Machine Learning-based Prediction of Porosity for Concrete Containing Supplementary Cementitious Materials

TL;DR

This study employs ensemble machine learning methods to accurately predict concrete porosity based on mixture composition, aiding durability assessment of concrete with supplementary cementitious materials.

Contribution

It introduces an ensemble learning approach for predicting concrete porosity, demonstrating superior accuracy over traditional methods and optimizing hyperparameter tuning strategies.

Findings

Gradient boosting trees outperform random forests in accuracy.

Out-of-bag error tuning is more efficient than k-Fold Cross-Validation.

Ensemble models reliably predict porosity from mixture features.

Abstract

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.