Artificial intelligence combined with high-throughput calculations to improve the corrosion resistance of AlMgZn alloy

Efficiently designing lightweight alloys with combined high corrosion resistance and mechanical properties remains an enduring topic in materials engineering. Due to the inadequate accuracy of conventional stress-strain machine learning (ML) models caused by corrosion factors, a novel reinforcement self-learning ML algorithm combined with...

Rheological Properties of High-Viscosity Modified Asphalt Containing Warm-Mix Additives

High-viscosity modified asphalt (HVMA) is the most commonly applied method in drainage asphalt pavements. However, some disadvantages of hot-mix HVMA, including high energy consumption and unavoidable environmental pollution, should be improved. Therefore, warm-mix additive (WMA) was introduced. In this paper, the effects of WMA on the...

Regeneration mechanisms of aged SBS modified asphalt from RAP materials: Molecule structure, morphology, phase transition, and interface adhesion characteristics

In this study, the environmentally friendly rejuvenators with waste thermal conductive oil, polymer, and the interfacial reinforcing agent were prepared. And the influence of the different rejuvenators on the rejuvenation mechanisms of aged SBS modified asphalt was characterized by different scales including the molecule structure, morphology...

Chemo-Rheological Characterization of Aging Behaviors of Warm-Mix High-Viscosity Modified Asphalt

Today, high-viscosity modified asphalt (HVMA) is widely used in drainage asphalt pavements. However, due to its high construction temperature, HVMA is prone to thermal oxidative aging, which reduces its life cycle and increases the maintenance costs of asphalt pavements. In this study, the effects of warm-mix additives on the physical,...

Random forest incorporating ab-initio calculations for corrosion rate prediction with small sample Al alloys data

Corrosion jeopardizes the materials longevity and engineering safety, hence the corrosion rate needs to be forecasted so as to better guide materials selection. Although field exposure experiments are dependable, the prohibitive cost and their time-consuming nature make it difficult to obtain large dataset for machine learning. Here, we...