Deformation prediction of open-pit mine slope based on ABC-GRNN combined model

Authors

NING Yongxiang, Department of Earth Science and Engineering, Shanxi Institute of Technology, Yangquan 045000, China; Shanxi Key Laboratory of Mine Ecological Restoration and Solid Waste Recycle, Shanxi Institute of Technology, Yangquan 045000, ChinaFollow
CUI Ximin, College of Geosciences and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
CUI Jianguo, Department of Earth Science and Engineering, Shanxi Institute of Technology, Yangquan 045000, China

Abstract

Accurate prediction of slope deformation in open-pit mine is an important guarantee for effective disaster early warning of slope. The traditional slope deformation prediction method is unable to characterize and comprehensively analyze the effect of various factors on slope deformation. In view of this, the combined prediction model (ABC-GRNN) of an artificial bee colony algorithm (ABC) and the optimized generalized regression neural network (GRNN) was proposed for slope deformation in open-pit mine. In this prediction model, the following 5 factors affecting the slope deformation of open-pit mine are considered comprehensively: mining disturbance, rainfall, rainfall duration, temperature and humidity. Herein, the prediction results were compared and analyzed in combination with the measured deformation data and the artificial intelligence algorithms, such as genetic algorithm improved BP neural network (GA-BPNN) and support vector machine (SVM), based on Pingshuo Anjialing open-pit mine of China National Coal Group Corp.. The research results show that ABC algorithm could quickly help GRNN to optimize the appropriate transfer parameters and effectively predict the deformation. ABC-GRNN combined prediction model reduces the average absolute error from 292.9 mm, the average absolute percentage error of 0.691 3% and the root mean square error of 338.9 mm to 25 mm, 0.043 3% and 29.5 mm, respectively, which shows that the model has higher prediction accuracy. In addition, ABC-GRNN model converges faster than other models, and the minimum mean square error can be obtained through only 7 iterations. Compared with other prediction models, this model has higher prediction accuracy, stronger generalization ability and faster convergence speed, with higher practical value.

Keywords

open-pit mine, slope deformation, bee colony algorithm, generalized regression neural network, prediction model, prediction accuracy

DOI

10.12363/issn.1001-1986.22.08.0608

Recommended Citation

NING Yongxiang, CUI Ximin, CUI Jianguo, et al. (2023) "Deformation prediction of open-pit mine slope based on ABC-GRNN combined model," Coal Geology & Exploration: Vol. 51: Iss. 3, Article 51.
DOI: 10.12363/issn.1001-1986.22.08.0608
Available at: https://cge.researchcommons.org/journal/vol51/iss3/51

Reference

[1] 范立民,马雄德,李永红,等. 西部高强度采煤区矿山地质灾害现状与防控技术[J]. 煤炭学报,2017,42(2):276−285.

FAN Limin,MA Xiongde,LI Yonghong,et al. Geological disasters and control technology in high intensity mining area of Western China[J]. Journal of China Coal Society,2017,42(2):276−285.

[2] 许锐,程辉,李寻昌,等. 施工诱发边坡失稳变形机制及应急治理对策[J]. 煤田地质与勘探,2017,45(3):107−111.

XU Rui,CHENG Hui,LI Xunchang,et al. Deformation mechanism and urgent treatment of unstable slope induced by construction[J]. Coal Geology & Exploration,2017,45(3):107−111.

[3] 芦家欣,汤伏全,赵军仪,等. 黄土矿区开采沉陷与地表损害研究述评[J]. 西安科技大学学报,2019,39(5):859−866.

LU Jiaxin,TANG Fuquan,ZHAO Junyi,et al. Review of study on mining subsidence and ground surface damage in loess mining area[J]. Journal of Xi’an University of Science and Technology,2019,39(5):859−866.

[4] 唐睿旋,晏鄂川,唐薇. 基于粗糙集和BP神经网络的滑坡易发性评价[J]. 煤田地质与勘探,2017,45(6):129−138.

TANG Ruixuan,YAN Echuan,TANG Wei. Landslide susceptibility evaluation based on rough set and back–propagation neural network[J]. Coal Geology & Exploration,2017,45(6):129−138.

[5] 孙世国,苏振华,王杰,等. 滑坡变形组合预测方法的研究[J]. 工程地质学报,2016,24(6):1041−1047.

SUN Shiguo,SU Zhenhua,WANG Jie,et al. Research on the synthetical prediction method of landslip deformation[J]. Journal of Engineering Geology,2016,24(6):1041−1047.

[6] 罗亦泳,姚宜斌,王亚. 基于MRVM的多输出边坡变形预测模型[J]. 大地测量与地球动力学,2017,37(12):1239−1242.

