Fig. 1: Four paradigms of concrete science: empirical, theoretical, computational, and data-driven.
From: Machine learning in concrete science: applications, challenges, and best practices
From left to right, representative examples include: trial-and-error, iterative experiments (proportioning, processing, and characterization); numerical simulations on cement hydration (e.g., hydration, morphology, and structural development model, HYMOSTRUC10); molecular dynamics simulations on cement hydrates (e.g., calcium silicate hydrate gel224); and artificial intelligence and machine learning predictions using experimental and computational data. Figure adapted with permission from ref. 225, CC BY 4.0.
