Next Article in Journal
Estimation of the Effects of the Cross-Head Speed and Temperature on the Mechanical Strength of Kenaf Bast Fibers Using Weibull and Monte-Carlo Statistics
Next Article in Special Issue
Effect of High Temperature on the Mechanical Properties of Steel Fiber-Reinforced Concrete
Previous Article in Journal
Fiber Microsphere Coupled in a Taper for a Large Curvature Range
Previous Article in Special Issue
Enhancing the Punching Load Capacity of Reinforced Concrete Slabs Using an External Epoxy-Steel Wire Mesh Composite
Open AccessArticle

ANN-Based Shear Capacity of Steel Fiber-Reinforced Concrete Beams without Stirrups

1
Abambres’ Lab, 1600-275 Lisbon, Portugal
2
Escola de Tecnologias e Engenharia, Instituto Superior de Educação e Ciências (ISEC), 1750-142 Lisbon, Portugal
3
Politécnico, Universidad San Francisco de Quito, Sector Cumbaya, EC 170157 Quito, Ecuador
4
Department of Civil Engineering and Geosciences, Delft University of Technology, 2628 CN Delft, The Netherlands
*
Author to whom correspondence should be addressed.
Fibers 2019, 7(10), 88; https://doi.org/10.3390/fib7100088
Received: 30 August 2019 / Revised: 25 September 2019 / Accepted: 26 September 2019 / Published: 11 October 2019
(This article belongs to the Special Issue Steel Fibre Reinforced Concrete Behaviour)
Comparing experimental results of the shear capacity of steel fiber-reinforced concrete (SFRC) beams without stirrups to the capacity predicted using current design equations and other available formulations shows that predicting the shear capacity of SFRC beams without mild steel shear reinforcement is still difficult. The reason for this difficulty is the complex mechanics of the problem, where the steel fibers affect the different shear-carrying mechanisms. Since this problem is still not fully understood, we propose the use of artificial intelligence (AI) to derive an expression based on the available experimental data. We used a database of 430 datapoints obtained from the literature. The outcome is an artificial neural network-based expression to predict the shear capacity of SFRC beams without shear reinforcement. For this purpose, many thousands of artificial neural network (ANN) models were generated, based on 475 distinct combinations of 15 typical ANN features. The proposed “optimal” model results in maximum and mean relative errors of 0.0% for the 430 datapoints. The proposed model results in a better prediction (mean Vtest/VANN = 1.00 with a coefficient of variation 1 × 10−15) as compared to the existing code expressions and other available empirical expressions, with the model by Kwak et al. giving a mean value of Vtest/Vpred = 1.01 and a coefficient of variation of 27%. Until mechanics-based models are available for predicting the shear capacity of SFRC beams without shear reinforcement, the proposed model thus offers an attractive solution for estimating the shear capacity of SFRC beams without shear reinforcement. With this approach, designers who may be reluctant to use SFRC because of the large uncertainties and poor predictions of experiments, may feel more confident using the material for structural design. View Full-Text
Keywords: artificial neural networks; beams; database; design formula; fiber-reinforced concrete; shear; steel fibers artificial neural networks; beams; database; design formula; fiber-reinforced concrete; shear; steel fibers
Show Figures

Figure 1

MDPI and ACS Style

Abambres, M.; Lantsoght, E.O. ANN-Based Shear Capacity of Steel Fiber-Reinforced Concrete Beams without Stirrups. Fibers 2019, 7, 88.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop