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<p>This study presents a mathematical model for the kinetics of the alkali–silica reaction (ASR), aiming to enhance predictive understanding of ASR gel formation in cementitious systems. A system of nonlinear kinetic equations, derived from the underlying chemistry of ASR in concrete, is analytically solved using the Rajendran–Joy method. The closed-form solution effectively captures the temporal evolution of ASR gel and exhibits excellent agreement with numerical simulations including the previously available results for experimental value of parameters, confirming its validity and robustness. A local normalized sensitivity analysis evaluates parameter impacts on experimental model values. Comprehensive parametric studies reveal how reaction rate constants and initial reactant concentrations drive ASR gel growth dynamics in concrete, aiding durability predictions and mix design optimization.</p>

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Coupled Nonlinear Kinetics Models for Predicting Alkali–Silica Reaction in Cementitious Systems: Analytical and Numerical Perspectives

  • 期刊论文

  • 发表日期：

    2026年3月23日

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  • L. Rajendran,
  • A. Marimuthu,
  • K. V. Tamil Selvi,
  • Navnit Jha

摘要

This study presents a mathematical model for the kinetics of the alkali–silica reaction (ASR), aiming to enhance predictive understanding of ASR gel formation in cementitious systems. A system of nonlinear kinetic equations, derived from the underlying chemistry of ASR in concrete, is analytically solved using the Rajendran–Joy method. The closed-form solution effectively captures the temporal evolution of ASR gel and exhibits excellent agreement with numerical simulations including the previously available results for experimental value of parameters, confirming its validity and robustness. A local normalized sensitivity analysis evaluates parameter impacts on experimental model values. Comprehensive parametric studies reveal how reaction rate constants and initial reactant concentrations drive ASR gel growth dynamics in concrete, aiding durability predictions and mix design optimization.