Early Prediction of Characteristic Compressive Strength of Concrete Based on Mix Proportions Using Modified Dimensional Analysis

Concrete technology research has marked concrete's compressive strength as a critical indicator for predicting other vital mechanical properties. Existing models for forecasting this strength—from empirical formulas to complex machine learning algorithms—suffer from issues like lack of rationality and dimensional consistency. This study introduces the Modified Dimensional Analysis (MDA) method, which overcomes these challenges by ensuring dimensional homogeneity and mathematical stability, facilitating its use via simple tools like spreadsheets or calculators. We evaluated MDA against conventional models such as Abrams' law and various machine learning approaches, including decision trees, random forests, support vector machines, multiple linear regression, and artificial neural networks. Our findings reveal that MDA exhibits impressive data efficiency, requiring only 60% less training data than all other machine learning algorithms. It demonstrates a testing average R-square value of 0.94 and a maximum RMSE value of 5.55 MPa, which is competitive compared to models trained on 60% more datasets. Furthermore, if adjusted for specific construction sites, MDA will prove to be a robust on-field instrument, responsive to alterations in mix proportions, offering critical, timely insights for site engineers and supervisors. This adaptability allows for immediate, informed interventions, potentially improving construction outcomes.