Special Time Series Prediction: Creep of Concrete
This paper presents an algorithm, different from the classical time series, specialised in extracting knowledge from time series. The algorithm, based on Genetic Programming, enables the dynamic introduction of non-terminal operators shaped as mathematical expressions (operator-expression) that works as an unique node for the purpose of genetic operations (crossover and mutation). A new characteristic of this algorithm is the possibility of expansion the individuals, which, besides inducing a better global fitness, enables breaking up the expressions (operator-expression) into basic operators in order to achieve expression recombination. The performance of the implemented algorithm was showed by means of its application to the creep of structural concrete, a specific case of Construction Engineering where a best adjustment to the current regulative codes was subsequently achieved.
KeywordsData Mining Evolutionary Computation Genetic Programming Civil Engineering
Unable to display preview. Download preview PDF.
- 2.Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Michigan (1975)Google Scholar
- 5.Brown, M., Harris, C.: Neurofuzzy adaptive modelling and control. Prentice-Hall, Hertfordshire, UK (1994)Google Scholar
- 12.Cladera, A., Marí, A.R.: Shear design procedure for reinforced normal and high-strength concrete beams using artificial neural networks. Part I: beams without stirrups. Eng. Struct. 26, 917–926 (2004)Google Scholar
- 13.Réunion Internationale des Laboratoires et Experts des Matériaux, systèmes de construction et ouvrages, http://www.rilem.net
- 14.ACI Committee 209: Prediction of Creep, Shrinkage and Temperature Effects in Concrete Structures. ACI 209-82. American Concrete Institute, Detroit (1982) Google Scholar
- 15.Müller, H.S., Hilsdorf, H.K.: Evaluation of the Time Dependent Behavior of Concrete. CEB Comite Euro-International du Beton. Bulletin d’Inforrnation No 199. France (1990)Google Scholar