Efficient prediction of the load-carrying capacity of ECC-strengthened RC beams – An extra-gradient boosting machine learning method

Keyphrases

• Efficient Prediction 100%
• Machine Learning Techniques 100%
• Load Capacity 100%
• Gradient Boosting Machine 100%
• Engineered Cementitious Composites 100%
• Boosting Learning 100%
• FRP-strengthened RC Beams 100%
• Reinforced Concrete Structures 25%
• XGBoost 25%
• Strengthened Beams 25%
• Governing Parameters 12%
• Corrosion 12%
• Machine Learning Models 12%
• Large Database 12%
• Composite Layer 12%
• Yield Strength 12%
• Number of Parameters 12%
• Regression-based Method 12%
• Optimized Model 12%
• Microcracks 12%
• Model Use 12%
• Longitudinal Reinforcement 12%
• Aggressive Environment 12%
• Order of Accuracy 12%
• Graphical User Interface 12%
• Tension Zone 12%
• Flexural Capacity 12%
• Consistent Patterns 12%
• User Interaction 12%
• Shapley Additive Explanations 12%
• Boosting Algorithm 12%
• Control Capability 12%
• Gini Coefficient 12%
• Concrete Substrate 12%
• Beam Depth 12%
• Steel Bar 12%
• Crack Width Control 12%
• Composite Thickness 12%
• Beam Specimen 12%
• Predicted Targets 12%

Engineering

• Machine Learning Method 100%
• Load Limits 100%
• Engineered Cementitious Composite 100%
• Reinforced Concrete Structure 25%
• Beam Specimen 12%
• Crack Width 12%
• Model Parameter 12%
• Limited Number 12%
• Durables 12%
• Machine Learning Technique 12%
• Flexural Capacity 12%
• Test Dataset 12%
• Steel Bar 12%
• Concrete Substrate 12%
• Beam Depth 12%
• Composite Thickness 12%
• Learning System 12%
• Yield Point 12%
• Graphical User Interface 12%

Material Science

• Reinforced Concrete 100%
• Corrosion 50%
• Yield Stress 50%