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<p>Accurate and early prediction of the onset of corrosion in reinforced concrete (RC) structures is vital for designing sustainable and resilient RC structures, which will have reduced carbon footprints, reduced depletion of natural resources, and effective utilization of waste materials. In view of the same, this study investigates the durability performance and corrosion initiation in concrete blended with Portland slag cement (PSC) and microsilica. The concrete cube samples with varying microsilica content (0%, 5%, 10%, 15%, and 20%), featuring centrally positioned bars, underwent accelerated corrosion under extreme chloride conditions (5% NaCl) for various durations, up to 105&#xa0;days, following ASTM C876 guidelines. Based on experimental output and scanning electron microscope (SEM) analysis, samples with 10% microsilica demonstrated the highest corrosion resistance due to the formation of high-calcium silicate hydrate (C–S–H) gel crystals. In this study, the experimental results have also been validated by employing machine learning (ML) algorithms including support vector machine (SVM), artificial neural network (ANN), random forest (RF), and&#xa0;Gaussian process (GP). Comparative analysis between the ML model prediction and experimental results revealed that the RF and ANN models yielded similar output with minimal error percentage, as demonstrated by the coefficient of correlation (R2) values of 1 and 0.99 and 0.99 and 0.98 in the training and testing phases, respectively. In the authors' opinion, PSC partially substituted with microsilica shows significant potential for developing sustainable concrete mixes suitable for extreme chloride environments.</p>

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Prediction of Corrosion Initiation Time in Sustainable Concrete Blended with Microsilica and Portland Slag Cement Under Extreme Chloride Conditions

  • 期刊论文

  • 发表日期：

    2025年2月27日

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  • Amgoth Rajender,
  • Amiya K. Samanta

摘要

Accurate and early prediction of the onset of corrosion in reinforced concrete (RC) structures is vital for designing sustainable and resilient RC structures, which will have reduced carbon footprints, reduced depletion of natural resources, and effective utilization of waste materials. In view of the same, this study investigates the durability performance and corrosion initiation in concrete blended with Portland slag cement (PSC) and microsilica. The concrete cube samples with varying microsilica content (0%, 5%, 10%, 15%, and 20%), featuring centrally positioned bars, underwent accelerated corrosion under extreme chloride conditions (5% NaCl) for various durations, up to 105 days, following ASTM C876 guidelines. Based on experimental output and scanning electron microscope (SEM) analysis, samples with 10% microsilica demonstrated the highest corrosion resistance due to the formation of high-calcium silicate hydrate (C–S–H) gel crystals. In this study, the experimental results have also been validated by employing machine learning (ML) algorithms including support vector machine (SVM), artificial neural network (ANN), random forest (RF), and Gaussian process (GP). Comparative analysis between the ML model prediction and experimental results revealed that the RF and ANN models yielded similar output with minimal error percentage, as demonstrated by the coefficient of correlation (R2) values of 1 and 0.99 and 0.99 and 0.98 in the training and testing phases, respectively. In the authors' opinion, PSC partially substituted with microsilica shows significant potential for developing sustainable concrete mixes suitable for extreme chloride environments.