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This study introduces an auto-tune learning framework designed to predict the properties of ultra-high-performance concrete (UHPC), such as compressive strength, flexural strength, workability, and porosity. The framework integrates structured and unstructured data, employs anomaly detection to enhance dataset quality, and optimizes machine learning hyperparameters using advanced techniques. Results indicate that the proposed method significantly improves prediction accuracy and can facilitate material deve

Author

bundien123

Language

EN