[33] |
高 玮. 基于蚁群聚类算法的岩爆预测研究[J]. 岩土工程学报,2010,32(6):874-880.(GAO Wei. Research on rockburst prediction based on ant colony clustering algorithm[J]. Journal of Geotechnical Engineering,2010,32(6):874-880.(in Chinese))
|
[3] |
王建宇,王 锋. 隧道支护系统设计的模糊类比方法[J]. 土木工程学报,1990,23(4):51-59.(WANG Jianyu,WANG Feng. Fuzzy analogy method for tunnel support system design[J]. China Civil Engineering Journal,1990,23(4):51-59.(in Chinese))
|
[12] |
LIU K Y,LIU B G,FANG Y. An intelligent model based on statistical learning theory for engineering rock mass classification[J]. Bulletin of Engineering Geology and the Environment,2019,78(6):4 533-4 548.
|
[21] |
魏莉萍,张 清. 隧道工程喷锚支护的自动化设计方法[J]. 铁道学报,2000,22(1):83-86.(WEI Liping,ZHANG Qing. An automatic design approach for tunnel anchor bolt-shotcrete support[J]. Journal of the China Railway Society,2000,22(1):83-86.(in Chinese))
|
[5] |
李德军,于程硕,谢东武. 大跨度城市山岭隧道初期支护参数优化研究[J]. 现代隧道技术,2020,57(增1):387-393.(LI Dejun,YU Chengshuo,XIE Dongwu. Optimization of initial support parameters for large-span urban mountain tunnels[J]. Modern Tunnelling Technology,2020,57(Supp.1):387-393.(in Chinese))
|
[14] |
夏述光,刘 辉,何玉清,等. 围岩模糊信息分级模型在软岩隧道中的应用[J]. 隧道建设,2008,28(2):137-139.(XIA Shuguang,LIU Hui,HE Yuqing,et al. Application of fuzzy information classification model of surrounding rock in soft rock tunnel[J]. Tunnel Construction,2008,28(2):137-139.(in Chinese))
|
[16] |
YANG G,LI T B,MA C C,et al. Intelligent rating method of tunnel surrounding rock based on one-dimensional convolutional neural network[J]. Journal of Intelligent and Fuzzy Systems,2022,42(3):2 451-2 469.
|
[23] |
夏永旭,裘军良,王永东. 神经元网络在公路隧道支护设计中的应用[J]. 长安大学学报:自然科学版,2005,25(2):69-72.(XIA Yongxu,QIU Junliang,WANG Yongdong. Application of neuronal networks in highway tunnel support design[J]. Journal of Chang?an University:Natural Science,2005,25(2):69-72.(in Chinese))
|
[32] |
杨超杰,裴以建,刘 朋. 改进粒子群算法的三维空间路径规划研究[J]. 计算机工程与应用,2019,55(11):117-122.(YANG Chaojie,PEI Yijian,LIU Peng. Improved particle swarm algorithm for 3D spatial path planning research[J]. Computer Engineering and Applications,2019,55(11):117-122.(in Chinese))
|
[34] |
周煦桐. 基于神经网络算法的岩爆预测方法研究[硕士学位论文][D]. 湘潭:湘潭大学,2020.(ZHOU Xutong. Research on rockburst prediction method based on neural network algorithm[M. S. Thesis][D]. Xiangtan:Xiangtan University,2020.(in Chinese))
|
[41] |
陈 语. 隧道大变形灾害动态风险评估与支护决策研究[硕士学位论文][D]. 成都:成都理工大学,2017.(CHEN Yu. Dynamic risk assessment and support decisions for large deformation hazards in tunnels[M. S. Thesis][D]. Chengdu:Chengdu University of Technology,2017.(in Chinese))
|
[43] |
FENG T G,WANG C R,ZHANG J,et al. An improved artificial bee colony-random forest(IABC-RF) model for predicting the tunnel deformation due to an adjacent foundation pit excavation[J]. Underground Space,2022,7(4):514-527.
|
[47] |
ZHANG H,ZENG J,MA C C,et al. Multi-classification of complex microseismic waveforms using convolutional neural network:a case study in tunnel engineering[J]. Sensors,2021,21(20):6 762-6 777.
|
[20] |
任建喜. 岩体隧道支护神经网络决策系统[J]. 西安公路交通大学学报,1998,18(4):61-64.(REN Jianxi. Neural network decision system for rock mass tunnel supporing[J]. Journal of Xi?an Highway University,1998,18(4):61-64.(in Chinese))
|
[29] |
马忠臣,陈松灿. 多输出分类综述[J]. 杭州电子科技大学学报:自然科学版,2019,39(3):1-9.(MA Zhongchen,CHEN Songcan. Overview of multi-output classification[J]. Journal of Hangzhou Dianzi University:Natural Science,2019,39(3):1-9.(in Chinese))
|
[2] |
中华人民共和国行业标准编写组. JTG 3370.1—2018公路隧道设计规范[S]. 北京:人民交通出版社,2018.(The Professional Standards Compilation Group of People?s Republic of China. JTG 3370.1—2018 Code for design of highway tunnel[S]. Beijing:China Communications Press,2018.(in Chinese))
|
[7] |
梅 竹. 单线重载铁路隧道复合式衬砌结构优化研究[J]. 隧道建设(中英文),2021,41(9):1 547-1 554.(MEI Zhu. Optimization of composite lining structure of a single-track heavy-haul railway tunnel[J]. Tunnel Construction,2021,41(9):1 547-1 554.(in Chinese))
|
[25] |
BREIMAN L. Random forests[J]. Machine Learning,2001,45(1):5-32.
