Current status and prospects of research on landslide disasters in mine slopes based on multi-source information fusion

LI Hui; HAN Xiaofei; ZHU Wancheng; SONG Qingwei; ZHOU Wenlong

doi:10.13272/j.issn.1671-251x.2024040064

Volume 50 Issue 6

Jun. 2024

Turn off MathJax

Article Contents

Article Navigation > Journal of Mine Automation > 2024 > 50(6): 6-15

LI Hui, HAN Xiaofei, ZHU Wancheng, et al. Current status and prospects of research on landslide disasters in mine slopes based on multi-source information fusion[J]. Journal of Mine Automation，2024，50（6）：6-15. doi: 10.13272/j.issn.1671-251x.2024040064

Citation:

PDF( 681 KB)

Current status and prospects of research on landslide disasters in mine slopes based on multi-source information fusion

doi: 10.13272/j.issn.1671-251x.2024040064

Center for Rock Instability and Seismicity Research, Northeastern University, Shenyang 110819, China

Received Date: 2024-04-19
Rev Recd Date: 2024-06-25

Available Online: 2024-07-04

Abstract

Abstract

In order to overcome the problem that a single information source cannot accurately characterize the evolution features of mining landslide disasters, based on multi-source information fusion technology, this paper summarizes the research progress of mine slope landslide disasters from three aspects: multi-source information acquisition of mine slopes, multi-source information fusion of mine slopes, and mine slope displacement prediction and landslide risk assessment. The study summarizes typical slope monitoring methods of "sky", "air", and "ground" , as well as integrated collaborative monitoring method of "sky-air-ground". The study sorts out the slope multi-source information fusion process that includes data level, feature level, and decision level fusion. The paper organizes the fusion forms of displacement and stress, displacement and hydrological and meteorological monitoring information, as well as other different types. This paper elaborates on the current research status of slope displacement prediction and landslide risk assessment based on multi-source information fusion. The accuracy of disaster analysis in current research on mine slope landslide disasters heavily depends on the quality of monitoring data and insufficient utilization of knowledge of rock mechanics mechanisms. Based on the above problems, the development trends of research on landslide disasters in mine slopes are pointed out. The multi-source data collection and access standards are unified. The method for analyzing landslide disasters in mine slopes is developed by integrating monitoring data with rock mechanics mechanisms. The spatiotemporal association mining algorithm for multi-source information from the "sky-air-ground" is optimized. The construction of a mine slope landslide disaster warning platform based on multi-source information fusion is strengthened.
- mine slope,
- slope displacement prediction,
- landslide risk assessment,
- multi source information fusion,
- collaborative monitoring

FullText(HTML)

References(63)

