  Porosity has been identified as the key indicator of the durability
properties of concrete exposed to aggressive environments. This paper applies
ensemble learning to predict porosity of high-performance concrete containing
supplementary cementitious materials. The concrete samples utilized in this
study are characterized by eight composition features including w/b ratio,
binder content, fly ash, GGBS, superplasticizer, coarse/fine aggregate ratio,
curing condition and curing days. The assembled database consists of 240 data
records, featuring 74 unique concrete mixture designs. The proposed machine
learning algorithms are trained on 180 observations (75%) chosen randomly from
the data set and then tested on the remaining 60 observations (25%). The
numerical experiments suggest that the regression tree ensembles can accurately
predict the porosity of concrete from its mixture compositions. Gradient
boosting trees generally outperforms random forests in terms of prediction
accuracy. For random forests, the out-of-bag error based hyperparameter tuning
strategy is found to be much more efficient than k-Fold Cross-Validation.

Machine Learning;Computational Engineering, Finance, and Science