Predicting permanent strain accumulation of unbound aggregates using machine learning algorithms

Keyphrases

• Strain Accumulation 100%
• Unbound Aggregates 100%
• Machine Learning Algorithms 100%
• Permanent Strain 100%
• Machine Learning Based 75%
• Learning-based Framework 75%
• Neural Network 25%
• Material Properties 25%
• Numerical Results 25%
• Learning Process 25%
• Stress State 25%
• Permanent Deformation 25%
• Load Cycle 25%
• K-nearest 25%
• Generalization Performance 25%
• Single Machine 25%
• Learning Model 25%
• Random Forest 25%
• Decision Tree 25%
• Coefficient of Determination 25%
• Aggregate Layer 25%
• Machine Learning Models 25%
• Strength Characteristics 25%
• Deviatoric Stress 25%
• Repeated Load Triaxial Test 25%
• Generalization Ability 25%
• Deformation Test 25%
• Hyperparameters 25%
• Shear Strength Characteristics 25%
• Crushed Aggregate 25%
• Ensemble Machine Learning 25%
• Extreme Gradient Boosting 25%
• Extreme Gradient Boosting Model 25%
• Framework Materials 25%

Engineering

• Machine Learning Algorithm 100%
• Learning System 100%
• Applied Stress 25%
• Stress State 25%
• Permanent Deformation 25%
• Load Cycle 25%
• Learning Algorithm 25%
• Random Forest 25%
• Deviatoric Stress 25%
• Input Feature 25%
• Shear Strength 25%
• Nearest Neighbor 25%

Material Science

• Shear Strength 100%
• Materials Property 100%