From Experimental Studies to Predictive Machine Learning Modelling : Polypropylene Fibre Reinforced Concrete
(2025) In Results in Materials- Abstract
- 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... (More)
- 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. (Less)
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https://lup.lub.lu.se/record/2b126449-fad7-48d0-a838-5b766cea11a9
- author
- Bayat Pour, Mohsen LU
- organization
- publishing date
- 2025-10-07
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Fibre reinforced concrete, Polypropylene, Machine learning, Compressive strength, Tensile strength, Modulus of elasticity, Permeability
- in
- Results in Materials
- pages
- 16 pages
- publisher
- Elsevier
- external identifiers
-
- scopus:105018179345
- ISSN
- 2590-048X
- DOI
- 10.1016/j.rinma.2025.100777
- language
- English
- LU publication?
- yes
- id
- 2b126449-fad7-48d0-a838-5b766cea11a9
- date added to LUP
- 2025-10-24 20:58:21
- date last changed
- 2025-11-03 10:48:47
@article{2b126449-fad7-48d0-a838-5b766cea11a9,
abstract = {{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.}},
author = {{Bayat Pour, Mohsen}},
issn = {{2590-048X}},
keywords = {{Fibre reinforced concrete; Polypropylene; Machine learning; Compressive strength; Tensile strength; Modulus of elasticity; Permeability}},
language = {{eng}},
month = {{10}},
publisher = {{Elsevier}},
series = {{Results in Materials}},
title = {{From Experimental Studies to Predictive Machine Learning Modelling : Polypropylene Fibre Reinforced Concrete}},
url = {{http://dx.doi.org/10.1016/j.rinma.2025.100777}},
doi = {{10.1016/j.rinma.2025.100777}},
year = {{2025}},
}