Inconsistencies between performance data from laboratory-prepared and field samples have been widely reported. These inconsistencies often result in inaccurate condition prediction, which leads to inefficient maintenance planning. Traditional pavement management systems (PMS) do not have the appropriate means (e.g., mechanistic solutions, extensive data handling facilities, etc.) to consider these data inconsistencies. With the growing demand for sustainable materials, there is a need for more self-learning systems that could quickly transfer laboratory-based information to field-based information inside the PMS. The article aims to present a future-ready machine learning-based framework for analyzing the differences between laboratory and field-prepared samples. Developed on the basis of data obtained from field and laboratory data, the gradient-boosting decision trees-based framework was able to establish a good relationship between laboratory performance and field performance (R2test > 80 for all models). At the same time, the framework could also show more complex relationships that are often not considered in practice.@en

The lifetime of Porous Asphalt (PA) can be extended by doing preventive maintenance by means of rejuvenation products. The Dutch Road Authority for highways, Rijkswaterstaat (RWS), has constructed several test sections and monitors the development of stone loss by using the DOS/LCMS system. The monitoring runs now for 9 years and shows that in sections treated with rejuvenation products the development of raveling of PA16 is delayed for at least 3 years compared to non-treated sections. For 2-layered PA, 2LPA8 no raveling has been seen, thus no delay has been (yet) observed. Samples have been retrieved from drilled cores, which have been investigated by Rotating Surface Abrasion Test for stone loss, aging and erosion of the mortar. The results of the research are discussed. It is concluded that the method of investigation by taking cores arbitrarily on project level poorly reflect the average stone loss in a 100 m section.

@en