Neural network-aided prediction of post-cracking tensile strength of fibre-reinforced concrete. (November 2021)

• Record Type: Journal Article
• Authors: Ikumi, T.; Galeote, E.; Pujadas, P.; de la Fuente, A.; López-Carreño, R.D.
• Is Part Of: Computers & structures. Volume 256(2021)
• Publication Date: 2021-11
• Subjects: Artificial neural network -- Fibre-reinforced concrete -- Residual strength -- Tensile strength; Structural engineering -- Data processing -- Periodicals; Electronic data processing -- Structures, Theory of -- Periodicals; 624.171
• DOI: 10.1016/j.compstruc.2021.106640
• Languages: English
• ISSNs: 0045-7949

Abstract

Graphical abstract: Highlights: A multilayer perceptron neural network is proposed for Fibre Reinforced Concrete. The post-cracking tensile strength is successfully predicted. Analytical expressions are provided as an alternative tool to traditional testing. The model has pre-design, quality control and reinforcement optimization purposes. The model has proved to be coherent with the established knowledge. Abstract: Structural fibres are an effective method to improve concrete post-cracking tensile strength ( f ctR ). Currently, the characterization of this property is mainly performed experimentally. This is a source of uncertainties at design stages, which hinders the development of new fibre type and/or optimization of those currently existing. This paper presents a multilayer perceptron neural network to predict f ctR of fibre-reinforced concrete (FRC) subjected to the Barcelona Test. The optimal architecture of the predictor is obtained by evaluating 9216 configurations of input dimension and number of hidden layers and neurons. The generalization performance is assessed using repeated random sub-sampling validation with 50 iterations. The final model can predict with high accuracy the f ctR of FRC for different cracking stages. A parametric analysis is performed to prove coherence between the results predicted by the model and the established understanding of the FRC behaviour. Finally, numerical expressions are provided as an alternative tool to traditional testing to predict the residual strength of the Barcelona Test for pre-design and quality control purposes based on fibre dosage, concrete strength, specimen type and height and fibre geometric characteristics. These type of approaches are found to be necessary for boosting the development of the FRC technology.