A sensing-information-statistics integrated model to predict asphalt material density with intelligent compaction system

Abstract

Intelligent compaction (IC) is an innovative technology that has been used in road and earthwork construction. However, the current IC technology is unable to measure material density directly as the acceptance criteria by owner agencies. To tackle this issue, the authors have developed a sensing-information-statistics integrated model to predict asphalt material density for 100% coverage of construction area. Instrumented with the satellite navigation system, accelerometer, and infrared sensors, IC rollers measure mechanical responses of roller drums and material temperature in real time. With these measurements, panel data models—including both the multivariate linear and nonlinear models—were developed to predict asphalt material density. A reasoning model was proposed to estimate idiosyncratic errors due to uncertainty of measurements. An information management software was developed to analyze IC measurements with univariate statistics and geo-statistical models. Statistical models were implemented and validated with data collected from four paving projects in the US. Results indicate that the multivariate nonlinear panel data model can predict asphalt material density at the project level for 100% coverage of the construction zone within reasonable accuracy. Therefore, this model may serve as an enhanced quality control and acceptance tool for asphalt pavement construction to improve consistency and uniformity and long-term performances.