[1] |
冯夏庭, 肖亚勋, 丰光亮, 等. 岩爆孕育过程研究 [J]. 岩石力学与工程学报, 2019, 38(4): 649–673.
FENG X T, XIAO Y X, FENG G L, et al. Study on the development process of rockbursts [J]. Chinese Journal of Rock Mechanics and Engineering, 2019, 38(4): 649–673.
|
[2] |
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 Z L, XU Q J. Rockburst prediction based on nine machine learning algorithms [J]. Chinese Journal of Rock Mechanics and Engineering, 2020, 39(4): 773–781.
|
[3] |
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 N, WANG L G, JIA M T. 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.
|
[4] |
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): 2767–2776.
WU S C, ZHANG C X, CHENG Z Q. Prediction of intensity classification of rockburst based on PCA-PNN principle [J]. Journal of China Coal Society, 2019, 44(9): 2767–2776.
|
[5] |
李明亮, 李克钢, 秦庆词, 等. 岩爆烈度等级预测的机器学习算法模型探讨及选择 [J]. 岩石力学与工程学报, 2021, 40(Suppl 1): 2806–2816.
LI M L, LI K G, QIN Q C, et al. Discussion and selection of machine learning algorithm model for rockburst intensity grade prediction [J]. Chinese Journal of Rock Mechanics and Engineering, 2021, 40(Suppl 1): 2806–2816.
|
[6] |
吴菡, 郭永刚, 何军杰, 等. 基于GWO-SVM岩爆分级预测模型 [J]. 路基工程, 2023(1): 49–54.
WU H, GUO Y G, HE J J, et al. Rock burst classification prediction model based on GWO-SVM [J]. Subgrade Engineering, 2023(1): 49–54.
|
[7] |
BASNET P M S, MAHTAB S, JIN A B. A comprehensive review of intelligent machine learning based predicting methods in long-term and short-term rock burst prediction [J]. Tunnelling and Underground Space Technology, 2023, 142: 105434. doi: 10.1016/j.tust.2023.105434
|
[8] |
靳春玲, 姬照泰, 贡力, 等. 基于WOA-SVM的引水隧洞岩爆烈度评估模型 [J]. 中国安全科学学报, 2023, 33(9): 41–48.
JIN C L, JI Z T, GONG L, et al. Evaluation model of rockburst intensity of diversion tunnel based on WOA-SVM [J]. China Safety Science Journal, 2023, 33(9): 41–48.
|
[9] |
李康楠, 吴雅琴, 杜锋, 等. 基于卷积神经网络的岩爆烈度等级预测 [J]. 煤田地质与勘探, 2023, 51(10): 94–103. doi: 10.12363/issn.1001-1986.23.01.0018
LI K N, WU Y Q, DU F, et al. Prediction of rockburst intensity grade based on convolutional neural network [J]. Coal Geology & Exploration, 2023, 51(10): 94–103. doi: 10.12363/issn.1001-1986.23.01.0018
|
[10] |
郭延华, 赵帅. 基于KPCA-WOA-KELM的岩爆烈度预测 [J]. 河北工程大学学报(自然科学版), 2021, 38(2): 1–7.
GUO Y H, ZHAO S. Classified prediction model of rockburst using KPCA-WOA-KELM [J]. Journal of Hebei University of Engineering (Natural Science Edition), 2021, 38(2): 1–7.
|
[11] |
YIN X, LIU Q S, PAN Y C, et al. Strength of stacking technique of ensemble learning in rockburst prediction with imbalanced data: comparison of eight single and ensemble models [J]. Natural Resources Research, 2021, 30(2): 1795–1815. doi: 10.1007/s11053-020-09787-0
|
[12] |
刘慧敏, 徐方远, 刘宝举, 等. 基于CNN-LSTM的岩爆危险等级时序预测方法 [J]. 中南大学学报(自然科学版), 2021, 52(3): 659–670.
LIU H M, XU F Y, LIU B J, et al. Time-series prediction method for risk level of rockburst disaster based on CNN-LSTM [J]. Journal of Central South University (Science and Technology), 2021, 52(3): 659–670.
|
[13] |
辛付宇, 邢丽坤, 刘笑. 基于CNN-GRU神经网络的锂电池SOH估计与RUL预测 [J]. 上海节能, 2024(5): 819–826.
