Keyphrases
Machine Learning Approach
100%
Concrete Properties
100%
Experimental Learning
100%
Self-compacting Rubberized Concrete
100%
Powdered Waste
50%
Waste Rubber Tire
50%
Sustainable Building Materials
33%
Microstructural Analysis
33%
Mechanical Strength
16%
Negative Impact
16%
Adverse Effects
16%
Workability
16%
Unconventional Materials
16%
Model Performance
16%
Least Absolute Shrinkage and Selection Operator (LASSO)
16%
Compressive Strength
16%
Decision Tree
16%
Sodium Sulfate
16%
Random Forest
16%
Mechanical Testing
16%
Solid Waste
16%
Clean Environment
16%
Evaluation Metrics
16%
Mechanical Properties of Concrete
16%
Tensile Strength
16%
Correlation Matrix
16%
Random Forest Regression
16%
Waste Tire
16%
Flowability
16%
Acid Attack
16%
Ridge Regression
16%
Extreme Gradient Boosting
16%
Fine Aggregate
16%
Self-compacting Concrete
16%
Slump Flow
16%
Machine Learning Regression Algorithms
16%
Support Vector
16%
Splitting Tensile Test
16%
Interfacial Transition Zone
16%
Durability Test
16%
Global Awareness
16%
Workability Test
16%
Weak Adhesion
16%
Linear Ridges
16%
L-box
16%
Flexural Strength Test
16%
Flexural Strength
16%
Engineering
Learning Approach
100%
Rubberized Concrete
100%
Learning System
100%
Rubber Waste
50%
Random Forest
33%
Bending Strength
33%
Metrics
16%
Sustainable Building
16%
Negative Impact
16%
Tensiles
16%
Sustainable Construction
16%
Building Material
16%
Splitting Tensile Strength
16%
Fine Aggregate
16%
Compacting Concrete
16%
Slump Test
16%
Interfacial Transition Zone
16%
Compressive Test
16%
Correlation Matrix
16%
Support Vector Machine
16%
Compression Strength
16%
Compressive Strength
16%
Material Science
Building Material
100%
Ultimate Tensile Strength
100%
Microstructural Analysis
100%
Flexural Strength
100%
Mechanical Testing
50%
Compressive Strength
50%
Flowability
50%
Self Compacting Concrete
50%
Strength of Materials
50%