[1] 汪珂. 深埋隧道岩爆预测及防治技术现状综述[J]. 隧道建设,2021,41(2):212-224. (Wang Ke. Overview of state-of-art of rockburst prediction and prevention techniques for deep-buried tunnels[J]. Tunnel Construction, 2021, 41(2): 212-224. (in Chinese))
[2] 李准,苗勇刚,夏志远. 深部矿山巷道岩爆倾向性预测及防治技术研究[J]. 有色金属(矿山部分),2021,73(3):41-47, 60. (Li Zhun, Miao Yonggang, Xia Zhiyuan. Study on rockburst tendency prediction and prevention technology of deep mine roadway[J]. Nonferrous Metals(Mining Section), 2021, 73(3): 41-47, 60. (in Chinese))
[3] 刘晓悦,季红瑜. 基于AdaBoost-BAS-SVM模型的岩爆预测研究[J]. 金属矿山,2021,50(10):28-34. (Liu Xiaoyue, Ji Hongyu. Research on rockburst prediction based on AdaBoost-BAS-SVM model[J]. Metal Mine, 2021, 50(10): 28-34. (in Chinese))
[4] 谷明成,何发亮,陈成宗. 秦岭隧道岩爆的研究[J]. 岩石力学与工程学报,2002,21(9):1324-1329. (Gu Mingcheng, He Faliang, Chen Chengzong. Study on rockburst in Qingling tunnel[J]. Chinese Journal of Rock Mechanics and Engineering, 2002, 21(9): 1324-1329. (in Chinese))
[5] 李春杰,李洪奇. 秦岭隧道岩爆特征与施工处理[J]. 现代隧道技术,1999(1):36-41. (Li Chunjie, Li Hongqi. Rock burst characteristics and treatment methods in Qinling tunnel[J]. Modern Tunnelling Technology, 1999(1): 36-41. (in Chinese))
[6] 张可诚,曾金富,张杰,等. 秦岭隧道掘进机通过岩爆地段的对策[J]. 现代隧道技术,2000(4):34-38. (Zhang Kecheng, Zeng Jinfu, Zhang Jie, et al. Countermeasures for TBM passing through rock-blasting section in Qinling tunnel[J]. Modern Tunnelling Technology, 2000(4): 34-38. (in Chinese))
[7] 靳春玲,党丹丹,贡力,等. IPP-PNN模型在川藏铁路深埋长大隧道岩爆预测中的应用[J]. 铁道科学与工程学报, 2023,20(3): 986-995. (Jin Chunling, Dang Dandan, Gong Li, et al. Application of IPP-PNN model in rockburst prediction occurring deep-buried long tunnel of Sichuan-Tibet railway[J]. Journal of Railway Science and Engineering, 2023,20(3): 986-995. (in Chinese))
[8] 田睿. 基于机器学习的岩爆烈度等级预测模型研究与应用[D]. 包头:内蒙古科技大学,2020. (Tian Rui. Research and application of rockburst intensity classification prediction model based on machine learning algorithms[D]. Baotou: Inner Mongolia University of Science and Technology, 2020. (in Chinese))
[9] Barton N, Lien R, Lunde J. Engineering classification of rock masses for the design of tunnel support[J]. Rock Mechanics Felsmechanik Mécanique des Roches, 1974, 6(4):189-236.
