[1] 谢和平.深部岩体力学与开采理论研究进展[J]. 煤炭学报, 2019, 44(5): 1283-1305. (Xie Heping. Research review of the state key research development program of China: Deep rock mechanics and mining theory[J]. Journal of China Coal Society, 2019, 44(5): 1283-1305. (in Chinese))
[2] 张传庆,卢景景,陈珺,等.岩爆倾向性指标及其相互关系探讨[J]. 岩土力学, 2017, 38(5): 1397-1404. (Zhang Chuanqing, Lu Jingjing, Chen Jun, et al. Discussion on rock burst proneness indexes and their relation[J]. Rock and Soil Mechanics, 2017, 38(5): 1397-1404. (in Chinese))
[3] 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(5): 15-23. (Wang Yuanhan, Li Wodong, Li Qiguang, et al. Method of fuzzy comprenhensive evaluations for and rockburst prediction[J]. Chinese Journal of Rock Mechanics and Engineering, 1998(5): 15-23. (in Chinese))
[4] 赵浩杨,石广斌,杨振宏,等.基于组合赋权-改进集对分析的岩爆倾向性预测研究[J]. 金属矿山, 2021(5): 71-77. (Zhao Haoyang, Shi Guangbin, Yang Zhenhong, et al. Study on rockburst tendency prediction based on combined weighting-improved set pair analysis[J]. Metal Mine, 2021(5): 71-77. (in Chinese))
[5] 罗磊,曹平.深部巷道岩爆加权距离判别法模型的分析和应用[J]. 中南大学学报:自然科学版, 2012, 43(10): 3971-3975. (Luo Lei, Cao Ping. Model of weighted distance discriminant analysis and application for deep roadway[J]. Journal of Central South University:Science and Technology, 2012, 43(10): 3971-3975. (in Chinese))
[6] 张徐琛,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. (Zhang Xuchen, Liu Xiaoli, Wang Enzhi, et al. Prediction and classification of strain mode 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))
[7] 周航,陈仕阔,张广泽,等.基于功效系数法和地应力场反演的深埋长大隧道岩爆预测研究[J]. 工程地质学报, 2020, 28(6):1386-1396.(Zhou Hang, Chen Shikuo,Zhang Guangze, et al. Efficiency coefficient method and ground stress field inversion for rockburst predicition in deep and long tunnel[J]. Journal of Engineering Geology, 2020, 28(6): 1386-1396. (in Chinese))
[8] 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, et al. Prediction of rock burst-based on combination weighting and cloud model theory[J]. Chinese Journal of Underground Space and Engineering, 2018,14(Supp.1): 409-415. (in Chinese))
[9] 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, et al. 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))
[10] 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, et al. Application of self-organizing feature map in rockburst classification[J]. Chinese Journal of Underground Space and Engineering, 2023, 19(1): 334-342. (in Chinese))
[11] 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,et al.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))
[12] 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(3): 120-128. (Liu Jian, Zhou Zonghong. Rockburst grade predictionin based on modified scatter graph matrixn and random forest[J]. Nonferrous Metals Engineering, 2022, 12(3): 120-128. (in Chinese))
[13] 殷欣,刘泉声,王心语,等.基于组合赋权和属性区间识别理论的岩爆烈度分级预测模型[J]. 煤炭学报, 2020, 45(11): 3772-3780.(Yin Xin,Liu Quansheng,Wang Xinyu, et al. Prediction model of rockburst intensity classification based on combined weighting and attribute interval recognition theory[J]. Journal of China Coal Society, 2020, 45(11): 3772-3780. (in Chinese))
[14] 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))
[15] 李明亮,李克钢,秦庆词,等.基于改进组合赋权-TOPSIS法的岩爆倾向性评判模型[J]. 中国安全生产科学技术, 2020, 16(3): 74-80.(Li Mingliang,Li Kegang,Qin Qingci,et al. Judgment model of rock burst tendency based on improved combination weighting-TOPSIS method[J]. Journal of Safety Science and Technology, 2020, 16(3): 74-80.(in Chinese))
[16] 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, et al. 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. (in Chinese))
[17] 张晨,王清,陈剑平,等.金沙江流域泥石流的组合赋权法危险度评价[J]. 岩土力学, 2011, 32(3): 831-836.(Zhang Chen,Wang Qing,Chen Jianping, et al. Evaluation of debris flow risk in Jinsha River based on combined weight process[J]. Rock and Soil Mechanics, 2011, 32(3): 831-836. (in Chinese))
[18] 李韶慧,周忠发,但雨生,等.基于组合赋权贝叶斯模型的平寨水库水质评价[J].水土保持通报, 2020, 40(2): 211-217.(Li Shaohui,Zhou Zhongfa, Dan Yusheng, et al. Water quality evaluation of Pin Zhai reservoir based on combined weighted Bayesian model[J]. Bulletin of Soil and Water Conservation, 2020, 40(2): 211-217. (in Chinese))
[19] 徐健,吴玮,黄天寅,等.改进的模糊综合评价法在同里古镇水质评价中的应用[J]. 河海大学学报:自然科学版,2014,42(2):143-149.(Xu Jian, Wu Wei, Huang Tianyin, et al. Application of improved fuzzy comprehensive evaluation to water quality evaluation in Tongli Town[J]. Journal of Hohai University:Natural Sciences, 2014, 42(2): 143-149. (in Chinese))
[20] 王羽,许强,柴贺军,等.工程岩爆灾害判别的RBF-AR耦合模型[J].吉林大学学报:地球科学版, 2013, 43(6): 1943-1949, 1965.(Wang Yu, Xu Qiang, Chai Hejun, et al. Rock burst prediction in deep shaft based on RBF-AR model[J]. Journal of Jilin University(Earth Science Edition), 2013, 43(6): 1943-1949, 1965. (in Chinese))
[21] 李宗坤,莫向明,葛巍,等.基于变权集对-可拓耦合模型的溃坝后果综合评价[J]. 工程科学与技术, 2022, 54(5): 64-71. (Li Zongkun, Mo Xiangming, Ge Wei, et al. Comprehensive evaluation of dam-break consequences based on variable weight set pair-extenics coupling model[J]. Advanced Engineering Scineces, 2022, 54(5): 64-71. (in Chinese))
[22] 衣永亮,曹平,蒲成志.金川深部典型岩石岩爆倾向性多因素综合评判[J]. 科技导报, 2010, 28(2): 76-80. (Yi Yongliang, Cao Ping, Pu Chengzhi. Multi-factorial comprehensive estimation for Jinchuan's deep typical rockburst tendency[J]. Science & Technology Review, 2010, 28(2): 76-80. (in Chinese))