LUO Yiyong,YAO Yibin,WANG Ya. Multi output slope deformation model based on multi−output relevance vector machine[J]. Journal of Geodesy and Geodynamics,2017,37(12):1239−1242.

[7] LI Jinchao,GAO Fei,LU Jiaguo,et al. Deformation monitoring and prediction for residential areas in the Panji mining area based on an InSAR time series analysis and the GM−SVR model[J]. Open Geosciences,2019,11(1):738−749.

[8] 鲁小红. 最优权组合预测法在采煤沉陷变形预测中的应用[J]. 测绘通报,2020(4):111−115.

LU Xiaohong. Application of optimal weight combination method in predicting coal mining subsidence deformation[J]. Bulletin of Surveying and Mapping,2020(4):111−115.

[9] 陈小杰,龚赛博. 基于卡尔曼方法的历史建筑不均匀沉降预测[J]. 桂林理工大学学报,2021,41(2):350−353.

CHEN Xiaojie,GONG Saibo. Uneven settlement prediction of historical buildings based on Kalman filter method[J]. Journal of Guilin University of Technology,2021,41(2):350−353.

[10] 姜刚,康艳霞,杨志强,等. 灰色理论模型在矿区滑坡变形预测中的应用[J]. 煤田地质与勘探,2011,39(3):49−51.

JIANG Gang,KANG Yanxia,YANG Zhiqiang,et al. Application of gray model to landslide deformation forecast in mining areas[J]. Coal Geology & Exploration,2011,39(3):49−51.

[11] 杨雅萍,张文莲,孙晓云. 基于改进蝙蝠算法优化广义回归神经网络的岩质边坡稳定性预测[J]. 科学技术与工程,2021,21(20):8719−8726.

YANG Yaping,ZHANG Wenlian,SUN Xiaoyun. Stability prediction of rock slope based on improved bat algorithm–generalized regression neural network[J]. Science Technology and Engineering,2021,21(20):8719−8726.

[12] 段青达,陈天伟,田立佳,等. 基于改进型ARIMA–GRNN模型的高边坡变形预测[J]. 桂林理工大学学报,2021,41(2):342−349.

DUAN Qingda,CHEN Tianwei,TIAN Lijia,et al. High slope deformation prediction based on improved ARIMA–GRNN model[J]. Journal of Guilin University of Technology,2021,41(2):342−349.

[13] 肖宁,王鑫. 求解SEVM模型的人工蜂群算法[J]. 山东农业大学学报(自然科学版),2021,52(2):316−321.

XIAO Ning,WANG Xin. Artificial bee colony algorithm solving SEVM model[J]. Journal of Shandong Agricultural University (Natural Science Edition),2021,52(2):316−321.

[14] NWACHUKWU A,JEONG H,PYRCZ M,et al. Fast evaluation of well placements in heterogeneous reservoir models using machine learning[J]. Journal of Petroleum Science and Engineering,2018,163:463−475.

[15] MARTINS F F,VASCONCELOS G,MIRANDA T. Prediction of the mechanical properties of granites under tension using DM techniques[J]. Geomechanics and Engineering,2018,15(1):631−643.

[16] LU Peng,YE Lin,SUN Bohao,et al. A new hybrid prediction method of ultra−short−term wind power forecasting based on EEMD−PE and LSSVM optimized by the GSA[J]. Energies,2018,11(4):697.

[17] ANILAN T,UZLU E,KANKAL M,et al. The estimation of flood quantiles in ungauged sites using teaching–learning based optimization and artificial bee colony algorithms[J]. Scientia Iranica,2017,25(2):632−645.

[18] 胡雨菡,包腾飞,朱征,等. 基于IABC–FCM–RVM算法的拱坝变形预测模型[J]. 武汉大学学报(工学版),2020,53(12):1055−1064.

HU Yuhan,BAO Tengfei,ZHU Zheng,et al. IABC–FCM–RVM based prediction model of arch dam deformation[J]. Engineering Journal of Wuhan University,2020,53(12):1055−1064.

[19] SONG Jingge,ROMERO C E,YAO Zheng,et al. Improved artificial bee colony−based optimization of boiler combustion considering NO_X emissions,heat rate and fly ash recycling for on–line applications[J]. Fuel,2016,172:20−28.

[20] 杜振鑫,刘广钟,赵学华. 集成学习人工蜂群算法[J]. 西安电子科技大学学报,2019,46(2):124−131.

DU Zhenxin,LIU Guangzhong,ZHAO Xuehua. Ensemble learning artificial bee colony algorithm[J]. Journal of Xidian University,2019,46(2):124−131.