Ranking of Deflection Bowl Parameters based on Correlations with Critical Strains in Pavement Layers

Abstract: The structural condition of pavements is typically assessed using deflections measured by Falling Weight Deflectometer (FWD) equipment. Deflection Bowl Parameters (DBPs) derived from these deflections serve as valuable tools for evaluating the impact of individual pavement layers on various types of distress, such as cracking and rutting. Common DBPs include the Surface Curvature Index (SCI), Base Layer Index (BLI), and Lower Layer Index (LLI), which assess the condition of the asphalt layer, granular layer, and subgrade, respectively. Recent research also highlights the Area Under Pavement Profile (AUPP) and area parameters as effective indicators of pavement layer conditions. While numerous DBPs exist to evaluate pavement layers such as SCI, AUPP, and area parameters for asphalt layers, the most reliable DBP for assessing pavement condition remains unclear. This study aims to rank various DBPs based on their correlation with critical strains specifically horizontal tensile strain at the bottom of asphalt layer and vertical compressive strain at the top of the subgrade estimated using KENPAVE software. To achieve this goal, deflection data was collected through FWD testing from different highways in New Jersey with asphalt concrete on varying base layers. Following data collection, back-calculation was performed to estimate the moduli of different layers within the pavement system. Using these moduli values, critical strains in the pavement layers were then estimated using KENPAVE software. Additionally, several bowl parameters, including SCI, AUPP, Modified AUPP, area parameters, BDI, and LLI, were calculated from the surface deflection data. DBPs representing the asphalt layer (SCI, AUPP, Modified AUPP, and area parameters) were correlated with the horizontal tensile strain in asphalt layers, while those representing the granular and subgrade layers were linked to the vertical strain at the top of the subgrade. The load and temperature sensitivities of DBPs were studied using the deflection data generated from KENPAVE. The analysis revealed that the AUPP parameter had the strongest correlation with horizontal tensile strain in asphalt layers, ranking first, followed by SCI. Conversely, LLI ranked first in its correlation with the vertical strain on top of the subgrade layer. Following the load and temperature sensitivity analysis of these parameters, simplified power models were proposed to predict the tensile strain at the bottom of asphalt layer and vertical strains at the top of subgrade using AUPP and LLI parameters, respectively.