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First prediction tool published
Our new paper presents a physics-based machine learning model for predicting asphalt test results, combining a data-driven approach with physical constraints to improve reliability and interpretability. The main contribution is the methodology itself: a framework that leverages existing experimental data while remaining consistent with known material behavior. Although we demonstrate the approach using the Marshall test, this choice was driven…