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This study explores the optimization of machine learning models using Optuna to predict the strength and crack behavior of prestressed concrete beams. The research demonstrates that hyperparameter tuning significantly enhances prediction accuracy, with the LightGBM model achieving an R² value over 0.98 for strength prediction. Additionally, the integration of SHAP for model interpretability provides insights into feature contributions, improving the overall reliability of the predictions.

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23eg201b02

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EN