In order to study the effects of aggregate shape on the strength of mixtures, the glue-sphered ellipsoid particles were used to create cylinder samples, and triaxial tests were simulated using the discrete element method. This paper discussed (a) how to create ellipsoid aggregates by glued spheres and control its size; (b) how to simulate the triaxial performance of hot-mix asphalt (HMA) skeleton; and (c) how to choose coarse aggregates for HMA according to shape factors. Through computation, a nephogram of triaxial strength varying with shape factors Flatness and Elongation was finally obtained. The results showed that cubical and part of rod aggregate work much better in strength than others, which were proved to be consistent with that of experimental results.

The frequency of extreme weather events has increased worldwide, leading to more intense natural disasters, which pose significant threats to human life and property safety. The main form of disaster occurrence is multi-hazard coupling and multi-hazard chaining. This paper constructs a road natural disaster risk assessment model using a fuzzy comprehensive evaluation method and neural network to quantitatively analyze road disasters with multiple hazards, and provides valuable insights for the predication of road natural disaster risk. Here, ten factors, including temperature, relative humidity, precipitation, elevation, slope, slope orientation, vegetation cover, geologic lithology, historical impact factors, and road density, were selected as input variables, and risk grade was selected as the output value (the evaluation results). The remaining hidden layers use the fully connected neural network. This model was first trained using historical data (from 2011 to 2021) obtained from road networks and natural disasters in Guangxi, China. Then, taking Lingchuan County as an example, the model was used to predict the risk of natural disasters on its roads, and, finally, the prediction accuracy of the model was determined by comparing the results with actual disaster situations. This study can provide theoretical support and technical operations for the development of subsequent early warning systems.

Low temperature asphalt (LTA) technologies, such as warm-mixed asphalt mixes, are utilized in the paving industry to lower energy demands and greenhouse gas emissions during asphalt mixing and pavement construction. However, the asphalt mixes developed that incorporate LTA additives are more sensitive than hot-mixed asphalts to temperature reduction during compaction, which leads to inadequate compaction and subsequent poor pavement performance. The induction heating-assisted compaction of pavement structures appears to be an effective way to ameliorate such issues and to improve mix compactability at lower temperatures. Considering that induction-assisted compaction is a complex process, a computational methodology is proposed in this paper. A porous asphalt concrete mix was considered as case material. For the pavement compaction analyses after induction, the temperature field generated by electromagnetic induction was predicted and the material parameters of asphalt mortar were adjusted. The effect of induction heating on asphalt compaction effectiveness, the tendency of mix density changing, the increase in compactor passes, and the influence of temperature on compaction at different locations in the pavement were studied as well.

The angularity of particles has important effects on the mechanical properties of asphalt mixture and other granular materials. To simulate these effects, the glue-sphere method was usually used to create an arbitrary polyhedron particle. Unlike other studies, this paper aims to efficiently fill a polyhedron with as few spheres as possible through optimization technology in order to reduce the cost of calculation during mixture simulation. Four contents are mainly discussed here: a) how to produce non-spherical aggregates and control their sizes with the minimal bounding box; b) how to fill convex non-spherical particles with the fewest balls as possible using the constrained nonlinear optimization method; c) how to compute the typical shape factors of these particles; and d) how the control parameters affect the filling effect. The algorithm for this study was programmed by MATLAB software and was proven to have better filling performance and less computational cost compared to other methods.

Isocyanate esters are widely recognized for their superior curing capabilities. Leveraging this attribute, the current research formulated a modified cold-mixed asphalt blend using 4,4′-methylene diphenyl diisocyanate (MDI). Tests and analyses of the MDI-modified asphalt with varying inclusion percentages of MDI revealed that a mixture containing 15% rock asphalt and 15% MDI-modified asphalt exhibited a more balanced, comprehensive performance. We also conducted an examination of the role and properties of MDI in asphalt modification using molecular dynamics simulations. The cold-curing properties of MDI-modified asphalt as compared to petroleum asphalt were evaluated based on its density, free volume analysis, cohesive energy density, and glass transition temperature. Implementing the LB-13 gradation—a cold-mixed asphalt gradation with a nominal particle size of 13.2 mm recommended by Chinese specifications—we prepared MDI-modified cold-mixed asphalt and carried out analyses of its mechanical characteristics, high-temperature performance, and water damage resistance. The results demonstrated that MDI-modified asphalt showcases excellent ductility, flexibility, and aging resistance, surpassing the performance of petroleum asphalt. The stability, high-temperature rutting, and water damage resistance of the MDI-modified cold-mixed asphalt exceeded the requirements for hot-mixed asphalt. This research provides theoretical and experimental support for isocyanate ester applications in asphalt engineering, presenting significant value for practical engineering applications.