The Hamburg wheel tracking (HWT) test has been found to be a promising test to evaluate the field rutting performance of asphalt pavements and has been implemented as a material screening test during the mix design process by several state departments of transportation. However, the rutting performance of an asphalt pavement depends not only on the material properties, but also on many other factors such as pavement structure and traffic. To date, there are few performance models that have integrated the Hamburg rutting parameters for pavement rutting prediction. In addition, mechanistic-empirical–based prediction models have been found to have some difficulties in reasonably predicting field rut depth, especially when field variables and confounding factors have to be considered. Therefore, the objective of this paper is to evaluate the relationship between the HWT test results and the field rut depth, then develop a predictive model for field rut depth based on the HWT test results. Field projects consisting of 51 hot mix asphalt (HMA) and warm mix asphalt (WMA) pavements were included in the analysis. These projects were located in different climatic zones with varying traffic levels, pavement structures, and material properties. Through direct correlation, it was found that the field rut depth in general decreased with the increase of the rutting resistance index (RRI). However, HWT test results alone do not have a strong relationship with the field rut depth, and many other factors, such as climate and pavement structure, have to be considered. Further, statistical-based methods in conjunction with engineering interpretation were applied to identify critical influencing factors and develop a prediction model for field rut depth. The developed rutting predictive model indicated that (a) mixture property (rutting resistance index, a parameter developed based on the HWT test), pavement age (month), average annual daily truck traffic (AADTT), and pavement structure (total HMA thickness and overlay thickness) are critical influencing factors for field rut depth; (b) RRI, along with pavement age and traffic data, has the most significant effect on rut depth among the identified five key predictor variables; (c) no significant differences are observed between prediction results of HMA and WMA mixtures, and thus the prediction model can be applied for both; and (d) using the developed predictive model, the effect of the HWT RRI can be considered comprehensively with other factors including climate, traffic, and pavement structure to determine the suitability of a designed asphalt mixture for pavement construction.

Traffic information is critical for pavement design, management, and health monitoring. Numerous in-pavement sensors have been developed and installed to collect the traffic volume and loading amplitude. However, limited attention has been paid to the algorithm of vehicle speed estimation. This research focuses on the estimation of the vehicle speed based on a cross-correlation method. A novel wireless micro-electromechanical sensor (MEMS), Smartrock is used to capture the triaxial acceleration, rotation, and stress data. The cross-correlation algorithms, i.e., normalized cross-correlation (NCC) algorithm, the smoothed coherence transform (SCOT) algorithm, and the phase transform (PHAT) algorithm, are applied to estimate the loading speed of an accelerated pavement test (APT) and the traffic speed in the field. The signal-noise-ratio (SNR) and the mean relative error (MRE) are utilized to evaluate the stability and accuracy of the algorithms. The results show that both the correlated noise and independent noise have significant influence in the field data. The SCOT algorithm is recommended for speed estimation with reasonable accuracy and stability because of a large SNR value and the lowest MRE value among the algorithms. The loading speed investigated in this study was within 50 km/h and further verification is needed for higher speed estimation.

Compaction is a critical step in asphalt roadway construction to determine the pavement’s quality and service life. Current field compactions are mainly based on test strips and engineers’ experiences. Such empirical-based approaches sometimes cause compaction problems especially when new materials are implemented. Intelligent compaction (IC) technology was invented to improve compaction quality. The real-time collection and visualization of the pavement responses and temperature can greatly improve compaction uniformity. However, the material viscoelastic property and the complex pavement structure still hinder the establishment of the correlation between the Intelligent Compaction Measurement Value (ICMV) and the compactability of asphalt pavement. This paper aims to establish compaction prediction models based on machine learning (ML) algorithms and sensing technology. Given the strong correlation between the mixture’s compactability and the particle compaction characteristics, a particle-size wireless sensor, SmartRock, was used to collect the particle kinematic behaviors during compaction. 11 asphalt mixtures were compacted with an embedded SmartRock sensor. Three ML models and three dataset scenarios were applied to predict the compaction condition, and a higher than 98% prediction accuracy was achieved. The results revealed that combining the ML approach and sensing data is appropriate and practical for predicting the compaction condition of the asphalt mixture. Future studies shall further evaluate the algorithm based on field compaction data so that intelligent sensing technology can be implemented to improve compaction quality.

The laboratory aging tests for binders were developed based on Strategic Highway Research Program (SHRP) tests in the 1990s (i.e., performance grading); the applicability of performance grade (PG) to recently developed tests, such as multiple stress creep recovery (MSCR), is not clear. In addition, the ability of laboratory aging to represent field pavement aging, especially when polymer-modified asphalt (PMA) and warm-mix asphalt (WMA) binders are used, has been a concern to paving practice. This paper investigates quantitatively the level of laboratory aging compared to the field aging for both the control hot-mix asphalt (HMA) binders and WMA and PMA binders. The study focuses on a number of binder parameters, as well as evaluating whether the current laboratory aging conditions (loose-mix oven aging) will provide correlation to field short-term aging. HMA and WMA binders from four field projects that covered different climatic zones, pavement structures, and materials were selected. Binder properties including high-temperature PG, MSCR nonrecoverable creep compliance, low-temperature binder PG, and fracture energy were used for analysis. The results show that the laboratory rolling thin-film oven (RTFO) aging method is not sufficient to simulate field short-term aging conditions for most cases except for the low-temperature PG test. The aging of asphalt binder is parameter sensitive; depending on the parameters used, the comparison results between laboratory aging and field aging could be different. The effect of aging on PMA binder, especially when mixed with Sasobit additive, differs from non-polymer-modified binders. Limited results from non-polymer-modified binder mixes suggested that loose-mix oven aging at 85°C for 2, 5, and 7 days appears to provide a reasonable simulation of field pavement aging right after compaction (0 years), 1.8 years, and 3 years.

Traditional road-embedded monitoring systems for traffic monitoring have the disadvantages of a short life, high energy consumption and data redundancy, resulting in insufficient durability and high cost. In order to improve the durability and efficiency of the road-embedded monitoring system, a pavement vibration monitoring system is developed based on the Internet of things (IoT). The system includes multi-acceleration sensing nodes, a gateway, and a cloud platform. The key design principles and technologies of each part of the system are proposed, which provides valuable experience for the application of IoT monitoring technology in road infrastructures. Characterized by low power consumption, distributed computing, and high extensibility properties, the pavement vibration IoT monitoring system can realize the monitoring, transmission, and analysis of pavement vibration signal, and acquires the real-time traffic information. This road-embedded system improves the intellectual capacity of road infrastructure and is conducive to the construction of a new generation of smart roads.