Print Email Facebook Twitter The road to intelligent asphalt concrete mixture design Title The road to intelligent asphalt concrete mixture design: A Data driven analysis of common asphalt concrete property prediction methods and a solution to the inverse problem Author Hopman, Luuk (TU Delft Mechanical, Maritime and Materials Engineering) Contributor Sluiter, M.H.F. (mentor) Khedoe, RN (mentor) Degree granting institution Delft University of Technology Programme Materials Science and Engineering Date 2023-06-22 Abstract Asphalt concrete is one of the most widely used materials in modern road construction. Predicting its functional properties is crucial in the design of new asphalt concrete mixtures. However, current prediction models are limited in accuracy and applicability due to the complex nature of asphalt concrete properties. This thesis researches the use of machine learning algorithms to greatly improve upon existing prediction models. The input is limited to standardized test results in line with Dutch regulations, the output focusses on functional design parameters including stiffness, fatigue resistance, water sensitivity and resistance to permanent deformations. The performance of several machine learning algorithms and the effects of different regression methods are compared. Furthermore, a solution is found for the inverse problem, which allows for greater flexibility when using the models to design new asphalt concrete mixtures. The results show that machine learning algorithms outperform traditional models on accuracy while simplifying the model input parameters. Machine learning algorithms were also able to predict a greater range of output parameters, most of which with a high accuracy. The analysed possibility of modelling asphalt concrete mixtures directly from their desired functional properties is shown to be promising. The proposed machine learning models and their inverse problem counterparts have the potential to greatly improve the accuracy and practical usability of the prediction of asphalt concrete properties, ultimately leading to better mixture design and more durable roadways. Subject Machine LearningAsphalt ConcreteMixture ModelsMaterials EngineeringDecision Tree ModelMultiple Linear RegressionInverse Problem TheoryFunctional SpecificationGradient Boosting To reference this document use: http://resolver.tudelft.nl/uuid:73655768-1b83-4666-a54d-0f8039ef48ae Part of collection Student theses Document type master thesis Rights © 2023 Luuk Hopman Files PDF MS53035_Thesis_Hopman_FINAL_v3.pdf 5.73 MB Close viewer /islandora/object/uuid:73655768-1b83-4666-a54d-0f8039ef48ae/datastream/OBJ/view