- 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, Japan",
