the difficult problem of statistical burst fragmentation in LS-DYNA. Based on the theory of area measurement method, the fitting mechanism of machine learning is used to study the lumpiness of simulation results. The updated KUZ-RAM model adds a coefficient of 0.623 to the original equation of average lumpiness xm. The linear coefficient R2 between the predicted results and the field blasting results increases from −1.99 to 0.97, which significantly improves the prediction of blasting lumpiness.",

keywords = "KUZ-RAM model, area measurement method, average lumpiness, numerical simulation calculation, step blasting",

author = "Ma Jianbo and Wang Zhongqi and Yang En and Liu Menghua",

note = "Publisher Copyright: Copyright {\textcopyright} 2023 Jianbo, Zhongqi, En and Menghua.",

year = "2023",

doi = "10.3389/feart.2023.1177459",

language = "English",

volume = "11",

journal = "Frontiers in Earth Science",

issn = "2296-6463",

publisher = "Frontiers Media SA",

}

TY - JOUR

T1 - Research on evaluation model of rock failure integrity under complex geological conditions in karst area

AU - Jianbo, Ma

AU - Zhongqi, Wang

AU - En, Yang

AU - Menghua, Liu

PY - 2023

Y1 - 2023

N2 - Blasting lumpiness prediction is one of the most important research contents in engineering blasting. Although the traditional KUZ-RAM model is widely used, it often overestimates the size of blasting. Therefore, the KUZ-RAM model was updated or corrected in this paper by simplifying the difficult problem of statistical burst fragmentation in LS-DYNA. Based on the theory of area measurement method, the fitting mechanism of machine learning is used to study the lumpiness of simulation results. The updated KUZ-RAM model adds a coefficient of 0.623 to the original equation of average lumpiness xm. The linear coefficient R2 between the predicted results and the field blasting results increases from −1.99 to 0.97, which significantly improves the prediction of blasting lumpiness.

AB - Blasting lumpiness prediction is one of the most important research contents in engineering blasting. Although the traditional KUZ-RAM model is widely used, it often overestimates the size of blasting. Therefore, the KUZ-RAM model was updated or corrected in this paper by simplifying the difficult problem of statistical burst fragmentation in LS-DYNA. Based on the theory of area measurement method, the fitting mechanism of machine learning is used to study the lumpiness of simulation results. The updated KUZ-RAM model adds a coefficient of 0.623 to the original equation of average lumpiness xm. The linear coefficient R2 between the predicted results and the field blasting results increases from −1.99 to 0.97, which significantly improves the prediction of blasting lumpiness.

KW - KUZ-RAM model

KW - area measurement method

KW - average lumpiness

KW - numerical simulation calculation

KW - step blasting

UR - https://www.scopus.com/pages/publications/85159926265

U2 - 10.3389/feart.2023.1177459

DO - 10.3389/feart.2023.1177459

M3 - Article

AN - SCOPUS:85159926265

SN - 2296-6463

VL - 11

JO - Frontiers in Earth Science

JF - Frontiers in Earth Science

M1 - 1177459

ER -

Research on evaluation model of rock failure integrity under complex geological conditions in karst area

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Keywords

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