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Ultra-high performance concrete (UHPC) surpasses conventional concrete in performance. However, ensuring consistent mechanical properties during production, even with identical recipes, remains challenging. Using experimental data, this study investigates how material quality, environmental conditions, measurement errors in material dosing, and mixing and curing conditions influence the mechanical properties of UHPC. This broad scope of influencing factors and production conditions increases data dimensionality and, coupled with the high cost of UHPC experiments, results in a sparse dataset. Traditional evolutionary algorithms, though effective in feature selection, struggle with high-dimensional small-sized datasets. To address this, a search-space-constraining method for the non-dominated sorting genetic algorithm II (NSGA-II) is introduced, incorporating domain-specific knowledge into population initialization to reduce dimensionality and thus enhance prediction accuracy and solution stability. Comparative evaluations using various machine learning algorithms on the UHPC dataset demonstrate that population initialization to constrain the search space of NSGA-II outperforms the standard NSGA-II. Finally, the significance of each examined factor in the UHPC manufacturing process for the properties of the final product is discussed.

at - automatisierungstechnik

Investigating quality inconsistencies in the ultra-high performance concrete manufacturing process using a search-space constrained non-dominated sorting genetic algorithm II