Probabilistic simulation of TSD-based pavement deflections for Bayesian updating of material parameters
- Publisher:
- ELSEVIER
- Publication Type:
- Journal Article
- Citation:
- Transportation Geotechnics, 2025, 55
- Issue Date:
- 2025-11-01
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Compared to the Falling Weight Deflectometer (FWD) technology, Traffic Speed Deflectometer (TSD) provides continuous, non-destructive monitoring of pavement structural health. This feature has prompted many authorities worldwide to explore its potential in network-level pavement structural evaluation. Through parameter inference using TSD measurements, engineers can obtain physics-based evidence regarding pavement material parameters, which is crucial for informed decision-making on road operations and maintenance. However, three key challenges in existing TSD-based parameter inference have limited its practical uptake: (i) many studies introduce an intermediate correlation between TSD data and FWD data for FWD-based parameter inference, which adds extra uncertainty; (ii) conventional deterministic inference workflows yield estimates without uncertainty quantification; and (iii) high–fidelity simulations incur prohibitive computational costs, limiting real-time or near-real-time parameter inference. To overcome these gaps, this study presents a methodological framework for probabilistic parameter inference using TSD measurements. The innovation lies in the synergistic combination of: (i) a physics-based simulator, PaveMove, that directly simulates pavement responses under TSD dynamic loading, (ii) machine learning surrogates to accelerate PaveMove calculations, and (iii) Bayesian updating to transform traditional deterministic parameter inference into a probabilistic framework that explicitly incorporates multiple material and measurement uncertainties. The proposed framework is rigorously validated and compared with conventional parameter inference techniques. The results indicate that the proposed framework effectively addresses the limitations inherent in traditional techniques and provides more accurate, consistent, and reliable results of parameter inference. The proposed framework paves the way for the broader adoption of TSD technology in practice, ultimately permitting real-time, uncertainty-aware pavement management at the network scale.
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