Evaluating the Structural Capacity of Flexible Pavements (1B)
Accurate assessment of pavement structural capacity is crucial for effective and sustainable road asset management. However, traditional Pavement Management Systems (PMS) often rely on surface condition indicators and limited structural data. This pilot project develops a framework to embed data-driven asset management principles within Pavement Management Systems (PMS). By leveraging Traffic Speed Deflectometer (TSD) technology, it integrates pavement’s structural capacity into the process of maintenance strategy selection.
The framework guides the full analytical process, from data preparation and filtering to calculating a structural index, normalizing for temperature and speed effects, and assigning class and score values to each road section. Because there is no international standard for TSD data processing, new methods were developed, including AI models for anomaly detection and subsurface temperature estimation, physics-based normalization techniques, and an integrated classification and scoring system.
To support practical implementation, the framework combines Python-based analytical modules, SQL data integration, and QGIS visualization tools into one seamless workflow, transforming raw sensor data into actionable insights for decision-making.
This approach bridges the gap between network-level assessments and project-level planning. It enables road authorities to design smarter, more efficient, and more sustainable pavement management strategies for the future.
