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Computers and Concrete Volume 38, Number 3, September 2026 , pages 407-427 DOI: https://doi.org/10.12989/cac.2026.38.3.01 |
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Mechanical properties of steel fiber-reinforced concrete using neural networks |
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Julian Carrillo, Esteban Lasso, Orlando Arroyo
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| Abstract | ||
| Steel fiber reinforced concrete (SFRC) is a material with increased tensile strength and residual capacity compared to traditional plain concrete. SFRC has seen wider adoption in multiple countries following its inclusion in building codes that specify minimum requirements for strength and residual capacity. Numerous models have been developed to relate the mechanical properties of SFRC to its mixture characteristics. Empirical equations have been predominantly utilized in these studies, whereas models leveraging Machine Learning techniques remain relatively underexplored. This paper introduces an artificial neural network (ANN) approach for estimating the mechanical properties of SFRC. To this end, a database comprising results from 3- and 4-point bending tests (3PBT, 4PBT), double punch tests (DPT), splitting tensile tests (STT) and shear tests (SHT) was assembled and used as input data for training both ANN and traditional models. The results show that artificial neural networks (ANNs) outperform traditional empirical models in predicting SFRC's mechanical properties, achieving correlation coefficients above 0.9 with minimal root mean square errors and mean average pondered errors for indirect-tensile strength under STT, and residual flexural-tensile strengths under 3PBT, 4PBT, and DPT. A GUI tool has been developed and is included with both models to support user applications. | ||
| Key Words | ||
| double punch; neural networks 3PBT; neural networks 4PBT; shear; splitting tensile; steel fiber reinforced concrete | ||
| Address | ||
| Julian Carrillo: 1) Department of Civil Engineering, Universidad Militar Nueva Granada, Colombia; 2) Colombian Earthquake Engineering Research Network, Colombia Esteban Lasso: Department of Civil Engineering, Universidad Militar Nueva Granada, Colombia Orlando Arroyo: 1) Department of Civil Engineering, Universidad Industrial de Santander, Colombia; 2) Colombian Earthquake Engineering Research Network, Colombia | ||