Data proportionality and its impact on machine learning predictions of ground granulated blast furnace slag concrete strength

Abstract

Sustainable concrete’s compressive strength (CST) ensures structural safety, durability, and performance while minimizing environmental impact. It supports eco-friendly design, resource optimization, and compliance with green building standards. Determining the CST using laboratory procedures is time-consuming and lengthy. Therefore, the present research introduces a reliable machine learning (ML) model for assessing the CST of ground granulated blast furnace slag (GGBS) concrete by comparing ten ML models. In addition, this work presents the data proportionality effect on the performance and overfitting of ML models. For that purpose, a database has been compiled from the literature and created three data sets (training: testing), i.e., 70%:30%, 80%:20%, and 85%:15%. The analysis of performance metrics (correlation coefficient of 0.8526 and 0.9780 for 70%:30% and 85%:15%, respectively) presented that the performance of Takagi Sugeno Fuzzy (TSF) model has been enhanced with the database. The TSF model has predicted CST of GGBS concrete with a root mean square error of 3.2460 MPa and performance index of 1.86. In addition, the regression error characteristics curve, score analysis, and uncertainty analysis showed the superiority of the TSF model. Conversely, the a20 (= 93.75), agreement (= 0.90), and scatter (= 0.08) indexes presented that the TSF model is highly reliable in predicting the CST of GGBS concrete. The multicollinearity analysis revealed that the considerable multicollinearity of GGBS to binder ratio and fine aggregate features affected the performance and curve fitting of k-nearest neighbor and multilayer perceptron models. Overall analysis shows that 85% training data set improves generalization by capturing diverse data patterns and minimizes noise and outliers, resulting in a more robust model. The present investigation helps concrete designers and engineers assess the desired CST of GGBS concrete using mixed design parameters.

Graphical abstract

Keywords

compressive strength / sustainable concrete / concrete technology / multicollinearity / Takagi−Sugeno fuzzy / artificial intelligence

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