个人简介

曾获荣誉： 中南大学升华猎英人才计划,中国岩石力学与工程学会优博奖 周健，男，土家族，湖北巴东人，中国爆破行业协会理事，中国岩石力学与工程学会岩石破碎工程专业委员会委员，中南大学升华猎英人才。主持国家自然科学基金和湖南省自然科学基金各1项，负责国家重点研发项目课题二2017YFC0602902子任务一项，入选中南大学2020年“创新驱动青年人才计划”，荣获湖南省自然科学二等奖1项(排名第二)和中国岩石力学与工程学会优秀博士论文奖(推荐国际岩石力学罗哈奖评选)；荣获第四届矿山安全科学与工程国际会议优秀论文奖和《中国有色金属学报》2018年度优秀论文奖。先后以第一作者或通讯作者在Journal of Computing in Civil Engineering, ASCE, International Journal of Geomechanics, ASCE, Journal of Performance of Constructed Facilities, ASCE, Geoscience Frontiers, Engineering Applications of Artificial Intelligence, Safety Science, Tunnelling and Underground Space Technology, Bulletin of Engineering Geology and the Environment, International Journal of Mining, Reclamation and Environment, Journal of Vibration and Control, Engineering with Computers, Int J Rock Mech Min Sci., Natural Resources Research, Engineering Optimization, Natural Hazards, Underground Space和Trans. Nonferrous Met. Soc. China等国内外知名期刊上发表论文80余篇，SCI收录60余篇，Google学术引用2600余次， H-index:29, i10-index: 65, 其中14篇入选ESI高被引论文(1%)，3篇入选全球热点论文(0.1%)；申请国家发明专利10余项，已授权5项。受邀担任Applied Sciences主题编辑、Metaheuristic Computing and Applications创刊编委、Advances in Civil Engineering编委及IEEE Transactions on Cybernetics, Reliability Engineering and System Safety, Safety Science, Journal of Computing in Civil Engineering, ASCE, International Journal of Geomechanics, ASCE, Expert Systems With Applications, Engineering Applications of Artificial Intelligence, Neurocomputing, Journal of Cleaner Production, Underground Space, Journal of Rock Mechanics and Geotechnical Engineering, Journal of Vibration and Control, Measurement, Engineering with Computers, Rock Mechanics and Rock Engineering, Tunnelling and Underground Space Technology, Natural Hazards, Atmospheric Pollution Research, Journal of Geochemical Exploration, IEEE Access, IEEE/CAA Journal of Automatica Sinica, Environmental Earth Sciences, Energy Science & Engineering, PLOS ONE, Shock and Vibration, Natural Resources Research, Mathematical problem in engineering, Advances in Civil Engineering, International Journal of Mining Science and Technology, Complex & Intelligent Systems, CMES-Computer Modeling in Engineering & Sciences, Geotechnical and Geological Engineering等期刊审稿人。 教育经历 [1] McGill University [2] 中南大学 工作经历 [1] 中南大学 | 资源与安全工程学院 科研项目 [1]矿井深部超大空场采掘充工艺技术研究与应用—中深孔机械化采矿工艺采场结构参数研究 [2]寒区矿岩冻融环境和冲击荷载作用下岩石动静物理力学特性 [3]深部高应力硬岩岩爆灾害预警的大数据挖掘方法 [4]爆堆精准预测模型以及爆破震动和雷达监测数据内在机制 [5]深部典型非常规破坏孕灾参量贡献机制与灾害预测方法,在研,中南大学创新驱动计划,周健 [6]深部高储能矿岩组孔超前致裂精准爆破技术,在研,国家重点研发计划 [7]炸药能量空间协同装药结构优化算法及爆破块度预测模型,在研,金属矿山安全与健康国家重点实验室开放课题,周健 [8]大数据环境下基于数据驱动的岩爆评估与预测模型,在研,湖南省自然科学基金,周健 [9]中南大学升华猎英计划项目,在研,中南大学,周健 [10]诱发型岩爆的内外因参量贡献机制及预警模型,在研,国家自然科学基金,周健 获奖信息 [1]教育部博士研究生学术新人奖|2012,周健 [2]中国岩石力学与工程学会优博奖|2017,周健 [3]第四届矿山安全科学与工程国际会议（ISMSSE2018）优秀论文奖|2018,周健

研究领域

[1] 面向“矿业+”的工业大数据 [2] 矿山岩土工程灾害预警 [3] 爆破优化设计 [4] 小样本数据挖掘与应用预测建模

1. 矿山岩土工程灾害预警 岩爆风险估计+硬岩矿柱稳定性诊断+采空区危险性识别+边坡稳定性+砂土液化势+爆破振动危害... 2. 爆破优化设计 3. 尾砂基纤维充填材料 4. 矿山岩土工程数据挖掘 数据可视化+融合经典力学理论和有限矿山岩土工程样本数据信息+应用预测建模

