Soft Computing Based Approaches for High Performance Concrete

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@InProceedings{Alavi:2008:ICECT,
  author =       "A. H. Alavi and A. A. Heshmati and H. Salehzadeh and 
                 A. H. Gandomi and A. Askarinejad",
  title =        "Soft Computing Based Approaches for High Performance
                 Concrete",
  booktitle =    "Proceedings of the Sixth International Conference on
                 Engineering Computational Technology",
  year =         "2008",
  editor =       "M. Papadrakakis and B. H. V. Topping",
  volume =       "89",
  series =       "Civil-Comp Proceedings",
  pages =        "Paper 86",
  address =      "Athens",
  publisher_address = "Stirlingshire, UK",
  month =        "2-5 " # sep,
  publisher =    "Civil-Comp Press",
  keywords =     "genetic algorithms, genetic programming, linear
                 genetic programming, high performance concrete,
                 multilayer perceptron, compressive strength,
                 workability, mix design",
  isbn13 =       "978-1905088263",
  ISSN =         "1759-3433",
  URL =          "http://www.civil-comp.com/pubs/catalog.htm?t=contents&f=26_3",
  URL =          "http://www.amazon.co.uk/Proceedings-International-Conference-Engineering-Computational/dp/1905088264",
  DOI =          "doi:10.4203/ccp.89.86",
  abstract =     "High performance concrete (HPC) is a class of concrete
                 that provides superior performance than those of
                 conventional types. The enhanced performance
                 characteristics of HPC are generally achieved by the
                 addition of various cementitious materials and chemical
                 and mineral admixtures to conventional concrete mix
                 designs. These parameters considerably influence the
                 compressive strength and workability properties of HPC
                 mixes. An extensive understanding of the relation
                 between these parameters and properties of the
                 resulting matrix is required for developing a standard
                 mix design procedure for HPC mix.

                 To avoid testing several mix proportions to generate a
                 successful mix and also simulating the behaviour of
                 strength and workability improvement to an arbitrary
                 degree of accuracy that often lead to savings in cost
                 and time, it is idealistic to develop prediction models
                 so that the performance characteristics of HPC mixes
                 can be evaluated from the influencing parameters.
                 Therefore, in this paper, linear genetic programming
                 (LGP) is used for the first time in the literature to
                 develop mathematical models to be able to predict the
                 strength and slump flow of HPC mixes in terms of the
                 variables responsible. Subsequently, the LGP based
                 prediction results are compared with the results of
                 proposed multilayer perceptron (MLP) in terms of
                 prediction performance. Sand-cement ratio, coarse
                 aggregate-cement ratio, water-cement ratio, percentage
                 of silica fume and percentage of superplasticiser are
                 used as the input variables to the models to predict
                 the strength and slump flow of HPC mixes. A reliable
                 database was obtained from the previously published
                 literature in order to develop the models.

                 The results of the present study, based on the values
                 of performance measures for the models, demonstrated
                 that for the prediction of compressive strength the
                 optimum MLP model outperforms both the best team and
                 the best single solution that have been created by LGP.
                 It can be seen that for the slump flow the best LGP
                 team solution has produced better results followed by
                 the LGP best single solution and the MLP model. It can
                 be concluded that LGPs are able to reach a prediction
                 performance very close to or even better than the MLP
                 model and as promising candidates can be used for
                 solving such complex prediction problems.",
  notes =        "A.H. Alavi1, A.A. Heshmati1, H. Salehzadeh1, A.H.
                 Gandomi2 and A. Askarinejad3

                 1College of Civil Engineering, Iran University of
                 Science & Technology (IUST), Tehran, Iran 2College of
                 Civil Engineering, Tafresh University, Iran 3Department
                 of Civil, Environmental and Geomatic Engineering, Swiss
                 Federal Institute of Technology, Zurich, Switzerland",
}

Genetic Programming entries for A H Alavi Ali Akbar Heshmati Hossein Salehzadeh A H Gandomi Amin Askarinejad

Citations