- author = "Amir Hossein Alavi and Amir Hossein Gandomi",
- title = "A Robust Data Mining Approach for Formulation of Geotechnical Engineering Systems",
- journal = "International Journal of Computer Aided Methods in Engineering-Engineering Computations",
- year = "2011",
- volume = "28",
- number = "3",
- pages = "242--274",
- email = "ah_alavi@hotmail.com, a.h.gandomi@gmail.com",
- keywords = "genetic algorithms, genetic programming, gene expression programming, multi expression programming, Linear-based genetic programming, Data mining, Data collection, Geotechnical engineering, Programming and algorithm theory, Systems analysis, Formulation",
- ISSN = "0264-4401",
-
URL = "
http://www.emeraldinsight.com/journals.htm?articleid=1912293",
-
DOI = "
doi:10.1108/02644401111118132",
- size = "33 pages",
-
abstract = "Purpose- The complexity of analysis of geotechnical
behaviour is due to multivariable dependencies of soil
and rock responses. In order to cope with this complex
behavior, traditional forms of engineering design
solutions are reasonably simplified. Incorporating
simplifying assumptions into the development of the
traditional models may lead to very large errors. In
the present study, capabilities of promising variants
of genetic programming (GP), namely linear genetic
programming (LGP), gene expression programming (GEP)
and multi expression programming (MEP) are illustrated
by applying them to the formulation of several complex
geotechnical engineering
problems.
Design/methodology/approach- LGP, GEP and MEP are new variants of GP that make a clear distinction between the genotype and the phenotype of an individual. Compared with the traditional GP, the LGP, GEP and MEP techniques are more compatible with computer architectures. This results in a significant speedup in their execution. These methods have a great ability to directly capture the knowledge contained in the experimental data without making assumptions about the underlying rules governing the system. This is one their major advantages over most of the traditional constitutive modeling methods.
Findings- In order to demonstrate the simulation capabilities of LGP, GEP and MEP, they were applied to the prediction of (i) relative crest settlement of concrete-faced rockfill dams, (ii) slope stability, (iii) settlement around tunnels, and (iv) soil liquefaction. The results are compared with those obtained by other models presented in the literature and found to be more accurate. LGP has the best overall behaviour for the analysis of the considered problems in comparison with GEP and MEP. The simple and straightforward constitutive models developed using LGP, GEP and MEP provide valuable analysis tools accessible to practising engineers.
Originality/value- The LGP, GEP and MEP approaches overcome the shortcomings of different methods previously presented in the literature for the analysis of geotechnical engineering systems. Contrary to artificial neural networks and many other soft computing tools, LGP, GEP and MEP provide prediction equations that can readily be used for routine design practice. The constitutive models derived using these methods can efficiently be incorporated into the finite element or finite difference analyses as material models. They may also be used as a quick check on solutions developed by more time consuming and in-depth deterministic analyses.",