References

[1]	李红毅,李永华,高会有. 白云鄂博矿区凸形边坡多因素影响下滑坡破坏模式研究[J]. 中国安全生产科学技术,2023,19(增刊1):98-103. LI Hongyi,LI Yonghua,GAO Huiyou. Study on landslide failure mode under the influence of multiple factors on convex slopes in Baiyunebo mining area[J]. Journal of Safety Science and Technology,2023,19(S1):98-103.
[2]	李树建,张义平. 晋宁磷矿采场边坡灾害及其防治研究[J]. 化工矿物与加工,2014,43(8):39-42. LI Shujian,ZHANG Yiping. Hazards of pit slope in Jinning Phosphate Mine and its prevention[J]. Industrial Minerals & Processing,2014,43(8):39-42.
[3]	冯巩,夏元友,王智德,等. 基于位移信息融合的露天矿边坡动态预警方法[J]. 中国安全科学学报,2022,32(3):116-122. FENG Gong,XIA Yuanyou,WANG Zhide,et al. Dynamic early warning method of open-pit mine slopes based on integrated displacement information[J]. China Safety Science Journal,2022,32(3):116-122.
[4]	曹兰柱,孙成亮,王东,等. FLAC^3D的露天矿边坡变形破坏数值模拟分析[J]. 辽宁工程技术大学学报(自然科学版),2016,35(7):679-682. CAO Lanzhu,SUN Chengliang,WANG Dong,et al. Simulation analysis of damaged degeneration of open-pit mine slope based on FLAC^3D value[J]. Journal of Liaoning Technical University (Natural Science),2016,35(7):679-682.
[5]	宋卫东,杜建华,杨幸才,等. 深凹露天转地下开采高陡边坡变形与破坏规律[J]. 北京科技大学学报,2010,32(2):145-151. SONG Weidong,DU Jianhua,YANG Xingcai,et al. Deformation and failure of a high steep slope due to transformation from deep open-pit to underground mining[J]. Journal of University of Science and Technology Beijing,2010,32(2):145-151.
[6]	郭子钰,刘向峰,王来贵,等. 抚顺西露天矿开挖作用下边坡变形破坏滑动规律研究[J]. 金属矿山,2021(12):190-199. GUO Ziyu,LIU Xiangfeng,WANG Laigui,et al. Study on the slope deformation and failure sliding law under the excavation of Fushun west open-pit mine[J]. Metal Mine,2021(12):190-199.
[7]	邓李政,袁宏永,张鸣之,等. 滑坡变形监测预警技术研究进展[J]. 清华大学学报(自然科学版),2023,63(6):849-864. DENG Lizheng,YUAN Hongyong,ZHANG Mingzhi,et al. Research progress on landslide deformation monitoring and early warning technology[J]. Journal of Tsinghua University (Science and Technology),2023,63(6):849-864.
[8]	叶江,李才艺,高红旗,等. 基于多时相光学遥感影像亚像素相关的边坡监测——以溪洛渡电站为例[J]. 测绘通报,2024(1):38-43,108. YE Jiang,LI Caiyi,GAO Hongqi,et al. Sub-pixel correlation-based slope monitoring using multi-temporal optical remote sensing images:a case study of Xiluodu hydropower station[J]. Bulletin of Surveying and Mapping,2024(1):38-43,108.
[9]	李振洪,宋闯,余琛,等. 卫星雷达遥感在滑坡灾害探测和监测中的应用:挑战与对策[J]. 武汉大学学报(信息科学版),2019,44(7):967-979. LI Zhenhong,SONG Chuang,YU Chen,et al. Application of satellite radar remote sensing to landslide detection and monitoring:challenges and solutions[J]. Geomatics and Information Science of Wuhan University,2019,44(7):967-979.
[10]	李如仁,葛永权,李梦晨,等. 基于InSAR−COMSOL的露天矿边坡稳定性分析及形变预测[J]. 金属矿山,2024(3):172-182. LI Ruren,GE Yongquan,LI Mengchen,et al. Stability analysis and deformation prediction of open-pit mine slopes based on InSAR and COMSOL[J]. Metal Mine,2024(3):172-182.
[11]	万忠明,王亚文,范子义. 无人机倾斜摄影技术在边坡监测中的应用[J]. 测绘通报,2022(6):170-172. WAN Zhongming,WANG Yawen,FAN Ziyi. Application of UAV tilt photography technology in slope monitoring[J]. Bulletin of Surveying and Mapping,2022(6):170-172.
[12]	孙威,朱成峰,张玉婷. 基于GNSS的露天采场边坡监测系统构建[J]. 现代矿业,2022,38(6):213-215,219. SUN Wei,ZHU Chengfeng,ZHANG Yuting. Construction of open-pit stope slope monitoring system based on GNSS[J]. Modern Mining,2022,38(6):213-215,219.
[13]	钟小宇,衣瑛,亢建民,等. 北斗GNSS技术在露天采场边坡监测中的应用[J]. 中国矿山工程,2022,51(1):56-60. ZHONG Xiaoyu,YI Ying,KANG Jianmin,et al. Application of Beidou GNSS technology in open-pit mine slope monitoring[J]. China Mine Engineering,2022,51(1):56-60.
