Constructing Surrogate Model for Optimum Concrete Mixtures Using Neural Network

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7952)


The determination of concrete mix ratio is known as the concrete mix design which involves many theories and practice knowledge and must satisfy some requirements. In order to get high performance concrete, the mix design should be tuned using optimization. However, a lot of concrete experiments are needed to correct models which are very time-consuming and expensive. In this paper, a neural network surrogate model based method is proposed to optimize concrete mix design. This approach focuses on the optimization of compressive strength. Experimental results manifest that the optimum design which achieves high compressive strength can be found by employing the novel approach.


Neural Network Concrete Surrogate Model Genetic Algorithm 


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  1. 1.
    Cannon, J.P., Krishna Murti, G.R.: Concrete optimized mix proportioning (COMPO). Cement and Concrete Research 1, 353–366 (1971)CrossRefGoogle Scholar
  2. 2.
    Yeh, I.-C.: Computer-aided design for optimum concrete mixtures. Cement & Concrete Composites 29, 193–202 (2007)CrossRefGoogle Scholar
  3. 3.
    Starbird, S.A.: A Metamodel Specification for a Tomato Processing Plan. European Journal of Operational Research 41, 229–240 (1990)Google Scholar
  4. 4.
    Durieux, S., Pierreval, H.: Regression Metamodeling for the Design of auto mated Manufacturing System Composed of Parallel Machines sharing a Mate rial handling Resource. Int. J. Production Economics 89, 1–30 (2004)CrossRefGoogle Scholar
  5. 5.
    Jimmy, M., Tai, C., Mavris, D.N., Schrage, D.P.: Applicati on of a Response Surface Method to the Design of Tipjet Driven Stopped Rotor/Wing Concepts. In: 1st AIAA Aircraft Engineering, Technology and Operations Congress, Los Angeles. CA, pp. 19–21 (1995)Google Scholar
  6. 6.
    Kilmer, R.A., Smith, A.E., Shuman, L.J.: An Emergency Department Simulation and a Neural Network Metamodel. Journal of the Society for Health Systems 5, 63–79 (1997)Google Scholar
  7. 7.
    Hosder, S., Watson, L.T., Grossman, B.: Polynomial Response Surface Approximations for the Multidisciplinary Design Optimization of a High Speed Civil Transport. Optimization and Engineering 2, 431–452 (2001)zbMATHCrossRefGoogle Scholar
  8. 8.
    Dubourg, V., Sudret, B., Bourinet, J.-M.: Reliability-based design optimization using kriging surrogates and subset simulation. Structural and Multidisciplinary Optimization 44, 673–690 (2011)CrossRefGoogle Scholar
  9. 9.
    Sreekanth, J., Datta, B.: Multi-objective management of saltwater intrusion in coastal aquifers using genetic programming and modular neural network based surrogate models 393, 245–256 (2010)Google Scholar
  10. 10.
    Dun-wei, G., Jie, R., Xiao-Yan, S.: Neural network surrogate models of interactive genetic algorithms with individual’ sinterval fitness. Control and Decision 24, 1522–1530 (2009)Google Scholar
  11. 11.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Shandong Provincial Key Laboratory of Network Based Intelligent ComputingUniversity of JinanJinanChina
  2. 2.School of InformaticsLinyi UniversityLinyiChina
  3. 3.Information DepartmentChina United Network Communications Co. Ltd. Shandong branchJinanChina

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