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A new displacement back analysis to identify mechanical geo‐material parameters based on hybrid intelligent methodology
Author(s) -
Feng XiaTing,
Zhao Hongbo,
Li Shaojun
Publication year - 2004
Publication title -
international journal for numerical and analytical methods in geomechanics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.419
H-Index - 91
eISSN - 1096-9853
pISSN - 0363-9061
DOI - 10.1002/nag.381
Subject(s) - support vector machine , displacement (psychology) , genetic algorithm , point (geometry) , computer science , artificial intelligence , algorithm , engineering , machine learning , mathematics , geometry , psychology , psychotherapist
Displacement back analysis is a common method to identify mechanical geo‐material parameters using the monitored displacement. How to obtain a global optimum solution in large space search of highly non‐linear multimodal is a key point of optimum back analysis. The paper presents a new back analysis that is an integration of evolutionary support vector machines (SVMs), numerical analysis and genetic algorithm. The non‐linear relationship between the mechanical geo‐material parameters to be identified and the corresponding displacement values of key points is learned and represented by evolutionary SVMs in global optimum. Numerical analysis is used to create training and testing samples for recognition of SVMs. Then, performing a global optimum search on the obtained SVMs using genetic algorithm can identify the mechanical geo‐material parameters. The proposed algorithm is tested by back analysis of an elastic plate and an elastic–plastic plate and used to recognize mechanical parameters of subclay, strongly weathered tuff and weakly weathered tuff of Bachimen slope, Funing expressway, Fujian, China. The results indicate that applicability of the proposed algorithm with enough accuracy. Copyright © 2004 John Wiley & Sons, Ltd.