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Chuanqi Li
Chuanqi Li
Université Grenoble Alpes
Verified email at univ-grenoble-alpes.fr
Title
Cited by
Cited by
Year
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
2292021
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
2082022
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
2052019
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
1942021
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
1232021
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
942021
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
832020
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
772021
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
712021
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
472022
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
402022
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
322022
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
292023
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
292022
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
292021
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
272020
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
242022
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
202024
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
182023
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
152023
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