A CNN–BiLSTM–Attention-Based Deep Learning Approach for Predicting Asphalt Pavement Performance

Reliable prediction of asphalt pavement performance is essential for scientific maintenance decision-making. However, current methodologies have two primary challenges that represent significant research gaps: a heavy reliance on high-dimensional multi-source data—which is often inaccessible in resource-constrained remote regions—and the inability of traditional deep learning models to adequately capture nonlinear bidirectional temporal correlations within short-time-series pavement data. To address these limitations, this study proposes a hybrid CNN–BiLSTM–Attention architecture. The model was trained using a four-year dataset (2067 records from Xinjiang) of Pavement Condition Index (PCI) and Riding Quality Index (RQI) scores to predict fifth-year performance. Benchmarked against four state-of-the-art models, the proposed method demonstrated superior accuracy: PCI predictions achieved an R2 of 0.837 (a 1.7% improvement) and a Mean Absolute Error (MAE) of 5.31 (a 0.57% reduction) compared to the second-best model. Similarly, RQI predictions yielded an R2 of 0.855 and an MAE of 1.84, representing a 1.1% increase in accuracy and a 5.6% reduction in error, respectively. By obviating the dependency on multi-source data, this approach reduces the data acquisition and processing overhead by over 80%. Consequently, this research fills a critical gap in single-source, short-time-series prediction and provides a robust, data-driven solution for infrastructure maintenance in remote areas.