[10] 王元汉,李卧东,李启光,等. 岩爆预测的模糊数学综合评判方法[J]. 岩石力学与工程学报,1998,17(5):493-501. (Wang Yuanhan, Li Wodong, Li Qiguang, et al. Method of fuzzy comprehensive evaluations for rockburst prediction[J]. Chinese Journal of Rock Mechanics and Engineering, 1998, 17(5): 493-501. (in Chinese))
[11] 张乐文,张德永,邱道宏. 基于粗糙集的可拓评判在岩爆预测中的应用[J]. 煤炭学报,2010,35(9):1461-1465. (Zhang Lewen, Zhang Deyong, Qiu Daohong. Application of extension evaluation method in rockburst prediction based on rough set theory[J]. Journal of China Coal Society, 2010, 35(9): 1461-1465. (in Chinese))
[12] 蔡嗣经,张禄华,周文略. 深井硬岩矿山岩爆灾害预测研究[J]. 中国安全生产科学技术,2005,(5):19-22. (Cai Sijing, Zhang Luhua, Zhou Wenlue. Research on prediction of rock burst in deep hard-rock mines[J]. Journal of Safety Science and Technology, 2005, (5): 19-22. (in Chinese))
[13] 陈炳瑞,冯夏庭,肖亚勋,等. 深埋隧洞TBM施工过程围岩损伤演化声发射试验[J]. 岩石力学与工程学报,2010,29(8):1562-1569. (Chen Bingrui, Feng Xiating, Xiao Yaxun, et al. Acoustic emission test on damage evolution of surrounding rock in deep-buried tunnel during TBM excavation[J]. Chinese Journal of Rock Mechanics and Engineering, 2010, 29(8): 1562-1569. (in Chinese))
[14] 马佳骥,马春驰,曾俊,等. 基于LSTM多变种模型的岩爆微震参数预测研究[J]. 地下空间与工程学报,2022,18(5):1481-1494. (Ma Jiaji, Ma Chunchi, Zeng Jun. Research on the prediction of rockburst microseismic parameters based on LSTM multivariate model[J]. Chinese Journal of Underground Space and Engineering, 2022, 18(5): 1481-1494. (in Chinese))
[15] 吴其斌. 微重力方法在岩爆预测中的应用[J]. 地球物理学进展,1993,(3):136-142. (Wu Qibin. Application of microgravity methods in rockburst prediction[J]. Progress in Geophysics, 1993, (3): 136-142. (in Chinese))
[16] 谭以安. 模糊数学综合评判在地下洞室岩爆预测中的应用[A]. 第二次全国岩石力学与工程学术会议论文集[C]. 北京:知识出版社,1989:247-253. (Tan Yi'an. Application of comprehensive assessment using fussy mathematics to rockburst prediction in underground caverns[A]. Proceeding of the 2nd National Congress of Chinese Society for Rock Mechanics and Engineering[C]. Beijing: Knowledge Press, 1989: 247-253. (in Chinese))
[17] 张永习. 可拓综合评判在某水工隧洞岩爆等级评价中的应用[J]. 山东大学学报(工学版),2012,42(2):58-63. (Zhang Yongxi. Application of extensible comprehensive evaluation for rockburst prediction in a hydro-tunnel[J]. Journal of Shandong University(Engineering Science), 2012, 42(2): 58-63. (in Chinese))
[18] 周科平,雷涛,胡建华. 深部金属矿山RS-TOPSIS岩爆预测模型及其应用[J]. 岩石力学与工程学报,2013,32(增2):3705-3711. (Zhou Keping, Lei Tao, Hu Jianhua. RS-TOPSIS model of rockburst prediction in deep metal mines and its application[J]. Chinese Journal of Rock Mechanics and Engineering, 2013, 32(Suppl. 2): 3705-3711. (in Chinese))
[19] 龚剑,胡乃联,崔翔,等. 基于AHP-TOPSIS评判模型的岩爆倾向性预测[J]. 岩石力学与工程学报,2014,33(7):1442-1448. (Gong Jian, Hu Nailian, Cui Xiang, et al. Rockburst tendency prediction based on AHP-TOPSIS evalution model[J]. Chinese Journal of Rock Mechanics and Engineering, 2014, 33(7): 1442-1448. (in Chinese))
