Evolving Flexible Neural Tree Model for Portland Cement Hydration Process
- 2.3k Downloads
The hydration of Portland cement is a complicated process and still not fully understood. Much effort has been accomplished over the past years to get the accurate model to simulate the hydration process. However, currently existing methods using positive derivation from the conditions for physical-chemical reaction are lack of information in real hydration data. In this paper, one model based on Flexible Neural Tree (FNT) with acceptable goodness of fit was applied to the prediction of the cement hydration process from the real microstructure image data of the cement hydration which has been obtained by Micro Computed Tomography (micro-CT) technology. Been prepared on the basis of previous research, this paper used probabilistic incremental program evolution (PIPE) algorithm to optimize the flexible neural tree structure, and particle swarm optimization (PSO) algorithm to optimize the parameters of the model. Experimental results show that this method is efficient.
KeywordsFlexible Neural Tree Probabilistic Incremental Program Evolution Particle Swarm Optimization Cement Hydration
Unable to display preview. Download preview PDF.
- 2.Tennis, P.D., Bhatty, J.I.: Characteristics of Portland and Blended Cements Results of a Survey of Manufacturers. In: Cement Industry Technical Conference, pp. 156–164. IEEE Press, Holly Hill (2006)Google Scholar
- 3.Tomosawa, F.: Kinetic Hydration Model of Cement. Cem. Concr. 23, 53–57 (1974)Google Scholar
- 5.Kondo, R., Kodama, M.: On the Hydration Kinetics of Cement. Semento Gijutsu Nenpo 21, 77–82 (1967)Google Scholar
- 6.Kondo, R., Ueda, S.: Kinetics and Mechanisms of the Hydration of Cements. In: Proceedings of the Fifth International Symposium on the Chemistry of Cement, pp. 203–248. Cement Association of Japan, Tokyo (1968)Google Scholar
- 8.Tomosawa, F.: Development of a Kinetic Model for Hydration of Cement. In: Proceedings of the 10th International Congress on the Chemistry of Cement, pp. 125–137. Amarkai AB and Congrex, Sweden (1997)Google Scholar
- 12.Kennedy, J., Mendes, R.: Population Structure and Particle Swarm Performance. In: Proceedings of the Congress on Evolutionary Computation, pp. 1671-1676. IEEE Press, Nanjing (2002)Google Scholar