The Netherlands is a northwestern European country. The location makes the Netherlands an important entry for trade to the European continent resulting in high traffic intensities [1]. The Netherlands has one of the densest road networks in the world, having over 139.000km of roadways [2, 3]. The understanding of the link between the functional properties and the behavior of the road is crucial [4]. However, predicting such properties is still challenging, and expectations differ from the results found in the field [5, 4]. Data-driven approaches have been part of pavement engineering for decades and have shown to be a powerful tool for performance prediction [6]. This thesis aims to develop a machine learning framework for predicting stiffness, fatigue resistance, resistance to permanent deformation, and water sensitivity. Along with sensitivity analysis to understand the deviations from expectation. The objective is to develop a framework to understand the applicability of machine learning tools and the impacts of the composition parameters into the mix. The models developed applied three different machine learning tools for regression: Support Vector Machine, Random Forests, and Gradient Boosting. The models were compared to a statistical model to validate the work. The statistical approach was a Multiple Linear Regression. All developed models used the database from the NL-LAB project. The machine learning models were compared, and the best-performing proceeded to a sensitivity analysis. The sensitivity analysis used SHAP values, which derive from the Games Theory and have shown to be powerful tools for complex model interpretability [7]. The models had good accuracy prediction. For most properties, machine learning had a higher performance than the statistical model. Gradient Boosting performed the best from the machine learning tools and was selected to finalize the research. The sensitivity analysis had good results, confirming some of the expectations and setting a precedent for researching others.

The use of synthetic fibres has been reported to enhance the performance of asphalt pavement materials in terms of permanent deformation, fatigue and thermal cracking. However, limited results about the benefits of synthetic fibres in the reinforced warm-mix asphaltic materials, and the exact mechanism of reinforcing the binding part in pavement structures is still unclear. This research aims firstly to examine the material at the warm mixed mortar level using a combination of two synthetic fibres (aramid and polyolefin) to conclude its fracture performance. Several laboratory tests were performed using specially designed experimental tools. Samples of three different fibre contents (0.05%, 0.1% and 0.5% of specimen weight) and two fibre lengths (19 and 38mm) were evaluated. In particular, pull-out tests, whose objective was to explore the interaction of fibre-matrix demonstrated a matrix-type of fracture; meaning that the adhesion of fibre-matrix is higher than the strength of the matrix itself, which implies a benefit of adding fibre to a mixture at high service temperature.
Summary
Moreover, direct tension tests were carried out with both monotonic and cyclic loading to measure the effect of the synthetic fibres on tensile strength, fracture energy and fatigue life of reinforced warm mixes under monotonic and cyclic tension load, respectively. These tension experiments concluded improvements on mechanical characteristics of warm mixed asphalt mortars when fibres were added, mainly applying higher dosages than the recommended by the fibres supplier. Overall, the current results elucidated that implementing dedicated material studies at micro-scales can assist on understanding the material performance and tailoring systems beyond sometimes recommended reinforcement dosages by the suppliers. Finally, a semi-circular bending test was performed as the largest scale of this research using various fibre amount composition as well as fibre length inside the bituminous mix, and the final results mainly correspond with the other examinations that have also been conducted. Therefore, the research methodology utilised in this thesis has been able to examine the reinforcement effect brought by the integration of synthetic fibre to failure performance of the warm mixed asphaltic mixture specifically regarding the cracking resistance extensively.