Abstract
Alkali–silica reaction (ASR) expansion prediction in concrete is crucial for assessing the long-term durability and structural performance of concrete infrastructure. This study investigates the application of hybrid Simulated Annealing–Cuckoo Search optimized machine learning algorithms for accurate prediction of ASR expansion. By combining the strengths of Simulated Annealing and Cuckoo Search optimization techniques, the hybrid approach effectively fine-tunes machine learning models, i.e., Random Forest and XGBoost, to improve their predictive capability. The optimization process explores the hyperparameter space and identifies optimal configurations that minimize prediction errors. The results demonstrate that the hybrid Simulated Annealing–Cuckoo Search optimization method significantly enhances the accuracy of ASR expansion prediction. The models developed using the hybrid approach exhibit enhanced accuracy with reduced error metrics. The predictions closely align with the actual ASR expansion values, indicating the reliability and robustness of the hybrid approach. FAST global sensitivity analysis was employed to find the factors that influence ASR expansion the most. Time, percentage of reactive aggregate, and alkali content obtained the highest sensitivity indices. The significance of this research lies in its potential applications in practical engineering scenarios, where accurate predictions of ASR expansion can inform decision-making processes regarding material selection, design choices, and the long-term durability of concrete structures. The findings contribute to mitigating ASR-related challenges and improving the overall performance and safety of concrete infrastructure.
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Parhi, S.K., Panigrahi, S.K. Alkali–silica reaction expansion prediction in concrete using hybrid metaheuristic optimized machine learning algorithms. Asian J Civ Eng 25, 1091–1113 (2024). https://doi.org/10.1007/s42107-023-00799-8
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DOI: https://doi.org/10.1007/s42107-023-00799-8