Neural network-based prediction of 7-days compressive strength of UHPC incorporating SCM

Abstract

Ultra-high-performance concrete (UHPC) is a high-tech kind of concrete which exhibits superb mechanical properties and improved durability. Over the last years the use of supplementary cementitious materials (SCM) as partial substitution of cement and silica fume has been the object of great interest by the scientific community in order to reduce the high costs and carbon footprint of UHPC. Some of the more promising applications of this type of special concrete, such as the seismic retrofitting of non-ductile existing structures, require the development of high or ultra-high early strength. However, the replacement of cement and silica fume can result in the modification of some properties such as the early strength of UHPC, which usually needs great amounts of cement and silica fume. On the other hand, the use of several SCM has as outcome a highly complex material, which makes it more difficult to understand the effect of each component and their interactions on early strength. This study is aimed to develop an artificial neural networks (ANN) approach to predict the seven-day compressive strength of UHPC, being able to incorporate several SCM such as silica fume, fly ash, ground granulated blast furnace slag, recycled glass powder, rice husk ash, fluid catalytic cra-cking catalyst residue, metakaolin, limestone powder, in addition to mineral filler such as quartz powder. A total of 523 data from previous published works was used to train the one-hidden-layer ANN model. The model was also validated by performing experimental works. Besides, Connection-Weight-Approach algo-rithm (CWA) was used to analyse the relationships between the UHPC‟s components and the seven-day compressive strength. The results pointed out that the ANN is an efficient model for predicting the early strength (7-day compressive strength) of UHPC even when SCM are incorporated.