[20] 过江,张为星,赵岩. 岩爆预测的多维云模型综合评判方法[J]. 岩石力学与工程学报,2018,37(5):1199-1206. (Guo Jiang, Zhang Weixing, Zhao Yan. A multidimensional cloud model for rockburst prediction[J]. Chinese Journal of Rock Mechanics and Engineering, 2018, 37(5): 1199-1206. (in Chinese))
[21] 李绍红,王少阳,朱建东,等. 基于权重融合和云模型的岩爆倾向性预测研究[J]. 岩土工程学报,2018,40(6):1075-1083. (Li Shaohong, Wang Shaoyang, Zhu Jiandong, et al. Prediction of rock burst tendency based on weighted fusion and improved cloud model[J]. Chinese Journal of Geotechnical Engineering, 2018, 40(6): 1075-1083. (in Chinese))
[22] 董源,裴向军,张引,等. 基于组合赋权-云模型理论的岩爆预测研究[J]. 地下空间与工程学报,2018,14(增1):409-415. (Dong Yuan, Pei Xiangjun, Zhang Yin. Prediction of rock burst-based on combination weighting and cloud model theory[J]. Chinese Journal of Underground Space and Engineering, 2018, 14(Suppl. 1): 409-415. (in Chinese))
[23] 史秀志,周健,董蕾,等. 未确知测度模型在岩爆烈度分级预测中的应用[J]. 岩石力学与工程学报,2010,29(增1):2720-2726. (Shi Xiuzhi, Zhou Jian, Dong Lei, et al. Application of unascertained measurement model to prediction of classification of rockburst intensity[J]. Chinese Journal of Rock Mechanics and Engineering, 2010, 29(Suppl. 1): 2720-2726. (in Chinese))
[24] 王迎超,尚岳全,孙红月,等. 基于熵权-理想点法的岩爆烈度预测模型及其应用[J]. 煤炭学报,2010,35(2):218-221. (Wang Yingchao, Shang Yuequan, Sun Hongyue, et al. Research and application of rockburst intensity prediction model based on entropy coefficient and ideal point method[J]. Journal of China Coal Society, 2010, 35(2): 218-221. (in Chinese))
[25] 徐琛,刘晓丽,王恩志,等. 基于组合权重-理想点法的应变型岩爆五因素预测分级[J]. 岩土工程学报,2017,39(12):2245-2252. (Xu Chen, Liu Xiaoli, Wang Enzhi, et al. Prediction and classification of strain model rockburst based on five-factor criterion and combined weight-ideal point method[J]. Chinese Journal of Geotechnical Engineering, 2017, 39(12): 2245-2252. (in Chinese))
[26] 贾义鹏,吕庆,尚岳全,等. 基于粗糙集-理想点法的岩爆预测[J]. 浙江大学学报(工学版),2014,48(3):498-503. (Jia Yipeng, Lv Qing, Shang Yuequan, et al. Rockburst prediction based on rough set and ideal point method[J]. Journal of Zhejiang University(Engineering Science), 2014, 48(3): 498-503. (in Chinese))
[27] 刘磊磊,张绍和,王晓密. 基于物元矩阵和理想点法的岩爆烈度预测[J]. 地下空间与工程学报,2016,12(1):205-212. (Liu Leilei, Zhang Shaohe, Wang Xiaomi. Prediction of rockburst intensity based on matter-element matrix and ideal point method[J]. Chinese Journal of Underground Space and Engineering, 2016, 12(1): 205-212. (in Chinese))
[28] 何正,李晓红,卢义玉,等. BP神经网络模型在深埋隧道岩爆预测中的应用[J]. 地下空间与工程学报,2008,(3):494-498. (He Zheng, Li Xiaohong, Lu Yiyu, et al. Application of BP neural network to the prediction of rockburst in Tongyu tunneling[J]. Chinese Journal of Underground Space and Engineering, 2008, (3): 494-498. (in Chinese))
