While having been successfully used for soil compaction for many years, intelligent compaction (IC) technology is still relatively new for asphalt pavement construction. The potential of using intelligent compaction meter value (ICMV) for evaluating the compaction of asphalt pavements has been hindered by the fact that ICMV can be affected by many factors, which include not only roller operation parameters, but also the temperature of asphalt layer and the underlying support. Therefore, further research is necessary to improve the application of IC for the asphalt compaction. In this study, the feasibility of IC for asphalt compaction was evaluated from many aspects. Based on that, a laboratory IC technology for evaluating asphalt mixture compaction in the laboratory was also developed. In this study, one field project for soil compaction was constructed using IC technology, and a strong and stable linear relationship between ICMV and deflection could be identified when the water content of soil was consistent. After that, more field projects for asphalt compaction were constructed using the IC asphalt roller. The density of asphalt, as the most critical parameter for asphalt layers, along with other parameters, were measured and correlated with the ICMVs. Various factors such as asphalt temperature and the underlying support were considered in this study to improve the correlation between the density and ICMV. Based upon the results of correlation analyses, three IC parameters were recommended for evaluating the compaction quality of resurfacing project. In addition, the geostatistical analyses were performed to evaluate the spatial uniformity of compaction, and the cost-benefit analysis was included to demonstrate the economic benefits of IC technology. Based on the test results of field projects, the IC indices were further utilized to quantify the lab vibratory compaction for paving materials. The compaction processes in the laboratory was monitored by accelerometers. Using Discrete-Time Fourier Transform, the recorded data during compaction were analyzed to evaluate the compactability of paving materials and to further correlate to the field compaction.

Effective compaction is crucial for the performance and durability of asphalt pavement. Traditional field compaction, relying heavily on engineers' experience and test strips, sometimes could be problematic to achieve a unified pavement with a desirable density, especially with new materials. To address these challenges, Intelligent Compaction (IC) has been developed to equip the vibratory rollers with GPS, accelerometers, onboard computers, and infrared thermometers to facilitate the quality control of pavement compaction. This technology allows for real-time monitoring and visualization of pavement responses and temperatures, significantly improving compaction uniformity. However, accurately predicting pavement density remains challenging due to the multilayered pavement structure and the complex interactions between the roller drum and the viscoelastic asphalt mixture. To understand the compaction mechanism and improve the compaction quality of the asphalt pavement, a Microelectromechanical System (MEMS) sensor, SmartKli was employed to study the asphalt mixture compaction at the mesoscale. It was found that the compaction characteristics at the macroscale are closely related to the behavior of coarse aggregates at the mesoscale level. The particle rotation plays a critical role in the densification of the asphalt specimens. Utilizing the Discrete Element Model (DEM), the impact of mix design and particle property on kinematic behaviors was examined. The mixture gradation and particle size also greatly affect the aggregates' behavior during compaction. Based on the developed compaction mechanism, a new method for evaluating asphalt mixture workability was proposed, incorporating workability parameters, compaction curves, and statistical analysis of compaction data. By verifying with different asphalt types including Hot Mix Asphalt (HMA), Warm Mix Asphalt (WMA), and Recycled Plastic Modified Asphalt (RPMA), this method could effectively assess the influence of various factors like asphalt content, compaction temperature, and additives on mixture workability, aiding in optimizing mix design and construction conditions. Moreover, an innovative compaction monitoring system was developed to accurately predict the compaction conditions of the asphalt pavement. This system uses a wireless particle size sensor for data acquisition and a machine learning model for density prediction. Linking laboratory gyratory and field roller compaction data through particle kinematic behaviors, the system achieved high precision in density prediction with a prediction error of less than 0.7%. The results demonstrate that integrating AI and sensing data is effective for predicting asphalt mixture compaction. This system could significantly enhance the compaction quality of asphalt pavement and contribute to the comprehensive quality control and assurance of pavement construction.

TRB's National Cooperative Highway Research Program (NCHRP) Report 676: Intelligent Soil Compaction Systems explores intelligent compaction, a new method of achieving and documenting compaction requirements. Intelligent compaction uses continuous compaction-roller vibration monitoring to assess mechanistic soil properties, continuous modification/adaptation of roller vibration amplitude and frequency to ensure optimum compaction, and full-time monitoring by an integrated global positioning system to provide a complete GPS-based record of the compacted area--

Green and Intelligent Technologies for Sustainable and Smart Asphalt Pavements contains 124 papers from 14 different countries which were presented at the 5th International Symposium on Frontiers of Road and Airport Engineering (IFRAE 2021, Delft, the Netherlands, 12-14 July 2021). The contributions focus on research in the areas of "Circular, Sustainable and Smart Airport and Highway Pavement" and collects the state-of-the-art and state-of-practice areas of long-life and circular materials for sustainable, cost-effective smart airport and highway pavement design and construction. The main areas covered by the book include: • Green and sustainable pavement materials • Recycling technology • Warm & cold mix asphalt materials • Functional pavement design • Self-healing pavement materials • Eco-efficiency pavement materials • Pavement preservation, maintenance and rehabilitation • Smart pavement materials and structures • Safety technology for smart roads • Pavement monitoring and big data analysis • Role of transportation engineering in future pavements Green and Intelligent Technologies for Sustainable and Smart Asphalt Pavements aims at researchers, practitioners, and administrators interested in new materials and innovative technologies for achieving sustainable and renewable pavement materials and design methods, and for those involved or working in the broader field of pavement engineering.

In this paper, a procedure for estimation of effective modulus of a multilayered hot mix asphalt (HMA) pavement using intelligent compaction (IC) is investigated. The study is conducted during the construction of an interstate highway (I-35) in Norman, OK. A complete coverage of the level of compaction of each of the asphalt pavement layers was recorded using the intelligent asphalt compaction analyzer (IACA). The collected IACA data allow determination of the level of compaction (density) at any selected location, for each layer, and provided a set of global positioning system (GPS) coordinates. Calibration procedures have previously been tested and validated by the authors to estimate the density of different types of pavements from IACA data. In this paper, a different calibration procedure is used to measure the dynamic modulus instead of the density of a pavement using IACA. Considering the IACA estimated density, the dynamic modulus of each of the selected locations for an individual pavement layer was measured from laboratory developed master curves. Thereafter, an effective modulus of the three-layer pavement system was calculated for all of the selected locations using Odemark's method. The proposed technique was verified by conducting falling-weight deflectometer (FWD) tests at these selected locations. Analyses of the results show that the proposed intelligent compaction technique may be promising in estimating the effective modulus of the pavement layers in a non-destructive manner. In addition, the Witczak model was used to estimate moduli of each of the pavement layers. The comparison of the Witczak model with FWD revealed that the model over-predicted the modulus significantly.