Falling weight deflectometers (FWD) are utilised worldwide to analyse the condition and the load-bearing capacity of road pavement structures. One of the FWD measurement results, the deflection bowl, may provide surplus information that is suitable for better road pavement structure diagnostics, based on the novel approach presented in this paper. This study presents a computational method that can calculate the layer thicknesses from the deflection data recorded by the non-destructive FWD device. The motivation for this research is that FWD and GPR equipment are often not available at the same time. However, the back-calculation of the pavement layer moduli from the deflections requires knowledge of the exact thicknesses. The developed method is based on the inertia point principle and provides not only the total pavement thickness but also the total asphalt thickness at each FWD drop point. From 25,200 linear elastic layered pavement models, 350 virtual inertia points could be identified. To describe the relationship between the structural model characteristics of the pavement (thickness and subgrade modulus) and the virtual inertia points, we chose the Gaussian process regression, a widely used method in machine learning. In addition to the thicknesses, the point of inertia can also be used to calculate the bearing modulus of the subgrade with high accuracy. Based on the data from the experimental road section, the radius value of the inertia point rc is not sensitive to the stiffness of the layers that compose the pavement structure, depending only on the total pavement thickness and the bearing capacity of the subgrade. The calculation was compared with the AASHTO (1993) procedure, and very similar values for the subgrade-bearing capacity were obtained. Moreover, in the near future, the method can be further developed to provide an estimation of layer thicknesses, together with a deflection measurement, especially adapted to continuous deflection measurement devices (Curviameter and Rolling Wheel Deflectometer).