Chuanqi Li

Université Grenoble Alpes

France

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Articles (11)

Rockburst prediction and prevention in underground space excavation

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Year:

2024

Application of SVR models built with AOA and Chaos mapping for predicting tunnel crown displacement induced by blasting excavation

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Year:

2023

Sample-wised probabilistic stability analysis of circular shafts using the Atom Search Optimization-based Artificial Neural Network

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Year:

2023

Application of metaheuristic optimization algorithms-based three strategies in predicting the energy absorption property of a novel aseismic concrete material

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Year:

2023

Hybridized random forest with population-based optimization for predicting shear properties of rock fractures

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Year:

2023

Stability prediction of hard rock pillar using support vector machine optimized by three metaheuristic algorithms

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Year:

2023

Machine learning models to predict the tunnel wall convergence

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Year:

2023

Metaheuristic Optimization of Random Forest for Predicting Punch Shear Strength of FRP-Reinforced Concrete Beams

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Year:

2023

Compressive Strength Prediction of Rice Husk Ash Concrete Using a Hybrid Artificial Neural Network Model

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Year:

2023

Application of Back-Propagation Neural Network in the Post-Blast Re-Entry Time Prediction

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Year:

2023

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

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Year:

2022

Collaborators (1)

Daniel Dias

FRANCE

Chuanqi Li

Research Interests

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Contact this professor

Articles (11)

Rockburst prediction and prevention in underground space excavation

Application of SVR models built with AOA and Chaos mapping for predicting tunnel crown displacement induced by blasting excavation

Sample-wised probabilistic stability analysis of circular shafts using the Atom Search Optimization-based Artificial Neural Network

Application of metaheuristic optimization algorithms-based three strategies in predicting the energy absorption property of a novel aseismic concrete material

Hybridized random forest with population-based optimization for predicting shear properties of rock fractures

Stability prediction of hard rock pillar using support vector machine optimized by three metaheuristic algorithms

Machine learning models to predict the tunnel wall convergence

Metaheuristic Optimization of Random Forest for Predicting Punch Shear Strength of FRP-Reinforced Concrete Beams

Compressive Strength Prediction of Rice Husk Ash Concrete Using a Hybrid Artificial Neural Network Model

Application of Back-Propagation Neural Network in the Post-Blast Re-Entry Time Prediction

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

Collaborators (1)

Daniel Dias

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