Potential damage, eventually demonstrated as moisture damage on inner and in-situ road structures, is the most complex problem to predict, which costs lots of money, time, and natural resources for maintenance and even leads to safety problems. Traditional linear regression analysis cannot fit well with this multi-factor task in such in-field circumstances. Random Forest (RF) is a progressive nonlinear algorithm, which can combine all relative factors to gain accurate prediction and good explanation. In this study, an RF model is constructed for the prediction of potential damage. In addition, relative variable importance is analyzed to obtain the correlations between factors and potential damage separately. The results show that, through the optimization, the model achieved a good average accuracy of 83.33%. Finally, the controlling method for moisture damage is provided by combining the traditional analysis method and the RF model. In a word, RF is a prospective method in predictions and data mining for highway engineering. Trained with effective data, it can be multifunctional and powerful to solve hard problems.

The impact of degradation on plastics is a critical factor influencing their properties and behavior, particularly evident in polyethylene (PE) and polypropylene (PP) and their blends. However, the effect of photoaging and thermal degradation, specifically within recycled polyethylene (rPE) and recycled polypropylene (rPP), on the thermo-mechanical and thermostability aspects of these blends remains unexplored. To address this gap, a range of materials, including virgin polyethylene (vPE), recycled polyethylene (rPE), virgin polypropylene (vPP), recycled polypropylene (rPP), and their blends with different ratios, were comprehensively investigated. Through a systematic assessment encompassing variables such as melting flow index (MFI), functional groups, mechanical traits, crystallization behavior, microscopic morphology, and thermostability, it was found that thermo-oxidative degradation generated hydroxyl and carboxyl functional groups in rPE and rPP. Optimal mechanical properties were achieved with a 6:4 mass ratio of rPE to rPP, as validated by FTIR spectroscopy and microscopic morphology. By establishing the chemical model, the changes in the system with an rPE–rPP ratio of 6:4 and 8:2 were monitored by the molecular simulation method. When the rPE–rPP ratio was 6:4, the system’s energy was lower, and the number of hydrogen bonds was higher, which also confirmed the above experimental results. Differential scanning calorimetry revealed an increased crystallization temperature in rPE, a reduced crystallization peak area in rPP, and a diminished crystallization capacity in rPE/rPP blends, with rPP exerting a pronounced influence. This study plays a pivotal role in enhancing recycling efficiency and reducing production costs for waste plastics, especially rPE and rPP—the primary components of plastic waste. By uncovering insights into the degradation effects and material behaviors, our research offers practical pathways for more sustainable waste management. This approach facilitates the optimal utilization of the respective performance characteristics of rPE and rPP, enabling the development of highly cost-effective rPE/rPP blend materials and promoting the efficient reuse of waste materials.

Grouted Semi-flexible Pavement (GSP) is a novel pavement composed of open-graded asphalt concrete grouted with high-fluidity cement mortar. Due to its excellent load-bearing and anti-rutting performance, it has great potential as anti-rutting overlay and surface in road construction. However, the understanding of GSP performance remains limited and pertinent findings are inconsistent. This article aims to provide a systematic literature review for the articles which were published between 2000 and 2020 on GSP, explore the problems in the recent research, identify knowledge gaps, and deliver recommendations for future research. The influential factors and the relative evaluation methods of GSP performance are summarized and discussed in this article.

The fatigue performance of hard asphalt is an important factor that affects the service life of asphalt pavement. In order to comprehensively explore the influence of chemical components on the fatigue performance of hard asphalt, and to eliminate the chemical instability between the microstructure of asphalt from different oil sources, seven kinds of hard asphalt were designed and prepared with saturates, aromatics, resins, and asphaltenes (SARA) extracted from the same hard asphalt. Rheological, time sweep and linear amplitude sweep (LAS) tests were carried out to evaluate the fatigue properties. The results show that the complex modulus of asphalt binds increased rapidly with an increase of asphaltene and resins and that the colloidal structure was strengthened, which would increase the fatigue factor. In the time sweep test, the strength of the colloidal structure significantly affected the fatigue life, and the fatigue life was different under different test stresses. In the viscoelastic continuum damage (VECD) model, the cumulative damage was related to the modulus, while with the increase of asphaltene and resins, the fatigue life showed a trend of first increasing and then decreasing. The linear regression analysis showed that the fatigue life of hard asphalt had a good correlation with strain sensitivity. This study investigated the applicability of different fatigue evaluation methods and revealed the influence of four components on the fatigue properties of hard asphalt. The results provide significant insights in the improvement of the fatigue performance of both hard asphalt and corresponding mixtures.