Knowledge of the tension softening process of concrete is essential to understand fracture
mechanism, further to analyze fracture behaviour, and further to evaluate properties of concrete. For
the last eight years, many different tests on uniaxial tension with elimination of secondary flexure
were performed in Tohoku Institute of Technology. The paper is dedicated to predict tension
softening curve of concrete by using artificial neural networks (ANNs) based on experimental data of
five different mixtures of concrete (including High Performance Concrete). It is an advantage to
predict a proper tension softening curve without performing uniaxial tension tests. Several artificial
neural networks with different architectures (with various hidden neurons and layers) were studied
using software - Statistica Neural Network. In order to evaluate the prediction accuracy, tension
softening curve and other fracture parameters were predicted for each mix from the other four mixes
and compared with the omitted data of the relevant mix. High accuracy was obtained in the all
predicted tension softening curves and the fracture parameters were also well predicted.
Incremental displacement collocation method for the evaluation of tension softening curve of mortar
