This study introduces a novel deep learning framework designed to predict the compressive strength of high-performance concrete (HPC) by leveraging advanced optimization algorithms to tune neural network hyperparameters. The results demonstrate that the integrated model significantly outperforms standard machine learning techniques, providing a more accurate and reliable tool for civil engineers to optimize material safety and cost-efficiency.
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