Mechanical properties prediction of blast furnace slag and fly ash-based alkali-activated concrete by machine learning methods
Beibei Sun (Universiteit Gent)
Luchuan Ding (Tongji University)
G. Ye (Universiteit Gent, TU Delft - Materials and Environment)
Geert De SCHUTTER (Universiteit Gent)
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Abstract
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 compared with an empirical formula and experimental results. Besides, a literature study was carried out to investigate the connection between compressive strength and other mechanical characteristics. As a result, the gradient boosting regression trees model and several predictive formulas were eventually proposed for the prediction of the mechanical behavior including compressive strength, elastic modulus, splitting tensile strength, flexural strength, and Poisson's ratio of BFS/FA-AACs. The importance index of each parameter on the strength of BFS/FA-AACs was elaborated as well.