Utilisation of Computational Intelligence Techniques for Stabilised Soil

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "A. H. Alavi and A. A. Heshmati and A. H. Gandomi and 
                 A. Askarinejad and M. Mirjalili",
  title =        "Utilisation of Computational Intelligence Techniques
                 for Stabilised Soil",
  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 175",
  address =      "Athens",
  publisher_address = "Stirlingshire, UK",
  month =        "2-5 " # sep,
  publisher =    "Civil-Comp Press",
  keywords =     "genetic algorithms, genetic programming, linear
                 genetic programming, stabilised soil, multilayer
                 perceptron, textural properties of soil, cement, lime,
                 asphalt, unconfined compressive strength",
  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.89.175",
  abstract =     "In the present study, two branches of computational
                 intelligence techniques namely, the multilayer
                 perceptron (MLP) and linear genetic programming (LGP),
                 are employed to simulate the complex behaviour of the
                 strength improvement in a chemical stabilisation
                 process. Due to a need to avoid extensive and
                 cumbersome experimental stabilisation tests on soils on
                 every new occasion, it was decided to develop
                 mathematical models to be able to estimate the
                 unconfined compressive strength (UCS) as a quality of
                 the stabilised soil after both compaction and curing by
                 using particle size distribution, liquid limit,
                 plasticity index, linear shrinkage as the properties of
                 natural soil before compaction and stabilisation and
                 the quantities and types of stabiliser. A comprehensive
                 and reliable set of data including 219 previously
                 published UCS test results were used to develop the
                 prediction models.

                 Based on the values of performance measures for the
                 models, it was observed that all models are able to
                 predict the UCS value to an acceptable degree of
                 accuracy. The results demonstrated that the optimum MLP
                 model with one hidden layer and thirty six neurons
                 outperforms both the best single and the best team
                 program that have been created by LGP. It can also be
                 concluded that the best team program evolved by LGP has
                 a better performance than the best single evolved
                 program. This investigation revealed that, on average,
                 LGP is able to reach a prediction performance similar
                 to the MLP model. Moreover, LGP as a white-box model
                 provides the programs of an imperative language or
                 machine language that can be inspected and evaluated to
                 provide a better understanding of the underlying
                 relationship between the different interrelated input
                 and output data.",
  notes =        "A.H. Alavi1, A.A. Heshmati1, A.H. Gandomi2, A.
                 Askarinejad3 and M. Mirjalili4

                 1College of Civil Engineering, Iran University of
                 Science and Technology, Tehran, Iran 2College of Civil
                 Engineering, Tafresh University, Iran 3Department of
                 Civil, Environmental and Geomatic Engineering, Swiss
                 Federal Institute of Technology, Zurich, Switzerland
                 4Department of Civil & Earth Resources Engineering,
                 Graduate School of Engineering, Kyoto University,

Genetic Programming entries for A H Alavi Ali Akbar Heshmati A H Gandomi Amin Askarinejad Mojtaba Mirjalili