Bedrock Identification and Bedrock Depth Prediction in Asphalt Pavements Using Pavement System Transfer Function

Abstract

To determine optimal road maintenance and repair schedules, road agencies need to regularly evaluate asphalt pavement performance during both construction and operation. It usually involves back-calculating the pavement’s deflection responses to obtain modulus for each structural layer. However, bedrock under the subgrade can significantly affect this analysis. To enhance the accuracy of back-calculation, this study proposed bedrock depth prediction models based on pavement system transfer function (PSTF) aided by falling weight deflectometer (FWD) tests. To provide sufficient data for model development, a spectral element method with fixed-end boundary conditions (B-SEM) was used to calculate the deflection responses of various pavement structures under different bedrock conditions. Based on the transfer function (TF) theory of linear time-invariant (LTI) systems, Fourier transform (FT) was used to process time-domain data, resulting in the PSTF for each pavement structure, which was then used as the dataset. This study also analyzed the amplitude spectrum characteristics of PSTFs under different bedrock depths and proposed methods for identifying bedrock under the subgrade. A bedrock depth prediction model (PSTF-BD) based on the PSTF was developed using the results of the sensitivity analysis. The model’s performance was comprehensively evaluated using various error metrics. The results indicate that the PSTF-BD model demonstrates high accuracy in predicting bedrock depth. Specifically, the PSTF-BD (B) model achieves a correlation coefficient of 99.6%, with an average error of no more than 1.0% for the prediction results of the validated dataset. Compared to existing prediction models, the PSTF-BD model improves correlation by at least 6.4% and prediction accuracy by at least 34.1%. Furthermore, the PSTF-BD model offers superior predictive performance and is well-suited for engineering applications, showcasing significant potential for widespread adoption in road engineering projects.