Determination of flexible pavement deterioration conditions using Long‐Term Pavement Performance database and artificial intelligence‐based finite element model updating
Yong Deng, Xue Luo, Yazhou Zhang, Shengxin Cai, Kai Huang, Xijun Shi, Robert L. Lytton
Abstract
This paper aims to provide a methodology in determining the deterioration conditions of flexible pavements using the Long-Term Pavement Performance (LTPP) database and artificial intelligence (AI)-based finite element (FE) model updating. A new term quantifying the effects of the aging and load repetitions on the modulus gradient of the asphalt layer was defined. The modulus gradient change was captured by a two-step calibration process. The proposed method combines the laboratory and field tests on the characterizations of the material properties and structural behaviors. Furthermore, it considers the effects of the environmental and loading conditions on the pavement behaviors and the gap between the laboratory and field tests on the same material characterizations. In this paper, the equivalent frequency in the asphalt layer for typical falling weight deflectometer (FWD) load was determined using the AI-based FE model updating as well. This paper extends the applications of the FE model updating in the pavement structures and discusses the performance of the modulus as an indicator of the pavement condition.