[29] 陈海军,郦能惠,聂德新,等. 岩爆预测的人工神经网络模型[J]. 岩土工程学报,2002(2):229-232. (Chen Haijun, Li Nenghui, Nie Dexin, et al. A model for prediction of rockburst by artificial neural network[J]. Chinese Journal of Geotechnical Engineering, 2002(2): 229-232. (in Chinese))
[30] 陈祖云,张桂珍,邬长福,等. 支持向量机在岩爆预测中的应用[J]. 金属矿山,2009(11):100-104. (Chen Zuyun, Zhang Guizhen, Wu Changfu, et al. Application of the support vector machine in prediction of rock burst[J]. Metal Mine, 2009(11): 100-104. (in Chinese))
[31] 李俊宏,姜弘道. 基于支持向量机的岩爆识别模型[J]. 水利学报,2007(增1):667-670. (Li Junhong, Jiang Hongdao. Model of rockburst identification based on SVM[J]. Journal of Hydraulic Engineering, 2007(Supp.1): 667-670. (in Chinese))
[32] 李宁,王李管,贾明涛. 基于粗糙集理论和支持向量机的岩爆预测[J]. 中南大学学报(自然科学版),2017,48(5):1268-1275. (Li Ning, Wang Liguan, Jia Mingtao. Rockburst prediction based on rough set theory and support vector machine[J]. Journal of Central South University(Science and Technology), 2017, 48(5): 1268-1275. (in Chinese))
[33] 汪华斌,卢自立,邱杰汉,等. 基于粒子群算法优化支持向量机的岩爆预测研究[J]. 地下空间与工程学报,2017,13(2):364-369. (Wang Huabin, Lu Zili, Qiu Jiehan. Prediction of rock burst by improved particle swam optimization based support vector machine[J]. Chinese Journal of Underground Space and Engineering, 2017, 13(2): 364-369. (in Chinese))
[34] 温廷新,陈晓宇. 基于组合赋权的混合粒子群优化支持向量机的岩爆倾向性预测[J]. 安全与环境学报,2018,18(2):440-445. (Wen Tingxin, Chen Xiaoyu. Forecast research on the rock burst liability based on the comprehensive evaluation H-PSO-SVM model[J]. Journal of Safety and Environment, 2018, 18(2): 440-445. (in Chinese))
[35] 贾义鹏,吕庆,尚岳全. 基于粒子群算法和广义回归神经网络的岩爆预测[J]. 岩石力学与工程学报,2013,32(2):343-348. (Jia Yipeng, Lv Qing, Shang Yuequan. Rockburst prediction using particle swarm optimization algorithm and general regression neural network[J]. Chinese Journal of Rock Mechanics and Engineering, 2013, 32(2): 343-348. (in Chinese))
[36] 杨悦增,邓红卫,虞松涛. 基于随机森林模型的岩爆等级预测研究[J]. 矿冶工程,2017,37(4):23-27. (Yang Yuezeng, Deng Hongwei, Yu Songtao. Prediction of rockburst classification based on random forest[J]. Mining and Metallurgical Engineering, 2017, 37(4): 23-27. (in Chinese))
[37] Dong L J, Li X B, Peng K. Prediction of rockburst classification using random forest[J]. Transactions of Nonferrous Metals Society of China, 2013, 23(2):15-21.
[38] Ma J J, Ma C C, Jun Z, et al. Research on prediction of rockburst microseismic parameters based on CNN-LSTM hybrid model[J]. IOP Conference Series: Earth and Environmental Science, 2021, 861(2):21-31.
[39] Adoko A C, Gokceoglu C, Wu L, et al. Knowledge-based and data-driven fuzzy modeling for rockburst prediction[J]. International Journal of Rock Mechanics and Mining Sciences, 2013, 61: 86-95.
[40] Roohollah S F, Abbas T. Long-term prediction of rockburst hazard in deep underground openings using three robust data mining techniques[J]. Engineering with Computers, 2019, 35: 659-675.
