Intelligent pavement moduli back-calculation using an SEM–transformer framework

This study proposes an intelligent back-calculation framework to estimate multilayer pavement elastic moduli from FWD deflection data under realistic measurement uncertainty. A spectral element method (SEM) model is used to simulate transient FWD responses and generate large-scale datasets. A Transformer regression model is trained to map peak deflection basins to layer moduli, considering four noise scenarios (no error, random, systematic, and combined). Baseline models (BPNN, SVR, and XGBoost) are also evaluated for comparison. The proposed SEM–Transformer framework achieves strong accuracy and robustness, with average R 2 >0.94 and MAPE < 8% across all noise cases, and shows superior performance for the base course under noisy conditions. The results demonstrate a reliable and efficient data-driven feasibility framework to support pavement structural evaluation and future digital-twin-based pavement management.