Predicting the impact of adding metakaolin on the flexural strength of concrete using ML classification techniques – a comparative study - Velastegui, Luis; Velasco, Nancy; Sanchez Quispe, Hugo Rolando; Barahona, Fredy; Onyelowe, Kennedy C.; Hanandeh, Shadi; Ebid, Ahmed M.; John, TrustGod A. (Frontiers in Built Environment, 2024)
The structural design standards, particularly in concrete technology, heavily rely on the mechanical attributes of concrete. Utilizing dependable predictive models for these properties can minimize the need for extensive laboratory testing, evaluations, and experiments to acquire essential design data, thereby conserving time and resources. Metakaolin (MK) is frequently incorporated as an alternative to Portland cement in the production of sustainable concrete, owing to its technical advantages and positive environmental impact, aligning with the United Nations Sustainable Development Goals (UNSDGs) aimed at achieving net-zero objectives. However, this research presents a comparative study between eight (8) ML classification techniques namely, gradient boosting (GB), CN2, naïve bayes (NB), support vector machine (SVM), stochastic gradient descent (SGD), k-nearest neighbor (KNN), Tree and random forest (RF) to estimate the impact of adding metakaolin to concrete on its flexural strength considering mixture components contents and concrete age. The collected data entries for the prediction of the flexural strength (Ft) containing the following concrete components; contentof cement (C), content of metakaolin (MK), content of water (W), content of fine aggregates (FAg), content of coarse aggregates (CAg), content of super-plasticizer (P), and the concrete curing age at testing (Age) were partitioned into 80% and 20% for training and validation sets respectively. At the end of the model protocol, it was found that the GB, SVM, and KNN models which produced an average MSE value of zero (0) showed their decisive ability to predict the flexural strength of the metakaolin (MK) mixed concrete (Ft). This outcome agrees with the previous reports in the literatures; however the work of Shah et al. happens to be the closest in terms of concrete components used in the production of the mixes and the application of machine learning techniques. It was found that the present research work’s models outperformed those presented by Shah et al. Hence the decisive models reported in this research paper show potentials to be applied in the design and production of MK concrete with optimal flexural strength.