|
[1] |
徐建平,桑运龙,刘学增,等. 节理发育岩体隧道支护的动态设计方法与应用[J]. 地下空间与工程学报,2017,13(2):416-421.(XU Jianping,SANG Yunlong,LIU Xuezeng,et al. Observational method and its application for tunnel supporting in jointed rock[J]. Chinese Journal of Underground Space and Engineering,2017,13(2):416-421.(in Chinese))
|
[8] |
黄海昀,仇文革,黄 黆,等. 石质铁路隧道初期支护优化研究[J]. 铁道科学与工程学报,2019,16(1):152-161.(HUANG Haiyun,QIU Wenge,HUANG Guang,et al. Study on primary support optimization for railway tunnel in rock[J]. Journal of Railway Science and Engineering,2019,16(1):152-161.(in Chinese))
|
[9] |
仇文革,孙克国,王立川,等. 基于围岩稳定性的大断面隧道初期支护优化[J]. 土木工程学报,2017,50(增2):8-13.(QIU Wenge,SUN Keguo,WANG Lichuan,et al. Primary support optimization of large section tunnel based on surrounding rock stability[J]. China Civil Engineering Journal,2017,50(Supp.2):8-13.(in Chinese))
|
[10] |
LIU K Y,LIU B G. Intelligent information-based construction in tunnel engineering based on the GA and CCGPR coupled algorithm[J]. Tunnelling and Underground Space Technology,2019,88:113-128.
|
[17] |
ZHAO S G,WANG M N,YI W H,et al. Intelligent classification of surrounding rock of tunnel based on 10 machine learning algorithms[J]. Applied Sciences,2022,12(5):2 656-2 675.
|
[18] |
马世伟,李守定,李 晓,等. 隧道岩体质量智能动态分级KNN方法[J]. 工程地质学报,2020,28(6):1 415-1 424.(MA Shiwei,LI Shouding,LI Xiao,et al. KNN method for intelligent observational classification of rock mass quality in tunnel[J]. Journal of Engineering Geology,2020,28(6):1 415-1 424.(in Chinese))
|
[19] |
LI X,WANG Q. Prediction of surrounding rock classification of highway tunnel based on PSO-SVM[C]// International Conference on Robots and Intelligent System (ICRIS),IEEE. [S. l.]:[s. n.],2019:443-446.
|
[26] |
BI X A,XING Z X,ZHOU W Y,et al. Pathogeny detection for mild cognitive impairment via weighted evolutionary random forest with brain imaging and genetic data[J]. IEEE Journal of Biomedical and Health Informatics,2022,26(7):3 068-3 079.
|
[27] |
ZHENG G,GU X Y,ZHANG T Q,et al. Random forest method-based prediction and control of bridge pier displacements during construction of two overlapped EPBM tunnels[J]. European Journal of Environmental and Civil Engineering,2022,26(6):2 273-2 293.
|
[45] |
ZHANG M L,ZHOU Z H. ML-KNN:A lazy learning approach to multi-label learning[J]. Pattern Recognition,2007,40(7):2 038-2 048.
|
[4] |
CHEN Y X,LI L P,ZHOU Z Q,et al. Support scheme optimization aimed at the asymmetric deformation of the supported rock in a deep tunnel[J]. Arabian Journal of Geosciences,2022,15(6):505-521.
|
[30] |
崔浩康. 多标签学习算法的改进与研究[硕士学位论文][D]. 成都:电子科技大学,2020.(CUI Haokang. Improvement and research of multi-label learning algorithm[M. S. Thesis][D]. Chengdu:University of Electronic Science and Technology of China,2020.(in Chinese))
|
[39] |
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):3 705-3 711. (ZHOU Keping,LEI Tao,HU Jianhua. RS-TOPSIS rockburst prediction model for deepmetal mines and its application[J]. Chinese Journal of Rock Mechanics and Engineering,2013,32(Supp.2):3 705-3 711. (in Chinese))
|
[48] |
MA Chunchi,FAN Junqi,JI Xiang,et al. Effect of the initial support of the tunnel on the characteristics of rockburst:case study and mechanism analysis[J]. Advances in Civil Engineering,DOI:https://doi.org/10.1155/2022/7235619.
