The main goal of the study was to discover knowledge from data about asphalt road pavement problems to achieve a better understanding of the behavior of them and via this understanding improve pavement quality and enhance its lifespan. Four pavement problems were chosen to be investigated; raveling of Porous Asphalt Concrete (PAC), cracking of Dense Asphalt Concrete (DAC), rutting of dense asphalt concrete, and determination of the stiffness of Cement Treated Bases (CTBs). At the moment, almost 75% of the Dutch motorways network has a PAC top layer. Raveling is the most dominant type of damage of PAC top layers. The DAC top layers which are mainly applied to the secondary roads in the Netherlands are the most commonly used top layers worldwide. The two main damage types of this top layer are cracking and rutting. Determination of the stiffness of the cement treated base layer stiffness is not an easy task. Therefore, a tool which can accurately calculate the stiffness of such base layers is desirable. Concerning data, the SHRP-NL databases provided the data for the three surface damages, being ravelling of PAC, cracking and rutting of DAC. The data for climate and traffic were obtained from databases of the Royal Dutch Meteorological Institute (KNMI), the Ministry of Transport and Water Management, and different provinces of the Netherlands. The data for the stiffness of CTBs was simulated using the multilayer linear-elastic computer program BISAR. During preparation of the data, the determination of outliers was a challenging task. Due to the low number of data points available for raveling, cracking, and rutting (in one case around 70 data points), an extensive variable selection was performed using eight different methods: decision trees, genetic polynomial, artificial neural network, rough set theory, correlation based variable selection with bidirectional and genetic search, wrappers of neural network with genetic search, and relief ranking filter. For development of models (data mining) from the mentioned data, four machine learning based techniques were employed. Two were prediction techniques; artificial neural networks and support vector machines. The other two were rule based techniques; decision trees and rough set theory. This study resulted in 20 intelligent models for the mentioned four problems