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Abstract Machine learning models for ultra-high-performance concrete (UHPC) commonly report coefficients of determination above 0.94 on held-out records, and these values now shape expectations for data-driven mix design. We show that a substantial part of this reported predictive performance depends on how the models are evaluated. Benchmark UHPC datasets contain replicate records, meaning the same mixture tested at several curing ages or specimen sizes.
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