A review on solutions for improving rutting resistance of asphalt pavement and test methods

Advances, limitations and prospects of nondestructive testing and evaluation of thick composites and sandwich structures: A state-of-the-art review

Integrating three-dimensional road design and pavement structure analysis based on BIM

Performance evaluation of warm mix asphalt additive modified epoxy asphalt rubbers

Effect of freezing-thawing and ageing on thermal characteristics and mechanical properties of conductive asphalt concrete

This study investigated the simulation of wheel tracking test and the high-temperature rutting behaviour of an asphalt mixture by using the discrete element method (DEM). Based on the DEM software named as Particle Flow Code in three dimensions (PFC3D), a micromechanical model of an asphalt mixture composed of coarse aggregates, asphalt mastic, and air voids was built and a two-dimensional virtual wheel tracking test was simulated. Based on the virtual wheel tracking test, the distribution of displacement and contact forces within test specimen were analysed. It is proved that the built virtual wheel tracking test can capture the rutting deformation caused by the combination of densification and lateral flow deformation. It is also indicated that, within an asphalt mixture, the aggregate skeleton is the main bearing body for the compressive forces, while the compressive and tensile forces between aggregate and mastic are severer than that within asphalt mastic. It is important to guarantee the stability o...

Simulation of wheel tracking test for asphalt mixture using discrete element modelling

This study evaluated the effects of warm mix asphalt (WMA) additives on the compaction temperature and properties of crumb rubber modified (CRM) asphalt binder and mixture. Two different WMA additives (named as Sas and Evm) were used to prepare warm-mix CRM asphalt binder and mixture. The viscosity of different warm-mix CRM asphalt binders and mastics was measured at different temperatures. The rheological and mechanical properties of different warm-mix CRM asphalt binders were tested. At the mixture level, the volumetric properties of different warm-mix CRM asphalt mixtures were experimented by Gyratory compactor at different temperatures and the performance of different warm-mix CRM asphalt mixtures were evaluated. It was found that both of the two WMA additives could lower the compaction temperatures of CRM asphalt mixtures by 10–20 °C. However, they have different influences on rheological properties of CRM binder and performance of CRM mixture. The Sas warm-mix additive can improve the anti-rutting performance of CRM mixture but may degrade its low-temperature performance and moisture stability. The Evm warm-mix additive has no adverse effects on the high-temperature and low-temperature performance of CRM asphalt mixtures and can improve its moisture stability.

Laboratory Investigation of Crumb Rubber Modified Asphalt Binder and Mixtures with Warm-Mix Additives

Real-time asphalt concrete (AC) pavement density monitoring is important for quality control (QC) and quality assurance (QA) purposes, because it allows correction during the compaction process. Ground penetrating radar (GPR) is capable of providing real-time AC mixture density prediction using the Al-Qadi, Lahouar, and Leng (ALL) density prediction model. However, noise sources, such as surface moisture and vibrations, may jeopardize the AC density prediction accuracy. This study proposes a mean reflection coefficient algorithm and digital filter design method to remove the surface moisture and smooth the density profile. In the mean reflection algorithm, the frequency-select bandwidth was selected as 40–70% of the actual peak frequency in the magnitude spectrum through the simulation studies. White Gaussian noise was added in the models for robustness testing. In the digital filter design method, the magnitude spectrum of the GPR predicted density profile was analyzed to decide filter types and corresponding parameters. Thresholding method was used to remove abnormal values, and window-based finite impulse response (FIR) filters were used to smooth the density profile. Lab-controlled and field tests were performed for both algorithms. Estimated aggregate dielectric constant was used to predict pavement density. A sensitivity analysis was performed to evaluate the effect of different aggregate dielectric constant on density (or air void). For surface moisture effect removal, mean reflection coefficient algorithm may be utilized to reconstruct dielectric constant values at an error less than 4%. This algorithm is independent of the antenna central frequency. For the density profile smoothing during continuous GPR survey, results show that various filter types have comparable smoothing performances. For the effect of aggregate dielectric constant on density prediction, sensitivity analysis shows that when aggregate dielectric constant values changes from 6.5 to 7, the predicted air void increases from 2.5% to 6.3%. This indicates the importance of an accurate aggregate dielectric constant estimate when applying ALL model for pavement density predictions; hence, aggregate dielectric constant estimate must be utilized.

Factors Impacting Monitoring Asphalt Pavement Density by Ground Penetrating Radar

Evaluating the rutting resistance of asphalt mixtures using a simplified triaxial repeated load test

The processing of ground-penetration radar (GPR) signals collected from thin asphalt concrete (AC) overlay is a challenging task due to the limitation of GPR signal resolution. In this study, a gradient descent based nonlinear optimization approach was developed to analyze GPR signals collected from thin AC overlays to estimate their thickness and density. Both finite difference time domain (FDTD) simulation and field tests were conducted to validate the proposed algorithm. The simulation showed that the accuracy of dielectric constant estimation increased after the nonlinear gradient descent method was applied. This resulted in a thickness estimation error of less than 1 mm. When nonlinear gradient descent was applied to field test measured signals, the average AC overlay thickness prediction and AC density estimation errors were 3 mm and 1.81%, respectively. This study demonstrates that the nonlinear gradient descent is an effective approach for estimating thin AC overlay thickness and density from GPR data.

Siqi Wang

Papers

Simulation of wheel tracking test for asphalt mixture using discrete element modelling

Laboratory Investigation of Crumb Rubber Modified Asphalt Binder and Mixtures with Warm-Mix Additives

Factors Impacting Monitoring Asphalt Pavement Density by Ground Penetrating Radar

Evaluating the rutting resistance of asphalt mixtures using a simplified triaxial repeated load test

Prediction of thin asphalt concrete overlay thickness and density using nonlinear optimization of GPR data