[41] 曲宏略,张新尚,张勇,等. 基于倾向性指标的深埋隧道岩爆预测研究[J]. 地下空间与工程学报,2021,17(增2):949-955. (Qu Honglue, Zhang Xinshang, Zhang Yong. Prediction of rock burst in deep-buried tunnel based on tendency index[J]. Chinese Journal of Underground Space and Engineering, 2021, 17(Supp.2): 949-955. (in Chinese))
[42] 陈则黄,李克钢,李明亮,等. 基于PCA-SOFM模型的岩爆烈度等级预测[J]. 地下空间与工程学报,2022,18(增2):934-942, 951. (Chen Zehuang, Li Kegang, Li Mingliang. Prediction of rockburst intensity based on PCA-SOFM model[J]. Chinese Journal of Underground Space and Engineering, 2022, 18(Supp.2): 934-942, 951. (in Chinese))
[43] 付自国,李化,邓建辉,等. 自组织特征映射神经网络在岩爆分级预测中的应用[J].地下空间与工程学报,2023,19(1):334-342. (Fu Ziguo, Li Hua, Deng Jianhui. Application of self-organizing feature map in rockburst classification[J]. Chinese Journal of Underground Space and Engineering, 2023, 19(1): 334-342. (in Chinese))
[44] 钱建南,丁颖,李玉杨,等. 14个水稻品种产量与农艺性状的灰色关联度分析[J]. 大麦与谷类科学,2022,39(5):37-41. (Qian Jiannan, Ding Ying, Li Yuyang, et al. Grey correlation analysis of the yield and agronomic characters of 14 rice varieties[J]. Barley and Cereal Sciences, 2022, 39(5): 37-41. (in Chinese))
[45] 张钧博. 拉萨至林芝铁路隧道岩爆烈度分级与应力强度判据标准研究[D]. 成都:西南交通大学,2021. (Zhang Junbo. Research on classification of rockburst intensity and criterion of stress intensity for LaSa to LinZhi railway tunnel[D]. Chengdu: Southwest Jiaotong University, 2021. (in Chinese))
[46] 宫凤强,李夕兵. 岩爆发生和烈度分级预测的距离判别方法及应用[J]. 岩石力学与工程学报,2007(5):1012-1018. (Gong Fengqiang, Li Xibing. A distance discriminant analysis method for prediction of possibility and classification of rockburst and its application[J]. Chinese Journal of Rock Mechanics and Engineering, 2007(5): 1012-1018. (in Chinese))
[47] Chung J Y, Çaglar G, Kyunghyun C, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling[J]. CoRR, 2014, abs/1412.3555.
[48] 王娇. 基于改进降维聚类-分解策略LSTM-GRU组合短期负荷预测[D]. 西安:西安理工大学,2021. (Wang Jiao. LSTM-GRU combined short-term load forecasting based on improved dimensionality reduction clustering-decomposition Strategy[D]. Xi'an: Xi'an University of Technology, 2021. (in Chinese))
[49] 王志建,张炜健,刘士杰. 基于EMD-GRU循环神经网络的转向交通流量组合预测[J]. 工业控制计算机,2020,33(12):73-76. (Wang Zhijian, Zhang Weijian, Liu Shijie. Combined forecasting of turning traffic flow based on EMD-GRU recurrent neural network[J]. Industrial control computer, 2020, 33(12): 73-76. (in Chinese))
[50] 朱伟,孙运全,钱尧,等. 基于CEEMD-GRU模型的短期电力负荷预测方法[J]. 电测与仪表, 2023, 60(1): 1-8. (Zhu Wei, Sun Yunquan, Qian Yao, et al. Short-term load forecasting method based on complementary ensemble empirical mode decomposition and gated recurrent unit neural network[J]. Electrical Measurement & Instrumentation, 2023, 60(1): 1-8. (in Chinese))
[51] 赵兵,王增平,纪维佳,等. 基于注意力机制的CNN-GRU短期电力负荷预测方法[J]. 电网技术,2019,43(12):4370-4376. (Zhao Bing, Wang Zengping, Ji Weijia, et al. A short-term power load forecasting method based on attention mechanism of CNN-GRU[J]. Power System Technology, 2019, 43(12): 4370-4376. (in Chinese))
[52] 王康,张智晟,撖奥洋,等. 基于Bagging的双向GRU集成神经网络短期负荷预测[J]. 电力系统及其自动化学报,2021,33(10):24-30. (Wang Kang, Zhang Zhisheng, Han Aoyang, et al. Short-term load forecasting based on bidirectional GRU neural network integrated by Bagging algorithm[J]. Proceedings of the CSU-EPSA, 2021, 33(10): 24-30. (in Chinese))
[53] 于春浩,李新利,李军,等. 基于Copula联合AT-GRU网络的火电机组实发功率辨识[J]. 华北电力大学学报(自然科学版), 2022, 49(6): 1-9. (Yu Chunhao, Li Xinli, Li Jun, et al. Identification of real power of thermal power units based on Copula and AT-GRU network[J]. Journal of North China Electric Power University(Natural Science Edition), 2022, 49(6): 1-9. (in Chinese))
[54] 邱道宏,李术才,张乐文,等. 基于模型可靠性检查的QGA-SVM岩爆倾向性分类研究[J]. 应用基础与工程科学学报,2015,23(5):981-991. (Qiu Daohong, Li Shucai, Zhang Lewen, et al. Research on QGA-SVM rock burst orientation classification based on model reliability examination[J]. Journal of Basic Science and Engineering, 2015, 23(5): 981-991. (in Chinese))
[55] Esteve M, Aparicio J, Rodriguezsala J, et al. Random Forests and the measurement of super-efficiency in the context of Free Disposal Hull[J]. European Journal of Operational Research, 2023(2):32-39.
