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From Experimental Studies to Predictive Machine Learning Modelling: Polypropylene Fibre Reinforced Concrete

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Titel: From Experimental Studies to Predictive Machine Learning Modelling: Polypropylene Fibre Reinforced Concrete
Autoren: Bayat Pour, Mohsen
Weitere Verfasser: Lund University, Faculty of Engineering, LTH, Departments at LTH, Department of Building and Environmental Technology, Division of Structural Engineering, Lunds universitet, Lunds Tekniska Högskola, Institutioner vid LTH, Institutionen för bygg- och miljöteknologi, Avdelningen för Konstruktionsteknik, Originator, Lund University, Faculty of Engineering, LTH, LTH Profile areas, LTH Profile Area: Circular Building Sector, Lunds universitet, Lunds Tekniska Högskola, LTH profilområden, LTH profilområde: Cirkulär byggindustri, Originator
Quelle: Results in Materials. 28
Schlagwörter: Engineering and Technology, Civil Engineering, Building materials, Teknik, Samhällsbyggnadsteknik, Byggnadsmaterial, Mechanical Engineering, Solid and Structural Mechanics, Maskinteknik, Solid- och strukturmekanik
Beschreibung: This study investigates the influence of polypropylene fibres (PF) on concrete performance across a finely resolved dosage spectrum (0.0–2.0% by cement mass, in 0.1% increments) using 2,100 laboratory specimens. The experimental programme evaluated compressive strength, tensile strength (flexural and splitting), modulus of elasticity, and water penetration depth. Predictive modelling was conducted using Random Forests (RF) and Support Vector Regression (SVR), trained and evaluated with simple cross-validation and benchmarked using mean absolute error (MAE) and coefficient of determination (R²). The results reveal distinct optima for strength indices and a threshold behaviour in permeability, with PF dosages between 0.2% and 0.6% balancing mechanical enhancement and substantial reductions in water penetration, although accompanied by a pronounced reduction in elastic modulus at very low PF contents. The RF models exhibited superior predictive performance, consistently outperforming SVR across properties. The experimental outcomes demonstrate that incorporating PF into the concrete mixture enhances its mechanical properties. However, the optimal fibre-to-cement ratios differ for various properties: compressive strength (0.3% to 0.4%), tensile strength (0.2% to 0.4%), modulus of elasticity (0.1%), and permeability (0.6%). The overall optimal fibre range is identified as 0.1% to 0.6%, which satisfies all specified criteria. Notably, the inclusion of PF results in a 60% increase in compressive strength, a 115% increase in tensile strength (bending test), a 288% increase in tensile strength (Brazilian test), a tenfold reduction in modulus of elasticity, and a twenty-fivefold reduction in permeability.
Zugangs-URL: https://doi.org/10.1016/j.rinma.2025.100777
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  Data: From Experimental Studies to Predictive Machine Learning Modelling: Polypropylene Fibre Reinforced Concrete
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  Data: This study investigates the influence of polypropylene fibres (PF) on concrete performance across a finely resolved dosage spectrum (0.0–2.0% by cement mass, in 0.1% increments) using 2,100 laboratory specimens. The experimental programme evaluated compressive strength, tensile strength (flexural and splitting), modulus of elasticity, and water penetration depth. Predictive modelling was conducted using Random Forests (RF) and Support Vector Regression (SVR), trained and evaluated with simple cross-validation and benchmarked using mean absolute error (MAE) and coefficient of determination (R²). The results reveal distinct optima for strength indices and a threshold behaviour in permeability, with PF dosages between 0.2% and 0.6% balancing mechanical enhancement and substantial reductions in water penetration, although accompanied by a pronounced reduction in elastic modulus at very low PF contents. The RF models exhibited superior predictive performance, consistently outperforming SVR across properties. The experimental outcomes demonstrate that incorporating PF into the concrete mixture enhances its mechanical properties. However, the optimal fibre-to-cement ratios differ for various properties: compressive strength (0.3% to 0.4%), tensile strength (0.2% to 0.4%), modulus of elasticity (0.1%), and permeability (0.6%). The overall optimal fibre range is identified as 0.1% to 0.6%, which satisfies all specified criteria. Notably, the inclusion of PF results in a 60% increase in compressive strength, a 115% increase in tensile strength (bending test), a 288% increase in tensile strength (Brazilian test), a tenfold reduction in modulus of elasticity, and a twenty-fivefold reduction in permeability.
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