[14]	尹永明,邹江湖,李华汐,等. 南方雨季环境下地基合成孔径雷达在露天矿山边坡监测中的应用[J]. 中国安全生产科学技术,2023,19(增刊1):55-59. YIN Yongming,ZOU Jianghu,LI Huaxi,et al. Application of ground-based synthetic aperture radar for monitoring of open pit mine slopes in southern rainy season environment[J]. Journal of Safety Science and Technology,2023,19(S1):55-59.
[15]	秦宏楠,马海涛,于正兴,等. 地基雷达干涉测量动态高频次数据用于滑坡早期预警方法研究[J/OL]. 武汉大学学报(信息科学版):1-9[2024-04-19]. https://doi.org/10.13203/j.whugis20220152. QIN Hongnan,MA Haitao,YU Zhengxing,et al. Landslide early warning method based on dynamic high frequency data of ground-based radar interferometry[J/OL]. Geomatics and Information Science of Wuhan University:1-9[2024-04-19]. https://doi.org/10.13203/j.whugis20220152.
[16]	王德军,万田宝,孙晓东,等. 基于三维激光扫描的植被覆盖边坡监测[J]. 测绘通报,2023(12):112-115. WANG Dejun,WAN Tianbao,SUN Xiaodong,et al. Monitoring of vegetation covered slopes based on 3D laser[J]. Bulletin of Surveying and Mapping,2023(12):112-115.
[17]	张峰,裴华富. 一种用于滑坡位移监测的OFDR测斜仪研发[J]. 中国测试,2023,49(1):119-125. ZHANG Feng,PEI Huafu. Development of an OFDR inclinometer for landslide displacement monitoring[J]. China Measurement & Test,2023,49(1):119-125.
[18]	许强,董秀军,李为乐. 基于天−空−地一体化的重大地质灾害隐患早期识别与监测预警[J]. 武汉大学学报(信息科学版),2019,44(7):957-966. XU Qiang,DONG Xiujun,LI Weile. Integrated space-air-ground early detection,monitoring and warning system for potential catastrophic geohazards[J]. Geomatics and Information Science of Wuhan University,2019,44(7):957-966.
[19]	许强,朱星,李为乐,等. “天−空−地” 协同滑坡监测技术进展[J]. 测绘学报,2022,51(7):1416-1436. XU Qiang,ZHU Xing,LI Weile,et al. Technical progress of space-air-ground collaborative monitoring of landslide[J]. Acta Geodaetica et Cartographica Sinica,2022,51(7):1416-1436.
[20]	许强,董秀军,朱星,等. 基于实景三维的天−空−地−内滑坡协同观测[J]. 工程地质学报,2023,31(3):706-717. XU Qiang,DONG Xiujun,ZHU Xing,et al. Landslide collaborative observation technology based on real scene 3D view from space-air-ground-interior perspective[J]. Journal of Engineering Geology,2023,31(3):706-717.
[21]	张凯,李全生,戴华阳,等. 矿区地表移动“空天地” 一体化监测技术研究[J]. 煤炭科学技术,2020,48(2):207-213. ZHANG Kai,LI Quansheng,DAI Huayang,et al. Research on integrated monitoring technology and practice of "space-sky-ground" on surface movement in mining area[J]. Coal Science and Technology,2020,48(2):207-213.
[22]	程刚,张昊宇,朱鸿鹄,等. 边坡全维度监测技术与模型试验研究[J]. 高校地质学报,2024,30(2):207-217. CHENG Gang,ZHANG Haoyu,ZHU Honghu,et al. Research of full dimension monitoring technology and model test of slopes[J]. Geological Journal of China Universities,2024,30(2):207-217.
[23]	罗勇,唐华伟,管贵平,等. 基于综合监测技术的大型岩堆边坡防治研究[J]. 中外公路,2016,36(6):9-13. LUO Yong,TANG Huawei,GUAN Guiping,et al. Study on slope prevention and control of large-scale rock piles based on comprehensive monitoring technology[J]. Journal of China & Foreign Highway,2016,36(6):9-13.
[24]	郭延辉,杨溢,高才坤,等. 云南鲁甸地震红石岩堰塞湖右岸特高边坡综合监测及变形特征分析[J]. 中国地质灾害与防治学报,2020,31(6):30-37. GUO Yanhui,YANG Yi,GAO Caikun,et al. Comprehensive monitoring and deformation analysis of extra high slope on the right bank of Hongshiyan Dammed Lake in Ludian Earthquake[J]. The Chinese Journal of Geological Hazard and Control,2020,31(6):30-37.
[25]	马旭峰,王家臣,杨占峰,等. 多源信息融合在露天矿滑体监测中的应用[J]. 有色金属(矿山部分),2008,60(3):32-35. MA Xufeng,WANG Jiachen,YANG Zhanfeng,et al. Application of multi-source information fusion in the landslide monitoring of open pit mine[J]. Nonferrous Metals (Mining Section),2008,60(3):32-35.