[56] 刘剑,周宗红. 基于修正散点图矩阵与随机森林的岩爆等级预测[J]. 有色金属工程,2022,12(2):120-128. (Liu Jian, Zhou Zonghong. Rockburst grade prediction based on modified scatter graph matrix and random forest[J]. Nonferrous Metals Engineering, 2022, 12(2): 120-128. (in Chinese))
[57] 宫凤强,李夕兵,张伟. 基于Bayes判别分析方法的地下工程岩爆发生及烈度分级预测[J]. 岩土力学,2010,31(增1):370-377, 387. (Gong F Q, Li X B, Zhang W. Rockburst prediction of underground engineering based on Bayes discriminant anlysis method[J]. Rock and Soil Mechanics, 2010, 31(Supp.1): 370-377, 387. (in Chinese))
[58] 裴启涛,李海波,刘亚群,等. 基于改进的灰评估模型在岩爆中的预测研究[J]. 岩石力学与工程学报,2013,32(10):2088-2093. (Pei Qitao, Li Haibo, Liu Yaqun, et al. Rockburst prediction based on a modified grey evaluation model[J]. Chinese Journal of Rock Mechanics and Engineering, 2013, 32(10): 2088-2093. (in Chinese))
[59] 王迎超,靖洪文,张强,等. 基于正态云模型的深埋地下工程岩爆烈度分级预测研究[J]. 岩土力学,2015,36(4):1189-1194. (Wang Yingchao, Jing Hongwen, Zhang Qiang, et al. A normal cloud model-based study of grading prediction of rockburst intensity in deep underground engineering[J]. Rock and Soil Mechanics, 2015, 36(4): 1189-1194. (in Chinese))
[60] 谢学斌,李德玄,孔令燕,等. 基于CRITIC-XGB算法的岩爆倾向等级预测模型[J]. 岩石力学与工程学报,2020,39(10):1975-1982. (Xie Xuebin, Li Dexuan, Kong Lingyan, et al. Rockburst propensity prediction model based on CRITIC-XGB algorithm[J]. Chinese Journal of Rock Mechanics and Engineering, 2020, 39(10): 1975-1982. (in Chinese))
[61] 赵洪波. 岩爆分类的支持向量机方法[J]. 岩土力学,2005(4):642-644. (Zhao Hongbo. Classification of rockburst using support vector machine[J]. Rock and Soil Mechanics, 2005(4): 642-644. (in Chinese))
[62] Xue Y G, Li Z Q, Li S C, et al. Prediction of rock burst in underground caverns based on rough set and extensible comprehensive evaluation[J]. Bulletin of engineering geology and the environment, 2019, 78(1): 417-429.
[63] 杨金林,李夕兵,周子龙,等. 基于粗糙集理论的岩爆预测模糊综合评价[J]. 金属矿山,2010(6):26-29. (Yang Jinlin, Li Xibing, Zhou Zilong, et al. A fuzzy assessment method of rockburst prediction based on rough set theory[J]. Metal Mine, 2010(6): 26-29. (in Chinese))
