Conceptual Model for Integrating Field Compaction Data into Mechanistic-Empirical Pavement Design Systems
DOI:
https://doi.org/10.32628/IJSRCE248068Keywords:
field compaction, mechanistic–empirical pavement design, resilient modulus, intelligent compaction, digital twins, performance prediction, adaptive maintenanceAbstract
The quality of field compaction plays a critical role in the structural performance and durability of pavements. Variability in compaction can lead to significant differences in layer stiffness, permanent deformation, fatigue resistance, and overall pavement service life. Traditional mechanistic–empirical (M–E) pavement design frameworks often rely on laboratory-measured material properties and assume uniform construction quality, limiting their ability to account for in-situ variability. This study proposes a conceptual model for integrating field compaction data directly into M–E design systems, enabling more accurate and performance-based pavement predictions. The model incorporates compaction measurements from nuclear density gauges, intelligent compaction (IC) systems, and roller-integrated measurement systems (RIMS) into constitutive models of stabilized soils and granular layers. These field-derived inputs adjust stress–strain relationships, resilient modulus, and permanent deformation parameters to reflect actual construction conditions. A layered architecture is proposed, including an input layer for field data, a mechanistic analysis layer for structural response simulation, a performance prediction layer for fatigue, rutting, and serviceability evaluation, and an optimization layer for adaptive design and life-cycle cost assessment. Feedback loops from real-time monitoring and post-construction performance data facilitate continuous refinement and calibration of the model. Integration with digital twins, sensor networks, and machine learning algorithms is highlighted to enhance predictive accuracy and enable adaptive maintenance planning. Case studies across highways, urban roads, and heavy-duty pavements demonstrate the potential of the framework to improve performance prediction, reduce maintenance interventions, and enhance construction quality control. The proposed conceptual model provides a pathway toward more resilient, cost-effective, and sustainable pavement infrastructure by explicitly incorporating field compaction variability into mechanistic–empirical design methodologies.
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