[26]	PENG M,LI X Y,LI D Q,et al. Slope safety evaluation by integrating multi-source monitoring information[J]. Structural Safety,2014,49:65-74. doi: 10.1016/j.strusafe.2013.08.007
[27]	刘阳,张建经,李孟芳,等. 基于模糊理论与SVM的边坡地震失稳规模贝叶斯网络评估方法[J]. 岩石力学与工程学报,2019,38(增刊1):2807-2815. LIU Yang,ZHANG Jianjing,LI Mengfang,et al. Fuzzy theory- and SVM-based Bayesian network assessment method for slope seismic instability scale[J]. Chinese Journal of Rock Mechanics and Engineering,2019,38(S1):2807-2815.
[28]	王智伟,王利,黄观文,等. 基于BP神经网络的滑坡监测多源异构数据融合算法研究[J]. 地质力学学报,2020,26(4):575-582. WANG Zhiwei,WANG Li,HUANG Guanwen,et al. Research on multi-source heterogeneous data fusion algorithm of landslide monitoring based on BP neural network[J]. Journal of Geomechanics,2020,26(4):575-582.
[29]	王利,许豪,舒宝,等. 利用互信息和IPSO−LSTM进行滑坡监测多源数据融合[J]. 武汉大学学报(信息科学版),2021,46(10):1478-1488. WANG Li,XU Hao,SHU Bao,et al. A multi-source heterogeneous data fusion method for landslide monitoring with mutual information and IPSO-LSTM neural network[J]. Geomatics and Information Science of Wuhan University,2021,46(10):1478-1488.
[30]	郑海青,赵越磊,宗广昌,等. 融合自注意力机制的Conv−LSTM边坡位移预测方法[J]. 金属矿山,2022(11):193-197. ZHENG Haiqing,ZHAO Yuelei,ZONG Guangchang,et al. Prediction of slope displacement based on conv-LSTM combined with self-attention mechanism[J]. Metal Mine,2022(11):193-197.
[31]	LIU Yong,XU Chang,HUANG Biao,et al. Landslide displacement prediction based on multi-source data fusion and sensitivity states[J]. Engineering Geology,2020,271. DOI: 10.1016/j.enggeo.2020.105608.
[32]	LIU Chun,SHAO Xiaohang,LI Weiyue. Multi-sensor observation fusion scheme based on 3D variational assimilation for landslide monitoring[J]. Geomatics,Natural Hazards and Risk,2019,10(1):151-167. doi: 10.1080/19475705.2018.1513871
[33]	鄢好,陈骄锐,李绍红,等. 基于时间序列和GRU的滑坡位移预测[J]. 人民长江,2021,52(1):102-107,133. YAN Hao,CHEN Jiaorui,LI Shaohong,et al. Predicting of landslide displacement based on time series and Gated Recurrent Unit[J]. Yangtze River,2021,52(1):102-107,133.
[34]	ZHANG Jie,WANG Zipeng,HU Jinzheng,et al. Bayesian machine learning-based method for prediction of slope failure time[J]. Journal of Rock Mechanics and Geotechnical Engineering,2022,14(4):1188-1199. doi: 10.1016/j.jrmge.2021.09.010
[35]	刘冠洲,张达,张元生,等. 高陡边坡多源监测预警信息一体化平台研究[J]. 冶金自动化,2018,42(6):1-7. LIU Guanzhou,ZHANG Da,ZHANG Yuansheng,et al. Research on multi-source monitoring and early warning information integration platform for high and steep slopes[J]. Metallurgical Industry Automation,2018,42(6):1-7.
[36]	张凌凡,陈忠辉,周天白,等. 基于梯度提升决策树的露天矿边坡多源信息融合与稳定性预测[J]. 煤炭学报,2020,45(增刊1):173-180. ZHANG Lingfan,CHEN Zhonghui,ZHOU Tianbai,et al. Multi-source information fusion and stability prediction of slope based on gradient boosting decision tree[J]. Journal of China Coal Society,2020,45(S1):173-180.
[37]	刘超云,尹小波,张彬. 基于Kalman滤波数据融合技术的滑坡变形分析与预测[J]. 中国地质灾害与防治学报,2015,26(4):30-35,42. LIU Chaoyun,YIN Xiaobo,ZHANG Bin. Analysis and prediction of landslide deformations based on data fusion technology of Kalman-filter[J]. The Chinese Journal of Geological Hazard and Control,2015,26(4):30-35,42.
[38]	朱自强,吴顺川,刘洋,等. 基于自适应Kalman滤波融合技术的边坡变形分析[J]. 矿业研究与开发,2020,40(1):16-21. ZHU Ziqiang,WU Shunchuan,LIU Yang,et al. Slope deformation analysis based on adaptive Kalman filter fusion technology[J]. Mining Research and Development,2020,40(1):16-21.
[39]	吴泽鑫,张成良,张华超,等. 基于SSA−BP的露天矿山边坡位移变形预测[J]. 有色金属工程,2024,14(6):125-133. WU Zexin,ZHANG Chengliang,ZHANG Huachao,et al. Research on slope displacement prediction of open-pit mine based on SSA-BP[J]. Nonferrous Metals Engineering,2024,14(6):125-133.
