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Advances in Concrete Construction Volume 20, Number 4, October 2025 , pages 281-303 DOI: https://doi.org/10.12989/acc.2025.20.4.281 |
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A step towards the production of eco-friendly non-proprietary ultra-high performance fiber reinforced concrete: Experimental assessment and machine learning quantification |
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Turki S. Alahmari and Brad D. Weldon
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| Abstract | ||
| This study investigates the mechanical performance of eco-friendly non-proprietary ultra-high-performance fiberreinforced concrete (UHPC) using locally available cementitious materials and evaluates the effect of ambient and thermal curing regimes over short- and long-term durations. Key mechanical properties, including compressive strength, first-peak flexural strength, and modulus of elasticity (MOE), were experimentally assessed. Results demonstrated that thermal curing significantly accelerated early-age strength development, with short-term compressive and flexural strength enhancements ranging from 77% to 90%, and long-term gains of 10% to 30% compared to ambient curing. MOE exhibited consistent growth across both curing methods, achieving values up to 52 GPa. Predictive equations for MOE and first-peak flexural strength were established to aid in design applications. Furthermore, two machine learning models—Random Forest (RF) and k-Nearest Neighbors (KNN)—were employed to predict mechanical performance. The RF model outperformed KNN across all metrics, achieving correlation coefficients (R2) between 0.93 and 0.99 and minimal error values (RMSE < 1.21 for compressive strength). These findings validate the potential of non-proprietary UHPC as a sustainable alternative to commercial mixes and provide a predictive framework for its behavior under varying curing conditions, advancing its practical implementation in structural applications. | ||
| Key Words | ||
| ambient curing; local materials; Machine Learning (ML); mechanical properties; thermal curing; Ultra-High-Performance Concrete (UHPC) | ||
| Address | ||
| (1) Turki S. Alahmari: Department of Civil Engineering, Faculty of Engineering, University of Tabuk, P.O. Box 741, Tabuk 71491, Saudi Arabia; (2) Brad D. Weldon: Department of Civil & Environmental Engineering & Earth Sciences, University of Notre Dame, Notre Dame, Indiana, USA. | ||
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