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

The roughness characteristics caused by pitting corrosion on the reinforcement surface have an important in-fluence on cover cracking. This study proposes two new indicators, RMPC and CMPC, for quantitatively evaluating reinforcement roughness and concavity. Then a novel approach to predicting crack volume was introduced based on ML. Results show that, RMPC is more applicable than commonly used morphological indicators for rein-forcement roughness evaluation. The dry-wet cycle corrosion produces more severe section roughness and concavity than the applied current corrosion, up to about 2.4 times. When the corrosion level exceeds 3%, average RMPC of the dry-wet cycle samples are consistently higher. When the corrosion level is less than 1%, the cross-section is typically concave. The introduction of roughness indicators significantly improves the accuracy of crack volume prediction, increasing R2 value from 0.646 to 0.956. Machine learning prediction models using ensemble learning algorithms demonstrate superior accuracy and stability compared to non-ensemble models.

Automated summary

This study proposes two new indicators, RMPC and CMPC, for quantitatively evaluating reinforcement roughness and concavity, which play an important role in predicting crack volume. Results show that RMPC is more applicable than commonly used morphological indicators for evaluating reinforcement roughness. Dry-wet cycle corrosion produces more severe section roughness and concavity than applied current corrosion, up to about 2.4 times. The introduction of roughness indicators significantly improves the accuracy of crack volume prediction, with R2 value increasing from 0.646 to 0.956. Machine learning prediction models using ensemble learning algorithms demonstrate superior accuracy and stability compared to non-ensemble models.