近期论文

查看导师新发文章 （温馨提示：请注意重名现象，建议点开原文通过作者单位确认）

[1].Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques:Geoscience Frontiers,2020 [2].Evaluation method of rockburst: State-of-the-art literature review:Tunnelling and Underground Space Technology,2018,81:632-659 [ESI 0.1% Since 2020] [3].Classification of Rockburst in Underground Projects Comparison of Ten Supervised Learning Methods:Journal of Computing in Civil Engineering, ASCE,2016,30(5):04016003 [ESI 1% Since 2020] [4].Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines:Safety Science,2012,50:629–644 [5].Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate:Engineering Applications of Artificial Intelligence,2020 [6].Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories:Safety Science,2019 [7].Charge design scheme optimization for ring blasting based on the developed Scaled Heelan model:Int J Rock Mech Min Sci.,2018,110:199-209 [8].Feasibility of Stochastic Gradient Boosting Approach for Evaluating Seismic Liquefaction Potential Based on SPT and CPT Case Histories:Journal of Performance of Constructed Facilities, ASCE,2019 [9].Feasibility of Random-Forest Approach for Prediction of Ground Settlements Induced by the Construction of a Shield-Driven Tunnel:International Journal of Geomechanics, ASCE,2017,17(6):04016129 [10].Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction:Natural Hazards,2015,79(1):291 – 316 [11].Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization:Underground Space,2020 [12].Deep neural network and whale optimization algorithm to assess flyrock induced by blasting:Engineering with Computers,2020 [13].Stability analysis of underground mine hard rock pillars via combination of finite difference methods, neural networks, and Monte Carlo simulation techniques:Underground Space,2020 [14].硬岩矿山开采技术回顾与展望:中国有色金属学报,2019 [15].硬岩矿山开采方式变革与智能化绿色矿山构建——以开阳磷矿为例:中国有色金属学报,2019 [16].Prediction of ground vibration induced by blasting operations through the use of the Bayesian Network and random forest models:Soil Dynamics and Earthquake Engineering,2020 [17].Forecasting of TBM advance rate in hard rock condition based on artificial neural network and genetic programming techniques:Bulletin of Engineering Geology and the Environment,2020 [18].Waveform features and failure patterns of hollow cylindrical sandstone specimens under repetitive impact and triaxial confinements:Geomechanics and Geophysics for Geo-Energy and Geo-Resources,2020 [19].Use of intelligent methods to design effective pattern parameters of mine blasting to minimize flyrock distance:Natural Resources Research,2019 [20].Development of a new methodology for estimating the amount of PPV in surface mines based on prediction and probabilistic models (GEP-MC):International Journal of Mining, Reclamation and Environment,2020 [21].Performance evaluation of hybrid FFA-ANFIS and GA-ANFIS models to predict particle size distribution of a muck-pile after blasting:Engineering with Computers,2019 [22].Random Forests and Cubist Algorithms for Predicting Shear Strengths of Rockfill Materials:Applied Sciences,2020 [23].Prediction of rockburst risk in underground projects developing a neuro-bee intelligent system:Bulletin of Engineering Geology and the Environment,2020 [24].Investigating the effective parameters on the risk levels of rockburst phenomena by developing a hybrid heuristic algorithm:Engineering with Computers,2020 [25].Developing a hybrid model of salp swarm algorithm based support vector machine to predict the strength of fiber reinforced cemented paste backfill:Engineering with Computers,2020 [26].A new hybrid model of information entropy and unascertained measurement with different membership functions for evaluating destressability in burst-prone underground mines:Engineering with Computers,2020 [27].深部固体资源开采评述与探索:中国有色金属学报,2017,27(6):1236-1262 [28].Integrating unascertained measurement and information entropy theory to assess blastability of rock mass:J. Cent. South Univ.,2012,19:1953？1960 [29].Predicting pillar stability for underground mine using Fisher discriminant analysis and SVM methods:Trans. Nonferrous Met. Soc. China,2011,21:2734-2743 [30].Utilizing gradient boosted machine for the prediction of damage to residential structures owing to blasting vibrations ofopen pit mining:Journal of Vibration and Control,2016,22(19):3986–3997 [31].Feasibility of stochastic gradient boosting approach for predicting rockburst damage in burst-prone mines:Trans. Nonferrous Met. Soc. China,2016,26:1938？1945 [32].Multiplanar detection optimization algorithm for the interval charging structure of large-diameter longhole blasting design based on rock fragmentation aspects:Engineering Optimization,2018,50(12):2177-2191

学术兼职

[1] Applied Sciences主题编辑，2019 Impact Factor (WoS) - 2.474 (Q2) [2] 中国岩石力学与工程学会岩石破碎工程专业委员会委员 [3] Advances in Civil Engineering编委， 2019 Impact Factor (WoS) - 1.176 (Q3) [4] Metaheuristic Computing and Applications编委 [5] 中国爆破行业协会理事