[1] LUIS R E S, TIAGO M, RITA L E S, et al. The use of data mining techniques in rockburst risk assessment[J]. Engineering, 2017, 3(4): 552-558. [2] 夏元友, 刘昌昊, 刘夕奇,等. 均布与梯度应力加载路径下岩爆破坏特征试验[J]. 中国安全科学学报, 2020,30(5): 149-155. XIA Yuanyou, LIU Changhao, LIU Xiqi, et al. Experimental study on rockburst characteristics under uniform and gradient stress loading paths[J]. China Safety Science Journal, 2020,30(5): 149-155. [3] ZHOU Jian, LI Xibing, SHI Xiuzhi. Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines[J]. Safety Science, 2012, 50(4): 629-644. [4] DONG Longjun, LI Xibing, PENG Kang. Prediction of rockburst classification using Random forest[J]. Transactions of Nonferrous Metals Society of China, 2013, 23(2): 472-477. [5] OHADI B, SUN Xi, ESMAIELI K, et al. Predicting blast-induced outcomes using random forest models of multi-year blasting data from an open pit mine[J]. Bulletin of Engineering Geology and the Environment: the Official Journal of the IAEG, 2020, 79(6): 329-343. [6] XUE Yiguo, BAI Chenghao, QIU Daohong, et al. Predicting rockburst with database using particle swarm optimization and extreme learning machine[J]. Tunnelling and Underground Space Technology incorporating Trenchless Technology Research, 2020, 98: 1-12. [7] 李任豪, 顾合龙, 李夕兵,等. 基于PSO-RBF神经网络模型的岩爆倾向性预测[J]. 黄金科学技术, 2020,28(1): 134-141. LI Renhao, GU Helong, LI Xibing, et al. A PSO-RBF neural network model for rockburst tendency prediction[J]. Gold Science and Technology, 2020,28(1): 134-141. [8] 吴顺川, 张晨曦, 成子桥. 基于PCA-PNN原理的岩爆烈度分级预测方法[J]. 煤炭学报, 2019,44(9): 2 767-2 776. WU Shunchuan, ZHANG Chenxi, CHENG Ziqiao. Prediction of intensity classification of rockburst based on PCA-PNN principle[J]. Journal of China Coal Society, 2019,44(9): 2 767-2 776. [9] 唐鸣东, 史秀志, 周健,等. 基于CFOA-GRNN的冲击地压危险等级预测[J]. 中国安全科学学报, 2016,26(12): 110-115. TANG Mingdong, SHI Xiuzhi, ZHOU Jian, et al. Prediction of rock-burst risk rating based on CFOA-GRNN network[J]. China Safety Science Journal, 2016,26(12): 110-115. [10] 仝跃, 陈亮, 黄宏伟. 基于PSO-SVM算法的高放废物处置北山预选区岩爆预测[J]. 长江科学院院报, 2017,34(5): 68-74. TONG Yue, CHEN Liang, HUANG Hongwei. Rockburst prediction of Beishan pre-selected area for disposal of high-level radioactive waste based on PSO-SVM[J]. Journal of Yangtze River Scientific Research Institute, 2017,34(5): 68-74. [11] 黄建, 夏元友, 吝曼卿. 基于改进组合赋权的岩爆多维云模型预测研究[J]. 中国安全科学学报, 2019,29(7): 26-32. HUANG Jian, XIA Yuanyou, LIN Manqing. Study on prediction of rock burst by multi-dimensional cloud model based on improved combined weight[J]. China Safety Science Journal, 2019,29(7): 26-32. [12] 邵良杉, 周玉. 基于MIV-MA-KELM模型的岩爆烈度等级预测[J]. 中国安全科学学报, 2018,28(2): 34-39. SHAO Liangshan, ZHOU Yu. MIV-MA-KELM model based prediction of rockburst intensity grade[J]. China Safety Science Journal, 2018,28(2): 34-39. [13] 汤志立, 徐千军. 基于9种机器学习算法的岩爆预测研究[J]. 岩石力学与工程学报, 2020,39(4): 773-781. TANG Zhili, XU Qianjun. Rockburst prediction based on nine machine learning algorithms[J]. Chinese Journal of Rock Mechanics and Engineering, 2020,39(4): 773-781. [14] 梁晴晴, 韩华, 崔晓钰,等. 基于主元分析-概率神经网络的制冷系统故障诊断[J]. 化工学报, 2016,67(3): 1 022-1 031. LIANG Qingqing, HAN Hua, CUI Xiaoyu, et al. Fault diagnosis for refrigeration system based on PCA-PNN[J]. CIESC Journal, 2016,67(3): 1 022-1 031. [15] 王慧慧, 王萍, 刘涛,等. 基于生长-修剪优化RBF神经网络的电能质量扰动分类[J]. 电网技术, 2018,42(8): 2 408-2 415. WANG Huihui, WANG Ping, LIU Tao, et al. Power quality disturbance classification based on growing and pruning optimal RBF neural network[J]. Power System Technology, 2018,42(8): 2 408-2 415. [16] 王庆武, 巨能攀, 杜玲丽,等. 拉林铁路桑日至加查段三维地应力场反演分析[J]. 岩土力学, 2018,39(4): 1 450-1 462. WANG Qingwu, JU Nengpan, DU Lingli, et al. Three dimensional inverse analysis of geostress field in the Sangri-Jiacha section of Lasa-Linzhi railway[J]. Rock and Soil Mechanics, 2018,39(4): 1 450-1 462. [17] 杨凌霄, 朱亚丽. 基于概率神经网络的高压断路器故障诊断[J]. 电力系统保护与控制, 2015,43(10): 62-67. YANG Lingxiao, ZHU Yali. High voltage circuit breaker fault diagnosis of probabilistic neural network[J]. Power System Protection and Control, 2015,43(10): 62-67. [18] 王佳信, 周宗红, 赵婷,等. 基于Alpha稳定分布概率神经网络的围岩稳定性分类研究[J]. 岩土力学, 2016,37(增2):649- 657,664. WANG Jiaxin, ZHOU Zonghong, ZHAO Ting, et al. Application of Alpha stable distribution probabilistic neural network to classification of surrounding rock stability assessment[J]. Rock and Soil Mechanics, 2016,37(S2):649- 657,664. [19] 钟国强, 王浩, 李莉,等. 基于SFLA-GRNN模型的基坑地表最大沉降预测[J].岩土力学, 2019,40(2):792-798,808. ZHONG Guoqiang, WANG Hao, LI Li, et al. Prediction of maximum settlement of foundation pit based on SFLA-GRNN model[J]. Rock and Soil Mechanics, 2019,40(2):792-798,808. [20] ZHOU Keping, LING Yun, DENG Hongwei, et al. Prediction of rock burst classification using cloud model with entropy weight[J]. Transactions of Nonferrous Metals Society of China, 2016, 26(7): 1 995-2 002. [21] 张科, 李娜, 陈宇龙,等.裂隙砂岩变形破裂过程中应变场及红外辐射温度场演化特征研究[J]. 岩土力学, 2020,41(增1): 95-105. ZHANG Ke, LI Na, CHEN Yulong, et al. Evolution characteristics of strain field and infrared radiation temperature field during deformation and rupture process of fractured sandstone[J]. Rock and Soil Mechanics, 2020,41(S1): 95-105. |