Quantitative characterization of reinforcement cross-sectional roughness and prediction of cover cracking based on machine learning under the influence of pitting corrosion

Keyphrases

• Machine Learning 100%
• Freeze-thaw Cycles 100%
• Quantitative Characterization 100%
• Concavity 100%
• Corrosion 100%
• Corrosion Level 100%
• Pitting Corrosion 100%
• Cover Cracking 100%
• Crack Volume 100%
• Applied Current 50%
• R2 Value 50%
• Ensemble Model 50%
• Ensemble Machine Learning 50%
• Machine Learning Prediction 50%
• Superior Stability 50%
• Superior Accuracy 50%
• Morphometric Indices 50%
• Volume Prediction 50%
• Roughness Characteristics 50%
• Roughness Measurement 50%
• New Indicators 50%

INIS

• prediction 100%
• roughness 100%
• machine learning 100%
• cracking 100%
• pitting corrosion 100%
• corrosion 66%
• indicators 50%
• levels 33%
• accuracy 33%
• volume 33%
• cracks 33%
• comparative evaluations 16%
• values 16%
• surfaces 16%
• evaluation 16%
• algorithms 16%
• increasing 16%
• stability 16%
• learning 16%
• cross sections 16%

Material Science

• Corrosion 100%
• Pitting Corrosion 100%
• Morphology 25%