Backcalculation of damage density of in‐service asphalt pavements using artificial intelligence‐based finite element model updating
Yong Deng, Xue Luo, Hang Wang
Abstract
Abstract Fatigue damage is a common distress significantly affecting asphalt pavements in the performance, economy, and safety aspects. Accordingly, this paper aims to propose a methodology to backcalculate a continuum damage mechanics (CDM)‐based damage density of in‐service asphalt pavements from field nondestructive testing (NDT). An artificial intelligence (AI)‐based finite element (FE) model updating was applied with the falling weight deflectometer (FWD) test results for the calibrations of the damage density and supporting layer moduli. The development of the damage density with pavement service time was categorized and fitted with proposed analytical models. The damage density was compared with field observations on the fatigue cracking performance of asphalt pavements. It was found that the damage density had close relations with the material type, structural configuration, and fatigue cracking area of the pavement. This study shows a promising application of the damage density in the evaluation and prediction of the fatigue damage of asphalt pavements, which will lead to a timely and cost‐effective pavement maintenance and rehabilitation.