Browse > Article
http://dx.doi.org/10.4334/JKCI.2008.20.5.627

Concrete Optimum Mixture Proportioning Based on a Database Using Convex Hulls  

Lee, Bang-Yeon (Dept. Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology)
Kim, Jae-Hong (Dept. Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology)
Kim, Jin-Keun (Dept. Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology)
Publication Information
Journal of the Korea Concrete Institute / v.20, no.5, 2008 , pp. 627-634 More about this Journal
Abstract
This paper presents an optimum mixture design method for proportioning a concrete. In the proposed method, the search space is constrained as the domain defined by the minimal convex region of a database, instead of the available range of each component and the ratio composed of several components. The model for defining the search space which is expressed by the effective region is proposed. The effective region model evaluates whether a mix-proportion is effective on processing for optimization, yielding highly reliable results. Three concepts are adopted to realize the proposed methodology: A genetic algorithm for the optimization; an artificial neural network for predicting material properties; and a convex hull for evaluating the effective region. And then, it was applied to an optimization problem wherein the minimum cost should be obtained under a given strength requirement. Experimental test results show that the mix-proportion obtained from the proposed methodology using convex hulls is found to be more accurate and feasible than that obtained from a general optimum technique that does not consider this aspect.
Keywords
optimum mix-proportion; database; genetic algorithm; artificial neural network; convex hull;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Flood, I. and Kartam, N., "Neural Networks in Civil Engineering. I: Principles and Understanding," Journal of Computing in Civil Engineering, Vol. 8, No. 2, 1994, pp. 132-148.
2 이방연, 김재홍, 김진근, "데이터베이스의 영역 특성을 고려한 콘크리트 최적 배합 선정 기법," 한국콘크리트학 회 봄학술발표회 논문집, 18권, 1호, 2006, pp. 621-624.
3 이방연, 김재홍, 김진근, 이성태, "유효 영역 판별 모델 에 따른 데이터베이스 기반 콘크리트 최적 배합 선정," 한국콘크리트학회 가을학술발표회 논문집, 18권, 2호, 2006. 11, pp. 909-913.
4 Noguchi, T., A Study on the Mechanical Properties of High- Strength Concrete, Doctoral Dissertation, Tokyo University, 1995.
5 Thomas, H. C., Charles, E. L., Ronald, L. R., and Clifford, S., Introduction to Algorithms, 2nd ed., MIT Press, pp. 947-957.
6 Kim, D. K., Lee, J. J., Lee, J. H., and Chang, S. K., "Application of Probabilistic Neural Networks for Prediction of Concrete Strength," Journal of Materials in Civil Engineering, Vol. 17, No. 3, 2005, pp. 353-362.   DOI   ScienceOn
7 Lai, S. and Sera, M., "Concrete Strength Prediction by Means of Neural Network," Construction and Building Materials, Vol. 11, No. 2, 1997, pp. 93-98.   DOI   ScienceOn
8 Barron, A. R., "Universal Approximation Bounds for Superpositions of a Sigmoidal Function," IEEE Transactions of Information Theory, Vol. 39, No. 3, 1993, pp. 930-945.   DOI   ScienceOn
9 Barber, C. B., Dobkin, D. P., and Huhdanpaa, H. T., "The Quickhull Algorithm for Convex Hulls," ACM Transactions on Mathematical Software, Vol. 22, No. 4, 1996, pp. 469-483.   DOI   ScienceOn
10 Haupt, R. L. and Haupt, S. E., Practical Genetic Algorithms, New York, Wiley Interscience, 1998.
11 Lim, C. H., Yoon, Y. S., and Kim, J. H., "Genetic Algorithm in Mix Proportioning of High-Performance Concrete," Cement and Concrete Research, Vol. 34, 2004, pp. 409-420.   DOI   ScienceOn
12 Moody, J. E. and Yarvin, N., "Networks with Learned Unit Response Functions," In: Moody, J.E., Hanson, S.J., Lippmann, R.P., editors. Advances in Neural Information Processing Systems 4, Morgan Kaufmann Publishers, 1992, pp. 1048-1055.
13 Hecht-Nielsen. R., "Theory of the Backpropogation Neural Network," In Proceedings of International Joint Conference on Neural Networks, Washington, D.C., USA: IEEE, Vol. I, 1989, pp. 593-605.
14 Yeh, I. -C., "Computer-Aided Design for Optimum Concrete Mixtures," Cement and Concrete Composites, Vol. 29, 2007, pp. 193-202.   DOI   ScienceOn
15 Ni, H. G. and Wang, J. Z. "Prediction of Compressive Strength of Concrete by Neural Networks," Cement and Concrete Research, Vol. 30, 2000, pp. 1245-1250.   DOI   ScienceOn
16 Tomosawa, F. and Noguchi, T., "Relationship between Compressive Strength and Modulus of Elasticity of High- Strength Concrete," In: Proceedings of the Third International Symposium on Utilization of High Strength Concrete, Lillehammer, Norway, 1993, pp. 1247-1254.
17 Dias, W. P. S. and Pooliyadda, S. P., "Neural Networks for Predicting Properties of Concretes with Admixtures," Construction and Building Materials, Vol. 15, 2001, pp. 371-379.   DOI   ScienceOn
18 Yeh, I. -C., "Design of High-Performance Concrete Mixture Using Neural Networks and Nonlinear Programming," Journal of Computing in Civil Engineering, Vol. 13, No. 1, 1999, pp. 36-42.   DOI   ScienceOn
19 Kim, J. I., Kim, D. K., Feng, M. Q., and Yazdani, F. Y., "Application of Neural Networks for Estimation of Concrete Strength," Journal of Materials in Civil Engineering, Vol. 16, No. 3, 2004, pp. 257-264.   DOI   ScienceOn
20 Lee, S. C., "Prediction of Concrete Strength Using Artificial Neural Networks," Engineering Structures, Vol. 25, 2003, pp. 849-857.   DOI   ScienceOn
21 Oztas, A., Pala, M., Özbay, E., Kanca, E., Çaglar, N., and Bhatti, M. A., "Predicting the Compressive Strength and Slump of High Strength Concrete Using Neural Network," Construction and Building Materials, Vol. 20, No. 9, 2005, pp. 769-775.   DOI   ScienceOn
22 Yeh, I. -C., "Design of High-Performance Concrete Mixture Using Neural Networks and Nonlinear Programming," Journal of Computing in Civil Engineering, Vol. 13, No. 1, 1999, pp. 36-42.   DOI   ScienceOn
23 Krogh, A. and Hertz, J. A., "A Simple Weight Decay Can Improve Generalization," In: Moody, J. E., Hanson, S. J. and Lippmann, R. P., Editors. Advances in Neural Information Processing Systems 4, Morgan Kaufmann Publishers, 1992, pp. 950-957.
24 Dias, W. P. S. and Pooliyadda, S. P., "Neural Networks for Predicting Properties of Concretes with Admixtures," Construction and Building Materials, Vol. 15, 2001, pp. 371-379.   DOI   ScienceOn