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Models for property prediction of pervious concretes
Omkar Deo
, Milani S. Sumanasooriya
,
Narayanan Neithalath
Civil and Environmental Engineering
Adaptive Intelligent Materials and Systems Center (AIMS)
AIMS Consortium
Sustainability Initiative
Sustainable Engineering and the Built Environment, School of (IAFSE-SEBE)
Enhancing Concrete Life in Infrastructure through Phase Change Systems (ECLIPS)
Bio-Mediated and Bio-Inspired Geotechnics, Center for (CBBG)
Research output
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Chapter in Book/Report/Conference proceeding
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Conference contribution
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Keyphrases
Pore Structure
100%
Property Prediction
100%
Pervious Concrete
100%
Structure Feature
85%
Statistical Model
14%
Three-dimensional (3D)
14%
Monte Carlo Simulation
14%
Structure-property Relationships
14%
Pore Clogging
14%
Material Structure
14%
Unconfined Compressive Strength
14%
Concrete Mix
14%
Pore Size
14%
Compressive Strength
14%
3D Geometry
14%
Particle Trapping
14%
Probablistic
14%
Particle Capture
14%
Image Analysis Technique
14%
Concrete System
14%
Specific Surface Area
14%
Pore Distribution
14%
Permeability Reduction
14%
Capture Model
14%
Clogging Substance
14%
Performance Features
14%
Distribution Density
14%
Material Addition
14%
Concrete Section
14%
Permeability Prediction
14%
Planar Images
14%
Katz-Thompson Equation
14%
Pore Area Ratio
14%
Sensitivity Evaluation
14%
Simulated Runoff
14%
Particle Retention
14%
Random Porous Media
14%
Feature Sensitivity
14%
Engineering
Pervious Concrete
100%
Compression Strength
33%
Compressive Strength
33%
Base Model
16%
Property Relationship
16%
Image Analysis
16%
Specific Surface Area
16%
Addition Material
16%
Distribution Density
16%
Porosity
16%
Statistical Model
16%
Concrete Mixture
16%
Material Science
Pore Structure
100%
Compressive Strength
28%
Density
14%
Materials Structure
14%
Concrete Mixture
14%
Pore Size
14%