This study investigated the effect of waste toner as a cost-effective enhancement modifier on short-term aging and moisture resistance of asphalt binders and mixtures. Moreover, the mechanism of asphalt binder aging and moisture resistance was reviewed, aiming to provide promising evaluation methods for assessing short-term aging resistance of asphalt binders as well as moisture resistance of asphalt binders and mixtures. First, the aging resistance of neat and toner-modified asphalt (TMA) binders was evaluated using three approaches: rheological aging index (RAI), viscosity aging index (VAI), and chemical functional group indices based on data obtained from Fourier transform infrared spectroscopy (FTIR) analysis. To measure viscoelastic and viscosity characteristics of neat and TMA binders, a dynamic shear rheometer (DSR) and a viscometer were employed, respectively. Second, the moisture resistance of asphalt binders and mixtures were investigated based on analyzing the infrared spectrum and tensile strength ratio (TSR), respectively. The findings indicated that modifying binder with low percentages of waste toner enhanced the short-term aging resistance. Also, TMA binders containing 12% of waste toner presented a significant improvement in moisture resistance of asphalt binders and mixtures.

Pavement integrity assessment based on ground-penetrating radar (GPR) remains challenging because of the uncertainty of the relationship between pavement damage and GPR reflection patterns. Further, existing image-processing techniques for analyzing GPR data still need significant improvements. In this study, a novel framework termed “parallel B-scan You Only Look Once (YOLO) plus A-scan voting” (P-BYPAV) was proposed to improve the performance of YOLO classification in the presence of ambiguous B-scan image patterns. The framework was developed by utilizing GPR-aided coring and falling-weight deflectometer test data. GPR B-scan reflection patterns can be classified into four categories: (1) the valley-peak-valley pattern (V-P-V) representing intact and healthy interfaces where moisture damage is minimal; (2) the peak-valley-peak pattern (P-V-P) representing the presence of air voids at layer interfaces, with a strong correlation to severe debonding; (3) the amplified valley-peak-valley pattern (Amp. V-P-V) representing the presence of moisture at layer interfaces, corresponding to various level of pavement damages; and (4) the symmetric peak-valley pattern, which indicates ambiguity in distinguishing moisture content and air voids. A dataset of 300 labeled images was created using the knowledge of these patterns. The dataset was then used to train the proposed P-BYPAV framework, demonstrating that P-BYPAV successfully integrated the advantages of YOLO and A-scan data recognition. The results show that P-BYPAV resolved more than 97% of the ambiguous regions outlined by YOLO alone.

Accurate in situ performance assessment of asphalt pavements is a critical step in developing cost-efficient maintenance and rehabilitation strategies. Back-calculation of pavement layer modulus from falling weight deflectometer (FWD) field-test data is a widely used method for estimating the load-bearing capacity of flexible pavements. However, traditional back-calculation methods assume purely elastic behavior and overlook the viscoelastic nature of asphalt materials, often resulting in inaccurate assessments. To address this limitation, this study introduces a mechanistic-based back-calculation technique to determine the dynamic modulus ( E *) master curve directly from FWD surface deflection time histories. The method integrates layered elastic theory with viscoelastic principles and uses differential-time Fourier transform analysis to determine the relaxation modulus from measured time-domain data. The corresponding frequency-domain response is computed and then fitted to a fractional viscoelastic model to construct the E * master curve. Validation against laboratory-measured E * values from field-cored specimens demonstrates that the proposed technique generates highly accurate E * master curves for all asphalt layers. Notably, the highest accuracy in the back-calculated E * master curve is achieved for the surface layer, followed by the intermediate and base layers. This framework offers a practical alternative to laboratory E * testing and improves the accuracy of in situ pavement structural evaluations.