Real-Time Embedded Smart-Particle Monitoring for Index-Based Evaluation of Asphalt Mixture Compaction Quality.

Area of Science:

• Civil Engineering
• Materials Science
• Geotechnical Engineering

Background:

• Asphalt pavement durability relies heavily on compaction quality.
• Traditional density checks are intermittent, hindering real-time control.
• Reliable compaction requires monitoring internal responses, not just final density.

Purpose of the Study:

• Develop an embedded smart-particle framework for in situ monitoring and evaluation of vibratory compaction quality.
• Integrate multi-source sensing, feature extraction, and compaction degree mapping.
• Provide real-time feedback for intelligent compaction.

Main Methods:

• Smart particles integrating inertial/orientation and thermal-mechanical sensing were developed.
• High-temperature survivability and calibratability were verified.
• Raw signals were filtered for stable attitude responses, and descriptors were extracted.
• Data-driven mapping from internal responses to compaction degree was established using regression models.

Main Results:

• Smart particles remained stable under hot-mix asphalt conditions with high linearity in calibration (R² > 0.990 for temperature, R² > 0.980 for force).
• Filtered inertial signals improved usability and interpretability during compaction.
• Attitude feature evolution correlated with compaction degree (|r| ≈ 0.35-0.47).
• Nonlinear regression models achieved strong predictive performance (R² up to 0.960, RMSE down to 0.016).

Conclusions:

• Embedded smart particles are feasible for online compaction quality evaluation.
• The framework enables index-based assessment of vibratory compaction.
• Distinct mixture-dependent characteristics were identified, informing future intelligent compaction strategies.