Predicting and optimizing the structural performance of rubber crumb – steel fibre wire - concrete using radial basis function approach of artificial neural network and 3D graphic calculator models

Waste tyres dumped into landfills have been contributing to environmental pollution. Recently, scraped tyres were incorporated into concrete to address both technical and environmental problems. Yet, the issue of concrete’s mechanical strengths reduction emanated from infusion of rubber crumb (RC) has not been resolved. This study investigates the influence of sodium hydroxide (NaOH) treated RC on the characteristic performance of concrete using experimental investigation data, radial basis function (RBF) approach of artificial neural network (ANN) and 3D graphing calculator (3DGC) model. The RC-SFW- concretes were produced using a mix ratio of 1:2:3, water-binder ratio of 0.4 with the addition of Optima 206 superplasticizer, 3% SFW being constant and varying RC content: 1.5, 2.5, 3.5 and 5.0%, respectively. The fresh and hardened properties of the composite were evaluated in accordance with SANS 5862–1: 2006, SANS 5863: 2006, and SANS 50,196–3. The optimum mechanical strengths of RC – SFW – concrete experimented and modelled by RBF – ANN are by 1.73–30.80% and 1.73–30.71% more than control at 1.5–5.0% of RC respectively showing the strong correlation between the experimented and model predicted values with good mechanical strengths increments. Furthermore, the 3DGC model optimized the RC – SFW – concrete’s mechanical strengths to be 3.9–84.8 MPa at 0–4% of RC. To buttress the point, the RBF – ANN model made a good prediction of RC – SFW – concrete’s mechanical strengths without outlier errors with average performance metric values of 0.701, 0.782, 0.113 and − 0.494 for coefficient of determination (R 2 ), root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute deviation (MAD) respectively. In conclusion, 1.5% RC together with 3.0% SFW has the best enhancement of concrete’s structural properties, thus, they are recommended for the production of sustainable concrete in the current construction sectors.

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Publication Details

Journal
Discover Materials
Published
2026-06-09
DOI
https://doi.org/10.1007/s43939-026-00714-3
Primary Topic
Innovative concrete reinforcement materials
Type
article
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article

Predicting and optimizing the structural performance of rubber crumb – steel fibre wire - concrete using radial basis function approach of artificial neural network and 3D graphic calculator models

Bolanle Deborah Ikotun, Rasheed Abdulwahab, Abiodun Kilani
Discover Materials
Innovative concrete reinforcement materials
article

Predicting and optimizing the structural performance of rubber crumb – steel fibre wire - concrete using radial basis function approach of artificial neural network and 3D graphic calculator models

Bolanle Deborah Ikotun, Rasheed Abdulwahab, Abiodun Kilani
article en

Abstract

Waste tyres dumped into landfills have been contributing to environmental pollution. Recently, scraped tyres were incorporated into concrete to address both technical and environmental problems. Yet, the issue of concrete’s mechanical strengths reduction emanated from infusion of rubber crumb (RC) has not been resolved. This study investigates the influence of sodium hydroxide (NaOH) treated RC on the characteristic performance of concrete using experimental investigation data, radial basis function (RBF) approach of artificial neural network (ANN) and 3D graphing calculator (3DGC) model. The RC-SFW- concretes were produced using a mix ratio of 1:2:3, water-binder ratio of 0.4 with the addition of Optima 206 superplasticizer, 3% SFW being constant and varying RC content: 1.5, 2.5, 3.5 and 5.0%, respectively. The fresh and hardened properties of the composite were evaluated in accordance with SANS 5862–1: 2006, SANS 5863: 2006, and SANS 50,196–3. The optimum mechanical strengths of RC – SFW – concrete experimented and modelled by RBF – ANN are by 1.73–30.80% and 1.73–30.71% more than control at 1.5–5.0% of RC respectively showing the strong correlation between the experimented and model predicted values with good mechanical strengths increments. Furthermore, the 3DGC model optimized the RC – SFW – concrete’s mechanical strengths to be 3.9–84.8 MPa at 0–4% of RC. To buttress the point, the RBF – ANN model made a good prediction of RC – SFW – concrete’s mechanical strengths without outlier errors with average performance metric values of 0.701, 0.782, 0.113 and − 0.494 for coefficient of determination (R 2 ), root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute deviation (MAD) respectively. In conclusion, 1.5% RC together with 3.0% SFW has the best enhancement of concrete’s structural properties, thus, they are recommended for the production of sustainable concrete in the current construction sectors.

Discover Materials
University of South Africa (ZA)
Sustainable cities and communities
Openalex Percentile: Top 10%
Innovative concrete reinforcement materials
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