|
[6] |
LIU Q W,LI Y S,LI W T,et al. Primary support optimization of large-span and shallow buried hard rock tunnels based on the active support concept[J]. Scientific Reports,2022,12(1):1-21.
|
[11] |
FENG X T,ZHANG C Q,QIU S L,et al. Dynamic design method for deep hard rock tunnels and its application[J]. Journal of Rock Mechanics and Geotechnical Engineering,2016,8(4):443-461.
|
[13] |
柳厚祥,李汪石,查焕奕,等. 基于深度学习技术的公路隧道围岩分级方法[J]. 岩土工程学报,2018,40(10):1 809-1 817.(LIU Houxiang,LI Wangshi,ZHA Huanyi,et al. Method for surrounding rock mass classification of highway tunnels based on deep learning technology[J]. Chinese Journal of Geotechnical Engineering,2018,40(10):1 809-1 817.(in Chinese))
|
[15] |
杨小永,伍法权,苏生瑞. 公路隧道围岩模糊信息分类的专家系统[J]. 岩石力学与工程学报,2006,25(1):100-105.(YANG Xiaoyong,WU Faquan,SU Sshengrui. Expert system of fuzzy information for classification of surrounding rock mass in highway tunnel[J]. Chinese Journal of Rock Mechanics and Engineering,2006,25(1):100-105.(in Chinese))
|
[22] |
裘军良. 公路隧道围岩判别和支护设计人工神经元网络方法的研究[硕士学位论文][D]. 西安:长安大学,2003.(QIU Junliang. Research on artificial neural network method of surrounding rock discrimination and support design of highway tunnel[M. S. Thesis][D]. Xi?an:Chang?an University,2003.(in Chinese))
|
[24] |
陈子全,何 川,周子寒,等. 基于机器学习的隧道支护体系智能化设计与评价方法[J/OL]. 中国公路学报,DOI:https://kns.cnki.net/kcms/detail/ 61.1313.u.20230320.1658.005.html.(CHEN Ziquan,HE Chuan,ZHOU Zihan,et al. Intelligent design and evaluation method for tunnel support system based on machine learning[J/OL]. Chinese Journal of Highways,DOI:https://kns.cnki.net/kcms/detail/61.1313.u.20230320.1658.005.html. (in Chinese))
|
[31] |
EBERHART R,KENNEDY J. A new optimizer using particle swarm theory[C]// Proceedings of the Sixth International Symposium on Micro Machine and Human Science. [S. l.]:[s. n.],1995:39-43.
|
[35] |
PU Y,APEL D,XU H. A principal component analysis/fuzzy comprehensive evaluation for rock burst potential in kimberlite[J]. Pure and Applied Geophysics,2018,175(6):2 141-2 151.
|
[36] |
ZHOU J,LI X B,SHI X Z et al. Long-term prediction model of rock burst in underground openings using heuristic algorithms and support vector machines[J]. Safety Science,2012,50(4):629-644.
|
[38] |
杨 玲,魏 静. 基于支持向量机和增强学习算法的岩爆烈度等级预测[J]. 地球科学,2023,48(5):2 011-2 023.(YANG Ling,WEI Jing. Rockburst intensity class prediction based on support vector machines and augmented learning algorithms[J]. Earth Sciences,2023,48(5):2 011-2 023.(in Chinese))
|
[40] |
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):1 461-1 465.(ZHANG Leiwen,ZHANG Deyong,QIU Daohong. Application of rough set-based topologizable judgments to rockburst prediction[J]. Journal of China Coal Society,2010,35(9):1 461-1 465.(in Chinese))
|
[42] |
FAN X M,ROSSITER D G,WESTEN C J,et al. Empirical prediction of coseismic landslide dam formation[J]. Earth Surface Processes and Landforms,2014,39(14):1 913-1 926.
|
[28] |
朱梦琦,朱合华,王 昕,等. 基于集成CART算法的TBM掘进参数与围岩等级预测[J]. 岩石力学与工程学报,2020,39(9):1 860-1 871.(ZHU Mengqi,ZHU Hehua,WANG Xin,et al. Study on CART-based ensemble learning algorithms for predicting TBM tunneling parameters and classing surrounding rockmasses[J]. Chinese Journal of Rock Mechanics and Engineering,2020,39(9):1 860-1 871. (in Chinese))
|
[37] |
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. Research on rockburst prediction based on 9 machine learning algorithms[J]. Chinese Journal of Rock Mechanics and Engineering,2020,39(4):773-781.(in Chinese))
|
[46] |
ZHANG M L,ZHOU Z H. Multilabel neural networks with applications to functional genomics and text categorization[J]. IEEE Transactions on Knowledge and Data Engineering,2006,18(10):1 338-1 351.
|
[44] |
XU D N,SHI X Y,TSANG I W,et al. A survey on multi-output learning[J]. IEEE Transactions on Neural Networks and Learning Systems,2020,31(7):2 409-2 429.
|