Predicting the Tensile Behaviour of Ultra-High Performance Fibre-Reinforced Concrete from Single-Fibre Pull-Out Tests

Abstract

In this paper, a prediction model for the tensile behaviour of ultra-high performance fibre-reinforced concrete is proposed. It is based on integrating force contributions of all fibres crossing the crack plane. Piecewise linear models for the force contributions depending on fibre orientation and embedded length are fitted to force–slip curves obtained in single-fibre pull-out tests. Fibre characteristics in the crack are analysed in a micro-computed tomography image of a concrete sample. For more general predictions, a stochastic fibre model with a one-parametric orientation distribution is introduced. Simple estimators for the orientation parameter are presented, which only require fibre orientations in the crack plane. Our prediction method is calibrated to fit experimental tensile curves.

Riesz Networks: Scale-Invariant Neural Networks in a Single Forward Pass

“…Nowadays, Fourth row: Gaussian derivative networks aggregated on growing subsets of scale set {1.5, 3, 6, 12} (from left to right). Image size 400 × 400 pixels fibers have become widely used as reinforcement in concrete creating a new class of reinforced concrete materials, e.g., ultrahigh-performance fiber-reinforced concrete [49][50][51]. A variety of materials can be used as fiber material, including glass, carbon, and basalt.…”