Study on an Intelligent Monitoring and Assessment Method for Asphalt Pavement Compaction Based on Roadside Vibration Acceleration

ABSTRACT To address the delayed feedback of asphalt pavement compaction testing, the destructive and discontinuous nature of conventional detection methods, and the susceptibility of vibration roller axle acceleration signals to mechanical‐structure vibration interference, this paper proposes an intelligent monitoring and assessment method for asphalt pavement compaction based on roadside vibration acceleration. First, the propagation characteristics of vibration energy from the compaction zone to the roadside during vibratory compaction are analyzed. The mean vibration acceleration amplitude over 20 vibration cycles is selected as the effective value of roadside vibration acceleration, denoted as F, and the Grubbs criterion and a finite impulse response (FIR) filter are used for outlier removal and noise reduction. Second, a finite element model of asphalt pavement vibratory compaction is established in Abaqus. The effective roadside vibration acceleration F and detection distance D under different compaction states are extracted, and a multivariate quadratic FDK regression model relating F, D, and the compaction degree K is developed. Finally, field tests are conducted based on the Chuzhou G328 maintenance project. The results show that the coefficient of determination of the FDK regression model reaches 0.979. Within the effective detection range of 0–1.5 m, the error between the predicted compaction degree and the core‐sampling measurement does not exceed 0.5%. The results indicate that the roadside vibration monitoring method is nondestructive, low‐cost, easy to deploy on site, and less affected by mechanical interference from the roller, providing a reference for dynamic quality control in asphalt pavement construction and intelligent road monitoring.