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...

Mechanical properties prediction of blast furnace slag and fly ash-based alkali-activated concrete by machine learning methods

In this paper, 871 data were collected from literature and trained by the 4 representative machine learning methods, in order to build a robust compressive strength predictive model for slag and fly ash based alkali activated concretes. The optimum models of each machine learning method were verified by 4 validation metrics and further...

Prediction of the autogenous shrinkage and microcracking of alkali-activated slag and fly ash concrete

This study aims to predict the autogenous shrinkage of alkali-activated concrete (AAC) based on slag and fly ash. A variety of analytical and numerical models are available for the prediction of autogenous shrinkage of ordinary Portland cement (OPC) concrete, but these models are found to show dramatic discrepancies when applied for AAC due...