This paper presents a brief overview of artificial intelligence applications in transportation geotechnics, highlighting new approaches and current research directions, including issues related to data mining interpretability and prediction capacities. Several practical applications to earthworks, including the compaction management and quality control aspects of embankments, as well as pavement evaluation, design and management, and the mechanical behaviour of jet grouting material, are presented to illustrate the advantages of using data mining, including artificial neural networks, support vector machines, and evolutionary computation techniques in this domain. This study also propose a novel simplified compaction table for reusing geomaterials and compaction management in embankments and applied one-and two-dimensional advanced sensitivity analyses to better interpret the proposed data-driven models for the prediction of the deformability modulus of jet grouting field samples. These applications show the capabilities of data mining models to address complex problems in transportation geotechnics involving highly nonlinear relationships of data and optimisation needs.