中文

Journal Publications

[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

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