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Intelligent pavement moduli back-calculation using an SEM–transformer framework
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DOI：10.3389/fmats.2025.1732297.png)
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摘要
En 中文
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 R2>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.
Keyword:
transformer
SEM
data-driven modeling
FWD
intelligent back-calculation
intelligent maintenance
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期刊
F
IF:
2.9
论文数:
389
被引数:
0
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引用论文
Bayesian backcalculation of pavement properties using parallel transitional Markov chain Monte CarloOAAI
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Improvements to the structural condition index (SCI) for pavement structural evaluation at network level网络层面路面结构评价的结构状态指数 (SCI) 的改进
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