Design of UHPC using Artificial Neural Networks E. Ghafari (1) , M. Bandarabadi (2) , H. Costa (3) , E. Júlio (4) (1) ICIST & Dep. of Civil Engineering, University of Coimbra, Portugal (2) CISUC, Dep. of Informatics Engineering, University of Coimbra, Portugal (3) ICIST & Polytechnic Institute of Coimbra – ISEC, Coimbra, Portugal (4) ICIST& DECivil IST, Technical University of Lisbon, Portugal ABSTRACT Ultra-high performance concrete (UHPC) results from the mixture of several constituents giving rise to a highly complex material in hardened state. The higher number of constituents in relation to current concrete, together with a higher number of possible combinations and relative proportioning, makes the behavior of this type of concrete more difficult to predict. Until now, most of the proposed mixture design methods are based on a trial and error procedure, which is expensive and work intensive. Moreover, these methods are not efficient in predicting neither the consistency in fresh state nor the strength in hardened state, and do not consider the effect of curing on the latter. The main objective of the research study herein described is to build an analytical model, based on artificial neural networks (ANN), to predict the required performance of UHPC. Specifically, back-propagation neural networks (BPNN) are adopted to model the relation between the input and the output parameters of UHPC, for two different curing conditions, including heat treatment and water storage. In order to train the neural network, a total set of 53 different mixtures were designed. It is concluded that the developed model can be used as a reliable method to predict the performance of UHPC. Keywords Ultra-high performance concrete, mixture design, artificial neural networks INTRODUCTION Ultra-high performance concrete (UHPC) results from the mixture of several constituents giving rise to a highly complex material in hardened state. The higher number of constituents in relation to current concrete, together with a higher number of possible combinations and relative proportioning, makes the behavior of this type of concrete more difficult to predict. Dewar et al. [1] presented a model for predicting the packing density of multi-sized aggregate from the grading of the aggregate. However, it has been proved that applying the hypothetical models developed for aggregate particles is not efficient. The same conclusion was drawn relatively to model the cementitious materials due to differences in packing behavior [2]. De Larrard [3] has also presented a packing model called “compressible packing model”, to determine the packing density achieved by a granular mixture. Fennis et al. [4] explained that with small particles, the packing density is influenced by the interaction caused by surface forces, such as van der Waals forces, electrical charges and steric forces; therefore, for very small particles, particle packing predictions by CPM deviate considerably from experiments data and, thus, the current CPM model cannot accurately predict the behavior of the mixtures containing micro- and nano-sized particle. Fennis et al.[4] presented a new method based on particle density of powder using centrifugal procedure. They indicated that by combining cement and fillers in a smart way, for instance by replacing cement with fly ash, the packing density of a concrete mixture can be increased and the water