Prediction & optimization of alkali-activated concrete based on the random forest machine learning algorithm

Alkali-activated concrete (AAC) is regarded as a promising alternative construction material to reduce the CO₂ emission induced by Portland cement (PC) concrete. Due to the diversity in raw materials and complexity of reaction mechanisms, a commonly applied design code is still absent to date. This study attempts to directly...

Evaluation of the fatigue properties for the long-term service asphalt pavement using the semi-circular bending tests and stereo digital image correlation technique

Reliable assessment of the fatigue resistance of asphalt pavement with a long-term service is critically crucial for the rational formulation of original pavement utilization strategies in reconstruction and expansion projects. Currently, the pavement performance evaluation indicators are mainly used to guide pavement preventive maintenance,...

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...