Paper Info
Title
A new multi-gene genetic programming approach to non-linear system modeling. Part II: geotechnical and earthquake engineering problems
Abstract
Complexity of analysis of geotechnical behavior 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 methods may lead to very large errors. This paper presents an endeavor to exploit a robust multi-gene genetic programming (MGGP) method for the analysis of geotechnical and earthquake engineering systems. MGGP is a modified genetic programming approach for model structure selection combined with a classical technique for parameter estimation. To justify the abilities of MGGP, it is systematically employed to formulate the complex geotechnical engineering problems. Different classes of the problems analyzed include the assessment of (i) undrained lateral load capacity of piles, (ii) undrained side resistance alpha factor for drilled shafts, (iii) settlement around tunnels, and (iv) soil liquefaction. The validity of the derived models is tested for a part of test results beyond the training data domain. Numerical examples show the superb accuracy, efficiency, and great potential of MGGP. Contrary to artificial neural networks and many other soft computing tools, MGGP provides constitutive prediction equations. The MGG-based solutions are particularly valuable for pre-design practices.
Year
DOI
Venue
2012
10.1007/s00521-011-0735-y
Neural Computing and Applications
Keywords
DocType
Volume
system modeling,traditional method,modified genetic programming approach,soil liquefaction,engineering design solution,new multi-gene genetic programming,complex behavior,earthquake engineering problem,earthquake engineering system,geotechnical behavior,Part II,robust multi-gene genetic programming,traditional form,complex geotechnical engineering problem
Journal
21
Issue
ISSN
Citations 
1
1433-3058
26
PageRank 
References 
Authors
1.82
6
2
Name
Order
Citations
PageRank
Amir Hossein Gandomi11836110.25
Amir Hossein Alavi2101645.59