Prediction of cement degree of hydration using artificial neural networks

This paper presents the development of a computer model for the prediction of cement degree of hydration α. The model is established by incorporating large experimental data sets using the neural networks (NNs) technology. NNs are computational paradigms, primarily based of the structural formation and the knowledge processing faculties of the human brain. Initially, the degree of hydration was estimated in the laboratory by preparing portland cement paste with the water-cement ratio (w/c) ranging from 0.2 to 0.6, curing times from 0.25 to 90 days and subjected to curing temperatures from 3 to 43 C (37 to 109 F). A total of 390 specimens were tested, thus producing 195 data points divided into five sets. The networks were trained using data in Set 1, 2, and 3. Once the NNs have been deemed fully trained, verification of the performance is then carried out using Set 4 and 5 of the experimental data, which were not included in the training phase. The results indicated that the NNs are very efficient in predicting concrete degree of hydration with great accuracy using minimal processing of data.