Title:
Prediction of Cement Degree of Hydration Using Artificial Neural Networks
Author(s):
Adnan A. Basma, Samer Barakat, and Salim Al-Oraimi
Publication:
Materials Journal
Volume:
96
Issue:
2
Appears on pages(s):
167-172
Keywords:
curing; hydration; models
DOI:
10.14359/441
Date:
3/1/1999
Abstract:
This paper presents the development of a computer model for the prediction of cement degree of hydration a. The model is established by incorporating large experimental data sets using neural networks technology. Neural networks are computational paradigms, primarily based on the structural formation and 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 Sets 1, 2, and 3. Once the neural networks have been deemed fully trained, verification of the performance is then carried out using Sets 4 and 5 of the experimental data, which were not included in the training phase. The results indicated that the neural network is very efficient in predicting concrete degree of hydration with great accuracy using minimal processing of data.