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<p>Concrete structures in coastal–industrial regions are simultaneously exposed to acid rain, carbonation, and chloride ingress; however, most durability studies investigate these mechanisms independently, limiting realistic service-life prediction. This study presents an integrated experimental, statistical, and artificial neural network (ANN)-based evaluation of M40-grade concrete subjected to synthetic acid rain, accelerated carbonation, and combined carbonation–chloride environments. Laboratory simulations were conducted for exposure durations of 20, 40, 60, and 80&#xa0;h to replicate aggressive atmospheric conditions. Mechanical degradation was assessed through compressive and splitting tensile strength tests, while chemical deterioration was quantified using depth-wise pH profiling and carbonation depth measurements. Results revealed that compressive strength decreased by up to ~ 25% under acid rain exposure and showed greater deterioration under combined carbonation–chloride conditions. Splitting tensile strength exhibited higher sensitivity to degradation, with reductions exceeding 20% at prolonged exposure durations. Surface pH declined from 12.6 to approximately 8.8, confirming rapid alkalinity loss in the outer 1&#xa0;cm zone. Carbonation depth increased progressively, reaching ~ 11.5&#xa0;mm in the combined exposure environment. Regression analysis demonstrated strong inverse correlations (R<sup>2</sup> &gt; 0.90) between carbonation depth and mechanical properties, while one-way ANOVA confirmed statistically significant differences across exposure durations (<i>p</i> &lt; 0.05). To enhance predictive capability, an ANN model was developed using experimental parameters as inputs, achieving high prediction accuracy (R<sup>2</sup> = 0.98 for training and R<sup>2</sup> = 0.96 for testing). The findings highlight synergistic deterioration effects of multi-environmental exposure and emphasize durability-based design strategies for reinforced concrete structures in aggressive environments.</p>

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Integrated experimental, statistical, and ANN validation study on concrete deterioration under acid rain, carbonation, and chloride environments

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月15日

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  • Y. H. Sudeep,
  • V. Vijaykumar,
  • M. S. Ujawl,
  • Christo George,
  • S. Karthik,
  • Rakesh Kumar,
  • Gopal Bharamappa Bekkeri,
  • Kiran K. Shetty

摘要

Concrete structures in coastal–industrial regions are simultaneously exposed to acid rain, carbonation, and chloride ingress; however, most durability studies investigate these mechanisms independently, limiting realistic service-life prediction. This study presents an integrated experimental, statistical, and artificial neural network (ANN)-based evaluation of M40-grade concrete subjected to synthetic acid rain, accelerated carbonation, and combined carbonation–chloride environments. Laboratory simulations were conducted for exposure durations of 20, 40, 60, and 80 h to replicate aggressive atmospheric conditions. Mechanical degradation was assessed through compressive and splitting tensile strength tests, while chemical deterioration was quantified using depth-wise pH profiling and carbonation depth measurements. Results revealed that compressive strength decreased by up to ~ 25% under acid rain exposure and showed greater deterioration under combined carbonation–chloride conditions. Splitting tensile strength exhibited higher sensitivity to degradation, with reductions exceeding 20% at prolonged exposure durations. Surface pH declined from 12.6 to approximately 8.8, confirming rapid alkalinity loss in the outer 1 cm zone. Carbonation depth increased progressively, reaching ~ 11.5 mm in the combined exposure environment. Regression analysis demonstrated strong inverse correlations (R2 > 0.90) between carbonation depth and mechanical properties, while one-way ANOVA confirmed statistically significant differences across exposure durations (p < 0.05). To enhance predictive capability, an ANN model was developed using experimental parameters as inputs, achieving high prediction accuracy (R2 = 0.98 for training and R2 = 0.96 for testing). The findings highlight synergistic deterioration effects of multi-environmental exposure and emphasize durability-based design strategies for reinforced concrete structures in aggressive environments.