[40]	宁永香,崔希民. 矿山边坡地表变形的PSO−ELM预测模型[J]. 煤田地质与勘探,2020,48(6):201-206,216. NING Yongxiang,CUI Ximin. PSO-ELM prediction model for surface deformation of mine slope[J]. Coal Geology & Exploration,2020,48(6):201-206,216.
[41]	JIANG Song,LIU Hongsheng,LIAN Minjie,et al. Rock slope displacement prediction based on multi-source information fusion and SSA-DELM model[J]. Frontiers in Environmental Science,2022,10. DOI: 10.3389/fenvs.2022.982069.
[42]	李胜,韩永亮. 基于MFOA−SVR露天矿边坡变形量预测研究[J]. 中国安全生产科学技术,2015,11(1):11-16. LI Sheng,HAN Yongliang. Research on forecasting of slope deformation in open-pit mine based on MFOA-SVR[J]. Journal of Safety Science and Technology,2015,11(1):11-16.
[43]	张研,范聪,吴哲康,等. 基于PSO−RVM的矿山边坡变形量预测模型[J]. 金属矿山,2022(10):191-196. ZHANG Yan,FAN Cong,WU Zhekang,et al. Forecast model of mine slope deformation based on PSO-RVM[J]. Metal Mine,2022(10):191-196.
[44]	金爱兵,张静辉,孙浩,等. 基于SSA−SVM的边坡失稳智能预测及预警模型[J]. 华中科技大学学报(自然科学版),2022,50(11):142-148. JIN Aibing,ZHANG Jinghui,SUN Hao,et al. Intelligent prediction and alert model of slope instability based on SSA-SVM[J]. Journal of Huazhong University of Science and Technology (Natural Science Edition),2022,50(11):142-148.
[45]	罗亦泳,张立亭,鲁铁定,等. 露天矿边坡变形预测的协同进化多核相关向量机模型[J]. 中国安全科学学报,2016,26(11):110-114. LUO Yiyong,ZHANG Liting,LU Tieding,et al. Forecasting of slope deformation in open-pit mine based on CEPSO-MK-RVM[J]. China Safety Science Journal,2016,26(11):110-114.
[46]	CHEN Bingqian,YU Hao,ZHANG Xiang,et al. Time-varying surface deformation retrieval and prediction in closed mines through integration of SBAS InSAR measurements and LSTM algorithm[J]. Remote Sensing,2022,14(3). DOI: 10.3390/rs14030788.
[47]	HE Yi,YAN Haowen,YANG Wang,et al. Time-series analysis and prediction of surface deformation in the Jinchuan mining area,Gansu Province,by using InSAR and CNN-PhLSTM network[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2022,15:6732-6751. doi: 10.1109/JSTARS.2022.3198728
[48]	姜长三,曾桢,万静. 多源信息融合研究进展综述[J]. 现代计算机,2023,29(18):1-9,29. JIANG Changsan,ZENG Zhen,WAN Jing. A review of research advances in multi-source information fusion[J]. Modern Computer,2023,29(18):1-9,29.
[49]	钟国强,徐润,宋杰,等. 基于多态模糊贝叶斯网络的桩锚支护边坡失稳可能性评价[J]. 公路,2020,65(2):50-55. ZHONG Guoqiang,XU Run,SONG Jie,et al. Possibility evaluation of slope instability of pile-anchor support based on polymorphic fuzzy Bayesian network[J]. Highway,2020,65(2):50-55.
[50]	AHMAD F,TANG Xiaowei,QIU Jiangnan,et al. Prediction of slope stability using Tree Augmented Naive-Bayes classifier:modeling and performance evaluation[J]. Mathematical Biosciences and Engineering,2022,19(5):4526-4546. doi: 10.3934/mbe.2022209
[51]	徐卫亚,胡业凡,吴伟伟,等. 基于云模型和D−S证据理论的多源信息融合滑坡安全性评价[J]. 河海大学学报(自然科学版),2022,50(1):59-66. doi: 10.3876/j.issn.1000-1980.2022.01.009 XU Weiya,HU Yefan,WU Weiwei,et al. Landslide safety evaluation by multi-source information fusion based on cloud model and D-S evidence theory[J]. Journal of Hohai University (Natural Sciences),2022,50(1):59-66. doi: 10.3876/j.issn.1000-1980.2022.01.009
[52]	张化进,吴顺川,李兵磊. 基于改进D-S证据理论选择性集成的边坡稳定性评价[J/OL]. 金属矿山:1-12[2024-04-19]. http://kns.cnki.net/kcms/detail/34.1055.TD.20230818.1631.002.html. ZHANG Huajin,WU Shunchuan,LI Binglei. Slope stability evaluation based on selective ensemble of improved D-S evidence theory[J/OL]. Metal Mine:1-12[2024-04-19]. http://kns.cnki.net/kcms/detail/34.1055.TD.20230818.1631.002.html.
