Modeling Slump Flow of Concrete Using Genetic Programming

Abstract:

The present study applied genetic programming (GP) to estimate the slump flow of high-performance concrete (HPC) using seven concrete ingredients. GP optimizes functions and their associated coefficients simultaneously and is suitable to automatically discover complex relationships between nonlinear systems. The results demonstrated that GP generates a more accurate formula and has lower estimating errors for predicting the slump flow of HPC than multiple linear regressions (MLRs).

• Periodical: Advanced Materials Research (Volumes 211-212)
• Pages: 374-378
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.211-212.374
• Online since: February 2011
• Authors: Hsun Hsin Hsu, Li Chen, Chang Huan Kou, Chang Hung Chiu
• Keywords: Genetic Programming, High-Performance Concrete (HPC), Slump Flow

References

[1] I. C. Yeh: Modeling slump flow of concrete using second-order regressions and artificial neural networks. Cement & Concrete Composites, Vol. 29 (2007), pp.474-480.

DOI: 10.1016/j.cemconcomp.2007.02.001

[2] M. Y. Mansour: Predicting the shear strength of RC beams using ANN. Eng Struct, Vol. 26 (2004), pp.781-799.

[3] A. Baykasoğlu, T. Dereli, S. Tanıs: Prediction of cement strength using soft computing techniques. Cement and Concrete Research, 34(11) (2004), p.2083-(2090).

DOI: 10.1016/j.cemconres.2004.03.028

[4] L. C. Lien, in: Applications of Genetic Algorithms in Reinforced Learning, Master's thesis. Dept. of Civ. Engrg. and Engrg. Informatics, Chung Hua Unniversity, Hsin Chu, Taiwan (2005), in Chinese.

[5] G. E. Pedro, V. Sebasti´an, H. Francisco: A Survey on the Application of Genetic Programming to Classification. IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews, 40(2) (2010), pp.121-144.

DOI: 10.1109/tsmcc.2009.2033566

[6] L. Chen: A study of applying macroevolutionary genetic programming to concrete strength estimation. Journal of Computing in Civil Engineering, ASCE, 17(4) (2003), pp.290-294.

DOI: 10.1061/(asce)0887-3801(2003)17:4(290)

[7] J. V. Ekaterina, Member, IEEE, F. S. Guido, Member, IEEE, D.D. Hertog: Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming. IEEE Transactions on Evolutionary Computation 13(2) (2009).

DOI: 10.1109/tevc.2008.926486

[8] J. R. Koza, in: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press (1992).

[9] I. C. Chen, in: Optimization the Mixture Design of High-performance Concrete by Neural Networks, Master's thesis. Dept. of Civ. Engrg. and Engrg. Informatics, Chung Hua Unniversity, Hsin Chu, Taiwan (2001), in Chinese.

[10] L. C. Lien, in: Applications of Genetic Algorithms in Reinforced Learning, Master's thesis. Dept. of Civ. Engrg. and Engrg. Informatics, Chung Hua Unniversity, Hsin Chu, Taiwan (2005), in Chinese.