Data for 'Feasibility of determining asphalt pavement condition from falling weight deflectometer test and finite element model updating'

Determination of pavement internal condition from nondestructive field test is a persistent topic for its practical necessity and difficulty. It is essentially an inverse problem calibrating pavement material and structural properties from pavement responses. Considering the intrinsic complexity of asphalt pavement materials (e.g. time and temperature dependencies of asphalt mixture and stress dependency of unbound granular materials), this problem has become a typical high-dimensional optimization problem with many parameters to be calibrated. This study investigated the feasibility of artificial intelligence-based finite-element model updating in addressing this problem, and focused accuracy as well as stability of the backcalculated results. For a comprehensive evaluation of this method, effects of its components such as the surrogate model representing the pavement system, applied pavement response, optimization algorithm and backcalculation scheme were characterized. Finally, we found the sensitivity of applied pavement responses to the backcalculated pavement condition, the number of applied pavement responses and the balance between the backcalculated pavement condition and applied test were of significant importance to accurate and stable backcalculation results. Corresponding modifications were recommended to be conducted in future research for improving the performance of the proposed backcalculation method.This article is part of the theme issue ‘Artificial intelligence in failure analysis of transportation infrastructure and materials’.