[53]	李哲,刘彤,刘路路,等. 基于证据理论的支挡型黄土高陡边坡稳定性评价[J]. 东南大学学报(自然科学版),2023,53(3):436-444. LI Zhe,LIU Tong,LIU Lulu,et al. Stability evaluation of supported high and steep loess slope based on D-S evidential reasoning[J]. Journal of Southeast University (Natural Science Edition),2023,53(3):436-444.
[54]	李国辉,刘永,招国栋,等. 基于RS-BPNN理论的边坡稳定性预测及应用[J]. 南华大学学报(自然科学版),2015,29(3):122-128. LI Guohui,LIU Yong,ZHAO Guodong,et al. The prediction and application of slope stability based on RS-BPNN[J]. Journal of University of South China (Science and Technology),2015,29(3):122-128.
[55]	ZHANG Duo,FENG Dongmei. Mine geological disaster risk assessment and management based on multisensor information fusion[J]. Mobile Information Systems,2022,2022(1). DOI: 10.1155/2022/1757026.
[56]	LIU Zaobao,SHAO Jianfu,XU Weiya,et al. An extreme learning machine approach for slope stability evaluation and prediction[J]. Natural Hazards,2014,73(2):787-804. doi: 10.1007/s11069-014-1106-7
[57]	杨勇,张忠政,胡军,等. 基于随机权重法改进PSO−ELM的露天矿边坡稳定性分析[J]. 有色金属工程,2022,12(5):128-134. doi: 10.3969/j.issn.2095-1744.2022.05.016 YANG Yong,ZHANG Zhongzheng,HU Jun,et al. Slope stability analysis of open-pit mine based on improved PSO-ELM with random weight method[J]. Nonferrous Metals Engineering,2022,12(5):128-134. doi: 10.3969/j.issn.2095-1744.2022.05.016
[58]	KANG Fei,XU Qing,LI Junjie. Slope reliability analysis using surrogate models via new support vector machines with swarm intelligence[J]. Applied Mathematical Modelling,2016,40(11/12):6105-6120.
[59]	KANG Fei,LI Jingshuang,LI Junjie. System reliability analysis of slopes using least squares support vector machines with particle swarm optimization[J]. Neurocomputing,2016,209:46-56. doi: 10.1016/j.neucom.2015.11.122
[60]	TIEN BUI D,MOAYEDI H,GÖR M,et al. Predicting slope stability failure through machine learning paradigms[J]. ISPRS International Journal of Geo-Information,2019,8(9). DOI: 10.3390/ijgi8090395.
[61]	王健伟,徐玉胜,李俊鑫. 基于网格搜索支持向量机的边坡稳定性系数预测[J]. 铁道建筑,2019,59(5):94-97. doi: 10.3969/j.issn.1003-1995.2019.05.22 WANG Jianwei,XU Yusheng,LI Junxin. Prediction of slope stability coefficient based on grid search support vector machine[J]. Railway Engineering,2019,59(5):94-97. doi: 10.3969/j.issn.1003-1995.2019.05.22
[62]	ZHANG Wengang,LI Hongrui,HAN Liang,et al. Slope stability prediction using ensemble learning techniques:a case study in Yunyang County,Chongqing,China[J]. Journal of Rock Mechanics and Geotechnical Engineering,2022,14(4):1089-1099. doi: 10.1016/j.jrmge.2021.12.011
[63]	QI Chongchong,TANG Xiaolin. Slope stability prediction using integrated metaheuristic and machine learning approaches:a comparative study[J]. Computers & Industrial Engineering,2018,118:112-122.

Relative Articles

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(1)

Get Citation

PDF

XML

Article Metrics

Article views (185) PDF downloads(211)

Current status and prospects of research on landslide disasters in mine slopes based on multi-source information fusion

doi: 10.13272/j.issn.1671-251x.2024040064

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Proportional views

Related

Current status and prospects of research on landslide disasters in mine slopes based on multi-source information fusion

doi: 10.13272/j.issn.1671-251x.2024040064

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Proportional views

Related

Export File

Citation

Format

Content