An ensemble method for predicting the mechanical properties of strain hardening cementitious composites

In recent years, Artificial Neural Networks (ANN) models have proven effective in learning to predict material properties only from data. In the study of strain-hardening cementitious composites (SHCC), conducting laboratory experimentation to collect this data is expensive, and to reach reliable accuracy, tens of thousands of instances may be required. In this paper, a forest deep neural network (FDNN) ensemble model is presented. The proposed...