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Genetic Programming for Modelling of Geotechnical Engineering Systems

  • Mohamed A. ShahinEmail author

Abstract

Over the last decade or so, artificial intelligence (AI) has proved to provide a high level of competency in solving many geotechnical engineering problems that are beyond the computational capability of classical mathematics and traditional procedures. This chapter presents one of the most interesting AI techniques, i.e. genetic programming (GP), and its applications in geotechnical engineering. In the last few years, GP, which is inspired by natural evolution of the human being, has proved to be successful in modelling several geotechnical engineering problems and has demonstrated superior predictive ability compared to traditional methods. In this chapter, the modelling aspects and formulation of GP are described and explained in some detail and an overview of most successful GP applications in geotechnical engineering are presented and discussed.

Keywords

Genetic Programming Artificial Neural Network Model Gene Expression Programming Geotechnical Engineering Standard Penetration Test 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Supplementary material

327421_1_En_2_MOESM1_ESM.xls (38 kb)
Data_Bearing Capacity of Drilled Shahfts (xls 37.5 kb)
327421_1_En_2_MOESM2_ESM.xls (32 kb)
Data_Bearing Capacity of Driven Piles (xls 31.5 kb)
327421_1_En_2_MOESM3_ESM.xls (38 kb)
Data_Settlement of Shallow Foundations (xls 38.0 kb)

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Civil EngineeringCurtin UniversityPerthAustralia

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