XIN F Y, XING L K, LIU X. SOH estimation and RUL prediction of lithium battery based on CNN-GRU neural networks [J]. Shanghai Energy Saving, 2024(5): 819–826.
|
[14] |
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. doi: 10.11988/ckyyb.20160058
TONG Y, CHEN L, HUANG H W. 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. doi: 10.11988/ckyyb.20160058
|
[15] |
陈志勇, 杜江. 基于1D-CNN-PSO-SVM的电力变压器故障诊断 [J]. 计算机仿真, 2024, 41(3): 71–75, 87. doi: 10.3969/j.issn.1006-9348.2024.03.013
CHEN Z Y, DU J. Fault diagnosis of power transformer based on 1D-CNN-PSO-SVM [J]. Computer Simulation, 2024, 41(3): 71–75, 87. doi: 10.3969/j.issn.1006-9348.2024.03.013
|
[16] |
WANG J, WANG W C, HU X X, et al. Black-winged kite algorithm: a nature-inspired meta-heuristic for solving benchmark functions and engineering problems [J]. Artificial Intelligence Review, 2024, 57(4): 98. doi: 10.1007/s10462-024-10723-4
|
[17] |
LIU W T, REN Y Y, MENG X Y, et al. Analysis of potential water inflow rates at an underground coal mine using a WOA-CNN-SVM approach [J]. Water, 2024, 16(6): 813. doi: 10.3390/w16060813
|
[18] |
刘剑, 周宗红, 刘军, 等. 基于主成分分析和改进Bayes判别的岩爆等级预测 [J]. 采矿与岩层控制工程学报, 2022, 4(5): 16–26.
LIU J, ZHOU Z H, LIU J, et al. Prediction of rockburst grade based on principal component analysis and improved Bayesian discriminant analysis [J]. Journal of Mining and Strata Control Engineering, 2022, 4(5): 16–26.
|
[19] |
XU G, LI K G, LI M L, et al. Rockburst intensity level prediction method based on FA-SSA-PNN model [J]. Energies, 2022, 15(14): 5016. doi: 10.3390/en15145016
|
[20] |
GUO J, GUO J W, ZHANG Q L, et al. Research on rockburst classification prediction based on BP-SVM model [J]. IEEE Access, 2022, 10: 50427–50447. doi: 10.1109/ACCESS.2022.3173059
|
[21] |
WANG Z Y, WANG Y L, JIN X L. Prediction of grade classification of rock burst based on PCA-SSA-PNN architecture [J]. Geofluids, 2023(1): 5299919.
|
[22] |
贾义鹏. 岩爆预测方法与理论模型研究 [D]. 杭州: 浙江大学, 2015.
JIA Y P. Study on prediction method and theorial model of rockburst [D]. Hangzhou: Zhejiang University, 2015.
|
[23] |
张恒源, 范俊奇, 郭佳奇, 等. 基于多参量判据的深地下工程岩爆倾向性研究 [J]. 高压物理学报, 2022, 36(2): 025202. doi: 10.11858/gywlxb.20210857
ZHANG H Y, FAN J Q, GUO J Q, et al. Rockburst tendency for deep underground engineering based on multi-parameters criterion [J]. Chinese Journal of High Pressure Physics, 2022, 36(2): 025202. doi: 10.11858/gywlxb.20210857
|
[24] |
张春生, 周垂一, 刘宁. 锦屏二级水电站深埋特大引水隧洞关键技术 [J]. 隧道建设(中英文), 2017, 37(11): 1492–1501.
ZHANG C S, ZHOU C Y, LIU N. Key technologies for extremely-large deep-buried headrace tunnel: a case study of Jinping Ⅱ Hydropower Station [J]. Tunnel Construction, 2017, 37(11): 1492–1501.
|
[25] |
SHAN Z G, YAN P. Management of rock bursts during excavation of the deep tunnels in Jinping Ⅱ Hydropower Station [J]. Bulletin of Engineering Geology and the Environment, 2010, 69(3): 353–363. doi: 10.1007/s10064-010-0266-2
|
[26] |
XIE X B, JIANG W, GUO J. Research on rockburst prediction classification based on GA-XGB model [J]. IEEE Access, 2021, 9: 83993–84020. doi: 10.1109/ACCESS.2021.3085745
|