Abstract
Based on the latest developments in concrete technology, High-Performance Concrete (HPC) is characterized by superior properties such as strength, workability, and durability. The modeling and precise proportioning of the concrete mixture are important for expanding the application of HPC. There have been many efforts to develop an expert system for HPC mix proportioning, but the systems developed from these efforts are still somewhat complicated, time-consuming, and have uncertain tasks. In this paper, to provide a tool for the convenient design of HPC mixtures, mix proportioning programs using a new meta-heuristic algorithm called “Harmony Search”, which was conceptualized based on a musical process of searching for a perfect harmony, is devised. The mix design programs developed by using harmony search algorithm were compared to other mix design approaches such as neural networks and genetic algorithm. The results showed that the harmony search algorithm can be a potentially strong tool for HPC mix proportioning.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Caldas, L. G. and Norford, L. K. (2002). “A design optimization tool based on a genetic algorithm.” Autom. Constr., Vol. 11, No. 2, pp 173–184
Cantú-Paz, E. and Goldberg, D. E. (2000). “Efficient parallel genetic algorithms: theory and practice.” Comput. Meth. Appl. Mech. Eng., Vol. 186, Nos. 2–4, pp. 221–238
Deb, K. (2000). “An efficient constraint handling method for genetic algorithms.” Comput. Meth. Appl. Mech. Eng., Vol. 186, Nos. 2–4, pp. 311–338
Geem, Z. W., Kim, J. H. and Loganathan, G. V. (2001). “A new heuristic optimization algorithm: Harmony search.” Simulation, Vol. 76, No 2, pp. 60–68
Jenkins, W. M. (2002). “A truss analogy and neural network for reinforced concrete deep beam analysis.” Proc. Inst. Civil Eng.-Struct. Build., Vol. 152, No. 3, pp. 259–267
Lee, K. S. and Geem, Z. W. (2005). “A new meta-heuristic algorithm for continuous engineering optimization: Harmony search theory and practice.” Comput. Meth. Appl. Mech. Eng., Vol. 194, Nos. 36–38, pp. 3902–3933
Lim, C. H., Yoon, Y. S. and Kim, J. H. (2004). “Genetic algorithm in mix proportioning of high-performance concrete.” Cem. Concr Res., Vol. 34, No. 3, pp. 409–420
Mahdavi, M., Fesanghary, M., and Damangir, E. (2007). “An improved harmony search algorithm for solving optimization problems.” Appl Math. Comput., Vol. 188, No. 2, pp. 1567–1579
Maru, S. and Nagpal, A. K. (2004). “Neural network for creep and shrinkage deflections in reinforced concrete frames.” J. Comput Civil. Eng., Vol. 18, No. 4, pp. 350–359
Miles, J. C., Sisk, G. M. and Moore, C. J. (2001). “The conceptual design of commercial buildings using a genetic algorithm.” Comput Struct., Vol. 79, No. 17, pp. 1583–1592
Nanakorn, P. and Meesomklin, K. (2001). “An adaptive penalty function in genetic algorithms for structural design optimization.” Comput. Struct., Vol. 79, Nos. 29–30, pp. 2527–2539
Oh, J. W., Lee, I.W., Kim, J. T. and Lee, G. W. (1999). “Application of neural networks for proportioning of concrete mixes.” ACI Mater. J., Vol. 96, No. 1, pp. 61–67
Oreta, A. W. C. and Kawashima, K. (2003). “Neural network modeling of confined compressive strength and strain of circular concrete columns.” J. Struct. Eng.-ASCE, Vol. 129, No. 4, pp. 554–561
Oztas, A., Pala, M., Ozbay, E., Kanca, E., Caglar, N. and Bhatti, M. A (2006). “Predicting the compressive strength and slump of high strength concrete using neural network.” Constr. Build. Mater., Vol 20, No. 9, pp. 769–775
Peng, J., Li, Z. and Ma, B. (2002). “Neural network analysis of chloride diffusion in concrete,” J. Mater. Civ. Eng., Vol. 14, No. 4, pp. 327–333
Seleemah, A. A. (2005). “A neural network model for predicting maximum shear capacity of concrete beams without transverse reinforcement.” Can. J. Civ. Eng., Vol. 32, No. 4, pp. 644–657
Sobolev, K. (2004). “The development of a new method for the proportioning of high-performance concrete mixtures.” Cem. Concr Compos., Vol. 26, No. 7, pp. 901–907
Xu, H. and Humar, J. (2006). “Damage detection in a girder bridge by artificial neural network technique.” Comput.-Aided Civil Infrastruct Eng., Vol. 21, No. 6, pp. 450–464
Yang, S. I., Yoon, Y. S., Lee, S. H. and Kim, G. D. (2003). “Applications of artificial neural networks for using high performance concrete.” Korean Society of Hazard Mitigation, Vol. 3, No. 4, pp. 119–129
Yeh, I.-C. (1999). “Design of high-performance concrete mixture using neural networks and nonlinear programming.” J. Comput. Civil Eng., Vol. 13, No. 1, pp. 36–42
Yeung, W. T. and Smith, J. W. (2005). “Damage detection in bridges using neural networks for pattern recognition of vibration signatures.” Eng. Struct., Vol. 27, No. 5, pp. 685–698
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Lee, JH., Yoon, YS. & Kim, JH. A new heuristic algorithm for mix design of high-performance concrete. KSCE J Civ Eng 16, 974–979 (2012). https://doi.org/10.1007/s12205-012-1011-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12205-012-1011-0