A Hybrid Polynomial Chaos Expansion-Based Surrogate Framework for Reliability and Interpretability of Concrete Compressive Strength

Keyphrases

• Boosted Trees 16%
• Classical Statistical 16%
• Compressive Strength 16%
• Concrete Compressive Strength 100%
• Concrete Mix 16%
• Concrete Strength 16%
• Curing Age 16%
• Data-driven Surrogates 16%
• Design Optimization 16%
• Expansion Model 16%
• Experimental Database 16%
• Extractor 16%
• Global Reliability 16%
• Global Sensitivity 33%
• Global Sensitivity Analysis 33%
• Gradient Boosted Trees 66%
• Hybrid Framework 16%
• Hybrid Gradient 16%
• Hybrid Model 16%
• Hybrid Polynomials 100%
• Index Assessment 16%
• Kriging Surrogate Model 16%
• Latin Hypercube Sampling 16%
• Machine Learning Models 16%
• Mix Composition 16%
• Mix Optimization 16%
• Multi-source 16%
• Nonlinear Mapping 16%
• Numerical Stability 16%
• Polynomial Chaos Expansion 100%
• Polynomial Chaos Kriging 16%
• Prediction Accuracy 33%
• Probabilistic Analysis 16%
• Reliability Analysis 16%
• Reliability Assessment 16%
• Reliability Metrics 16%
• Reliability-based Design 16%
• Risk-based Design 16%
• Sobol 16%
• Statistical Surrogates 16%
• Surrogate Model 16%
• Tree Response 16%
• Uncertainty Quantification 16%

Engineering

• Compression Strength 50%
• Compressive Strength 50%
• Concrete Compressive Strength 100%
• Concrete Mixture 50%
• Concrete Strength 50%
• Extractor 50%
• Hybrid Model 50%
• Interpretability 100%
• Latin-Hypercube Sampling 50%
• Learning System 50%
• Metrics 50%
• Model Tree 50%
• Reliability Analysis 50%
• Reliability Assessment 50%
• Reliability Based Design 50%
• Surrogate Model 50%
• Uncertainty Quantification 50%