Pavement thickness evaluation using GPR and fuzzy logic

Abstract

Accurate pavement thickness evaluation is essential, as insufficient thickness can lead to surface distress and structural failures. However, pavement thickness is often overlooked in condition assessment models. While ground penetrating radar (GPR) offers a useful non-destructive solution, existing models are typically limited to surface layers and require significant user input. To address these challenges, this paper developed an integrated layer interface detection system and a Thickness Condition Index (TCI) using GPR and fuzzy logic. The TCI is designed to support the incorporation of thickness evaluation into pavement condition models. The developed models were tested on twelve diverse road sections from the Long-Term Pavement Performance (LTPP) database. The developed TCI enables informed decisions, facilitating efficient pavement maintenance. While the models demonstrated consistent performance, key limitations include addressing low-thickness layers and low dielectric constant contrast between layers. Future research should explore signal processing techniques, such as decomposition methods, to enhance models robustness.