Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate J Zhou, Y Qiu, S Zhu, DJ Armaghani, C Li, H Nguyen, S Yagiz Engineering Applications of Artificial Intelligence 97, 104015, 2021 | 229 | 2021 |
Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibration Y Qiu, J Zhou, M Khandelwal, H Yang, P Yang, C Li Engineering with Computers 38 (Suppl 5), 4145-4162, 2022 | 208 | 2022 |
Random forests and cubist algorithms for predicting shear strengths of rockfill materials J Zhou, E Li, H Wei, C Li, Q Qiao, DJ Armaghani Applied sciences 9 (8), 1621, 2019 | 205 | 2019 |
Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques J Zhou, Y Qiu, DJ Armaghani, W Zhang, C Li, S Zhu, R Tarinejad Geoscience Frontiers 12 (3), 101091, 2021 | 194 | 2021 |
Performance evaluation of hybrid FFA-ANFIS and GA-ANFIS models to predict particle size distribution of a muck-pile after blasting J Zhou, C Li, CA Arslan, M Hasanipanah, H Bakhshandeh Amnieh Engineering with computers 37 (1), 265-274, 2021 | 123 | 2021 |
Rockburst prediction in hard rock mines developing bagging and boosting tree-based ensemble techniques S Wang, J Zhou, C Li, DJ Armaghani, X Li, HS Mitri Journal of Central South University 28 (2), 527-542, 2021 | 94 | 2021 |
Use of intelligent methods to design effective pattern parameters of mine blasting to minimize flyrock distance J Zhou, M Koopialipoor, BR Murlidhar, SA Fatemi, MM Tahir, ... Natural Resources Research 29, 625-639, 2020 | 83 | 2020 |
Stability analysis of underground mine hard rock pillars via combination of finite difference methods, neural networks, and Monte Carlo simulation techniques C Li, J Zhou, DJ Armaghani, X Li Underground Space 6 (4), 379-395, 2021 | 77 | 2021 |
Development of a new methodology for estimating the amount of PPV in surface mines based on prediction and probabilistic models (GEP-MC) J Zhou, C Li, M Koopialipoor, D Jahed Armaghani, B Thai Pham International Journal of Mining, Reclamation and Environment 35 (1), 48-68, 2021 | 71 | 2021 |
Developing hybrid ELM-ALO, ELM-LSO and ELM-SOA models for predicting advance rate of TBM C Li, J Zhou, M Tao, K Du, S Wang, DJ Armaghani, ET Mohamad Transportation Geotechnics 36, 100819, 2022 | 47 | 2022 |
Six novel hybrid extreme learning machine–swarm intelligence optimization (ELM–SIO) models for predicting backbreak in open-pit blasting C Li, J Zhou, M Khandelwal, X Zhang, M Monjezi, Y Qiu Natural Resources Research 31 (5), 3017-3039, 2022 | 40 | 2022 |
COSMA-RF: New intelligent model based on chaos optimized slime mould algorithm and random forest for estimating the peak cutting force of conical picks J Zhou, Y Dai, K Du, M Khandelwal, C Li, Y Qiu Transportation Geotechnics 36, 100806, 2022 | 32 | 2022 |
Development of a hybrid artificial intelligence model to predict the uniaxial compressive strength of a new aseismic layer made of rubber-sand concrete X Mei, C Li, Q Sheng, Z Cui, J Zhou, D Dias Mechanics of Advanced Materials and Structures 30 (11), 2185-2202, 2023 | 29 | 2023 |
Application of Six Metaheuristic Optimization Algorithms and Random Forest in the uniaxial compressive strength of rock prediction J Li, C Li, S Zhang Applied Soft Computing 131, 109729, 2022 | 29 | 2022 |
Stochastic assessment of hard rock pillar stability based on the geological strength index system C Li, J Zhou, DJ Armaghani, W Cao, S Yagiz Geomechanics and Geophysics for Geo-Energy and Geo-Resources 7, 1-24, 2021 | 29 | 2021 |
A new hybrid model of information entropy and unascertained measurement with different membership functions for evaluating destressability in burst-prone underground mines J Zhou, C Chen, K Du, D Jahed Armaghani, C Li Engineering with Computers, 1-19, 2020 | 27 | 2020 |
A kernel extreme learning machine-grey wolf optimizer (KELM-GWO) model to predict uniaxial compressive strength of rock C Li, J Zhou, D Dias, Y Gui Applied Sciences 12 (17), 8468, 2022 | 24 | 2022 |
Applying a novel slime mould algorithm-based artificial neural network to predict the settlement of a single footing on a soft soil reinforced by rigid inclusions J Zhang, D Dias, L An, C Li Mechanics of Advanced Materials and Structures 31 (2), 422-437, 2024 | 20 | 2024 |
Application of the improved POA-RF model in predicting the strength and energy absorption property of a novel aseismic rubber-concrete material X Mei, Z Cui, Q Sheng, J Zhou, C Li Materials 16 (3), 1286, 2023 | 18 | 2023 |
Stability prediction of hard rock pillar using support vector machine optimized by three metaheuristic algorithms C Li, J Zhou, K Du, D Dias International Journal of Mining Science and Technology 33 (8), 1019-1036, 2023 | 15 | 2023 |