Assessing Functional Performance of Asphalt Pavements under Data Sparsity: A Probabilistic-Deterministic Approach

Abstract

Asphalt pavements, subjected to continuous traffic loads and environmental stressors, undergo deterioration processes that gradually compromise their structural and functional integrity. Pavement management systems (PMS) have been implemented to forecast deterioration and plan maintenance, but empirical models commonly used in PMS encounter limitations in data-scarce environments. To address this, a probabilistic-deterministic approach is proposed to evaluate the functional condition of in-service asphalt pavements. The international roughness index (IRI) was selected as a functional condition indicator. IRI measurement data were used for two modeling approaches: (i) a Markov chain-based probabilistic model for short-term IRI condition states and (ii) a deterministic model for long-term IRI prediction and remaining service life estimation. The probabilistic model categorizes the IRI into five condition states, while the deterministic model uses exponential regression with estimated pavement age as input. Results show that the Markov chain model effectively represents functional deterioration and provides short-term predictions without historical data. Analysis revealed a gradual decline in pavement condition with a corresponding rise in lower condition states across all functional classes. State roads exhibited an accelerated transition to lower states, highlighting the need for early interventions. A faster degradation rate was also observed once pavements declined to “fair” or “poor.” Validation confirmed that the short-term predictions were consistent with field observations, with absolute state proportion differences ranging from 0.0002 to 0.1116. The deterministic model demonstrated accurate IRI predictions, with R² ranging from 0.84 to 0.86. Consequently, integrating both models enables reliable condition evaluation and maintenance planning, regardless of historical data availability.