Introduction: Ultraviolet (UV) radiation is one of the primary environmental factors causing performance degradation of asphalt pavements, while the compatibility between modifiers and petroleum asphalt significantly influences the aging resistance of modified asphalt. This study aims to elucidate the compatibility between Buton rock asphalt (BRA) and petroleum asphalts as well as the influence of compatibility on UV aging behavior. Methods: The compatibility of BRA (at a dosage of 30 wt%) with four base asphalts (Maoming Petrochemical 70# (70-A), Donghai 70# (70-B), Shuanglong 70# (70-C), and Panjin 90# (90-D)) was evaluated through physical property tests and dynamic shear rheometer (DSR) measurements. The thin-film oven test (TFOT) was employed to simulate short-term thermo-oxidative aging during construction, followed by multi-cycle variable-intensity UV aging to simulate long-term photo-oxidative aging during service. Viscosity, DSR, scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and gel permeation chromatography (GPC) tests were used to analyze multi-scale ultraviolet aging behavior. Results: 70-A exhibits the best compatibility with BRA, with its modified asphalt (70-A-MA) showing a softening point of 50.5 °C (an increase of 11.0%), moderate viscosity growth (37.1%), and good storage stability. The increments in viscosity aging index and rutting factor aging index of 70-A-MA are lower than those of 90-D-MA, and 70-A-MA maintains lower surface crack density. FTIR analysis shows that the total growth rates of carbonyl and sulfoxide indices for 70-A-MA (70.4% and 42.5%) are significantly lower than those for 90-D-MA (202.0% and 131.7%); GPC results indicate that the total large molecular size (LMS) content increase for 70-A-MA (30.0%) is also lower than that for 90-D-MA (68.2%). Discussion: These results demonstrate that the type of base asphalt is a key factor influencing its compatibility with BRA. Modified asphalt with superior compatibility exhibits better chemical stability and less severe aging damage. This study provides scientific guidance for the selection of base asphalt in BRA modification applications.

Traffic-induced noise pollution is a significant environmental issue, driving the development of advanced noise-reducing pavement materials. Semi-dense graded asphalt mixtures (SDAMs) present a promising compromise, offering enhanced acoustic properties compared to conventional dense-graded asphalt mixtures while maintaining superior durability to porous asphalt mixtures. However, the mechanism underlying the relationship between the energy dissipation characteristics and noise reduction effects of such mixtures remains unclear, which limits further optimization of their noise reduction performance. This study designed and prepared semi-dense graded noise-reducing asphalt mixtures SMA-6 TM, SMA-10 TM, and SMA-13 TM (SMA TM represents noise-reducing SMA mixture) based on traditional dense-graded asphalt mixtures SMA-6, SMA-10, and SMA-13, and conducted tests for water stability, high-temperature performance (60 °C), and low-temperature performance (−10 °C). Subsequently, energy loss parameters such as loss factor and damping ratio were calculated through dynamic modulus tests to characterize their energy dissipation properties. The mechanism linking the energy dissipation characteristics of semi-dense graded asphalt mixtures to noise reduction was investigated. Finally, the noise reduction effect was further verified through a tire free fall test and a close-proximity (CPX) method. The indoor test results indicate that the semi-dense mixtures exhibited a trade-off in performance: their dynamic stability was 11.1–11.3% lower and low-temperature performance decreased by 4.2% (SMA-13 TM) to 14.1% (SMA-6 TM), with moisture stability remaining comparable. Conversely, they demonstrated superior damping, with consistently higher loss factors and damping ratios. All mixtures reached peak damping at 20 °C, and the loss factor showed a strong positive correlation (R² > 0.91) with energy dissipation. Field results from a test section showed that the optimized SMA-10 TM mixture yielded a significant tire–road noise reduction of 3–5 dB(A) relative to the SMA-13, while concurrently meeting key performance criteria for anti-water ability and durability. This study establishes a link between the energy dissipation in SDAM and their noise reduction efficacy. The findings provide a theoretical framework for optimizing mixture designs and support the wider application of SDAM as a practical noise mitigation solution.

The reliability of the mechanical property values adopted for current pavement structural design remains low. Therefore, this study investigated the nonlinear characteristics for the tensile and compressive properties of cement-stabilized macadam (CSM) and an asphalt mixture (AM) under different gradation types and loading rates. And a multi-factor value model was developed for both. The results show that the tensile and compressive stress–strain behavior of both CSM and AM exhibited bilinear characteristics consistent with the bi-modulus theory (elasticity with different moduli in tension and compression). The strength, elastic modulus, and Poisson’s ratio followed a power function relationship with increasing loading rates, stabilizing at values beyond 0.1 MPa/s. The skeleton-dense gradation demonstrates the most favorable mechanical performance. For semi-open-graded AM with a void ratio of 8~16%, the mechanical parameters exhibit relatively high rates of change. Among the influencing factors, gradation type had the most significant impact on the mechanical parameters, especially on the elastic modulus. In general, tensile mechanical parameters were more sensitive to changes than compressive ones. R_c/R_t was most strongly affected by gradation type. Accordingly, a quantitative value model was established to describe the variation in tensile and compressive mechanical parameters of typical asphalt pavement materials, which vary with air void ratio, loading rate, binder content, and temperature. The findings provide a reference for the prediction of pavement structure design parameters considering the difference in compression and tension.

Fault diagnosis for embedded sensors in asphalt pavement faces significant challenges, including the scarcity of real-world fault data and the difficulty in identifying compound faults, which severely compromises the reliability of monitoring data. To address these issues, this study proposes an intelligent diagnostic framework that integrates a Discrete Wavelet Transform (DWT) with a staged, attention-based Long Short-Term Memory (LSTM) network. First, various fault modes were systematically defined, including short-term (i.e., bias, gain, and detachment), long-term (i.e., drift), and their compound forms. A fine-grained fault injection and labeling strategy was then developed to generate a comprehensive dataset. Second, a novel diagnostic model was designed based on a “Decomposition-Focus-Fusion” architecture. In this architecture, the DWT is employed to extract multi-scale features, and independent sub-models—a Bidirectional LSTM (Bi-LSTM) and a stacked LSTM—are subsequently utilized to specialize in learning short-term and long-term fault characteristics, respectively. Finally, an attention network intelligently weights and fuses the outputs from these sub-models to achieve precise classification of eight distinct sensor operational states. Validated through rigorous 5-fold cross-validation, experimental results demonstrate that the proposed framework achieves a mean diagnostic accuracy of 98.89% (±0.0040) on the comprehensive test set, significantly outperforming baseline models such as SVM, KNN, and a unified LSTM. A comprehensive ablation study confirmed that each component of the “Decomposition-Focus-Fusion” architecture—DWT features, staged training, and the attention mechanism—makes an indispensable contribution to the model’s superior performance. The model successfully distinguishes between “drift” and “normal” states—which severely confuse the baseline models—and accurately identifies various complex compound faults. Furthermore, simulated online diagnostic tests confirmed the framework’s rapid response capability to dynamic faults and its computational efficiency, meeting the demands of real-time monitoring. This study offers a precise and robust solution for the fault diagnosis of embedded sensors in asphalt pavement.