To address the issue of insufficient durability of traditional modified emulsified asphalt in the application of cold mix and cold paving anti-skid wear layers, this study utilizes cationic waterborne polyurethane (PU+) for composite modification to enhance adhesion and performance across a range of temperatures. Initially, composite-modified emulsified asphalt samples were prepared with varying dosages of PU+ according to a gradient method. Routine performance tests were conducted on the evaporated residues for analysis. Advanced rheological tests, including temperature sweep (TS), frequency sweep (FS), linear amplitude sweep (LAS), and multi-stress creep recovery (MSCR) tests, were performed using a dynamic shear rheometer (DSR). Surface free energy (SFE) tests were conducted with a fully automated surface tension meter (STM). A comprehensive evaluation of the high-temperature rheological properties, fatigue properties, adhesion properties, and water damage resistance of the modified emulsified asphalt residues was carried out. Chemical changes before and after modification were characterized using Fourier transform infrared spectroscopy (FTIR), and the distribution of polymers in the evaporated residue was observed using fluorescence microscopy (FM). The results demonstrated that cationic waterborne polyurethane significantly enhanced the fatigue and adhesion properties of SBS-modified emulsified asphalt, but it also weakened the water damage resistance of asphalt. MSCR tests revealed that the addition of cationic waterborne polyurethane might reduce the elastic recovery performance of modified asphalt, thereby weakening its resistance to rutting. Among the samples, the modified asphalt with a PU+ content of 6% exhibited good high-temperature shear resistance and elastic recovery performance, demonstrating the best anti-rutting performance.

To address the challenges of slow curing speed and suboptimal microwave absorption during the paving of cold-mixed and cold-laid asphalt mixtures, this study introduces SiC-Fe₃O₄ composite material (SF) into emulsified asphalt mixtures to enhance microwave absorption and accelerate curing via microwave heating. Initially, based on the maximum density curve theory, an appropriate mineral aggregate gradation was designed, and the optimal ratio of emulsified asphalt mixture was determined through mixing tests, cohesion tests, wet wheel wear tests, and load wheel sand adhesion tests. Subsequently, the influence of SF content on the mixing performance of emulsified asphalt mixtures was analyzed through mixing and consistency tests. Finally, the microwave absorption performance of the mixture was evaluated by designing microwave heating tests under different conditions, using temperature indicators and quality indicators. The experimental results indicate that when SF content ranges from 0% to 4%, the mixing performance of the emulsified asphalt mixture meets specification requirements. The dosage of SF, SF composite ratio, and microwave power significantly impact microwave absorption performance, whereas environmental temperature has a relatively minor effect. The optimal mix ratio for the emulsified asphalt mixture is mineral aggregate:modified emulsified asphalt:water:cement = 100:12.8:6:1. The ideal SF dosage is 4%, with an optimal SiC to Fe₃O₄ composite ratio of 1:1, and a suitable microwave power range of 600–1000 W.

The application of microwave heating technology can significantly enhance the water evaporation rate of emulsified asphalt mixtures post paving. To improve the microwave absorption and curing performance of these mixtures, SiC-Fe₃O₄ composite material (SF) was incorporated. This addition aims to enhance the microwave absorption efficiency and accelerate the curing process of emulsified asphalt mixtures under microwave heating. This study begins with an analysis of the microwave absorption principles pertinent to emulsified asphalt mixtures. Subsequently, the microwave heating temperature fields of ordinary emulsified asphalt mixture (EAM), SiC emulsified asphalt mixture (S-EAM), Fe₃O₄ emulsified asphalt mixture (F-EAM), and SiC-Fe₃O₄ emulsified asphalt mixture (SF-EAM) were simulated using COMSOL Multiphysics finite element software (COMSOL 6.2). The early strength variations in SF-EAM under different microwave heating durations were then examined through adhesion tests, leading to the proposal of a microwave heat curing process for SF-EAM. Finally, the wear resistance, water damage resistance, rutting resistance, and skid resistance of SF-EAM post-microwave curing were evaluated through wet wheel wear tests, wheel track deformation tests, and road friction coefficient tests. The results indicate that the optimal microwave heating time is 90 s, with the microwave absorption performance of the materials ranked as follows: EAM, S-EAM, F-EAM, and SF-EAM, from lowest to highest. The road performance of SF-EAM complies with specification requirements, and its wear resistance, water damage resistance, and rutting resistance are notably improved after microwave heating.

Being a kind of actual resources, manufacturing equipment resources (MERs) need to be virtualized and encapsulated into services. Our proposed works mainly focus on manufacturing capability of MERs that is consisted of two aspects: static functional capability and dynamic production capability, and relationship between related concepts so as to model MERs by ontology web language (OWL) that is based on semantic. In this paper, firstly, ontology based methodology within manufacture field is developed according to cloud manufacturing characters. Secondly, manufacturing capability is studied from functional attribute capability and production capability, then, the related concepts classes and relationship are analyzed, with the special properties defined to describe these classes based on semantic. Thirdly, the built in model is described by OWL (ontology web language) using protégé tool and an instance of MER is built based on the proposed model to express its manufacturing capability. Finally, this model is applied to Cloud MERs service platform, which is constructed for a given enterprise group, to provide MERs services. Moreover, Web Service is used in the platform to realize the sharing of the provided services.

This study systematically compares the predictive performance of single machine learning (ML) models (KNN, Bayesian ridge regression, decision tree) and ensemble learning methods (bagging, boosting, stacking) for quantifying the rheological properties of mineral powder-modified asphalt, specifically the complex shear modulus (G*) and the phase angle (δ). We used two emulsifiers and three mineral powders for fabricating modified emulsified asphalt and conducting rheological property tests, respectively. Dynamic shear rheometer (DSR) test data were preprocessed using the local outlier factor (LOF) algorithm, followed by K-fold cross-validation (K = 5) and Bayesian optimization to tune model hyperparameters. This framework uniquely employs cross-validated predictions from base models as input features for the meta-learner, reducing information leakage and enhancing generalization. Traditional single ML models struggle to characterize accurately as a result, and an innovative stacking model was developed, integrating predictions from four heterogeneous base learners—KNN, decision tree (DT), random forest (RF), and XGBoost—with a Bayesian ridge regression meta-learner. Results demonstrate that ensemble models outperform single models significantly, with the stacking model achieving the highest accuracy (R² = 0.9727 for G* and R² = 0.9990 for δ). Shapley additive explanations (SHAP) analysis reveals temperature and mineral powder type as key factors, addressing the “black box” limitation of ML in materials science. This study validates the stacking model as a robust framework for optimizing asphalt mixture design, offering insights into material selection and pavement performance improvement.