The asphalt test result database
Predict asphalt performance from real test data
AsphaltMine collects laboratory results from European partners in one organized database and uses them to train machine learning models that reduce the need for physical testing.
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First prediction model 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…
3000+
Marshall test results
6
Participating countries
2
Prediction models
What you can do with AsphaltMine
Contribute your results, explore what others have shared, and use the prediction tools.
Contribute data
Enter records with a web form or upload many at once from an Excel template. You decide who can see your data.
Explore and visualize
Search records, download results, and see live statistics across tests, mixtures and years.
Predict performance
Estimate Marshall stability and flow, and bulk density and air voids, from mixture properties.
Developed at the Empa Concrete and Asphalt Laboratory and funded by the Swiss National Science Foundation as part of the WEAVE project No.213163: “Fate of Polymers in Recycled Asphalt: a Multiscale Approach”
Join the AsphaltMine community
We can not do this alone. Contribute your asphalt test results and get access to all the database functionalities and features.