Optimization techniques for identifying soil parameters in geotechnical engineering: Comparative study and enhancement

Summary A comparative study of optimization techniques for identifying soil parameters in geotechnical engineering was first presented. The identification methodology with its 3 main parts, error function, search strategy, and identification procedure, was introduced and summarized. Then, current op...

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Bibliographic Details
Published in:International journal for numerical and analytical methods in geomechanics Vol. 42; no. 1; pp. 70 - 94
Main Authors: Yin, Zhen‐Yu, Jin, Yin‐Fu, Shen, Jack Shuilong, Hicher, Pierre‐Yves
Format: Journal Article
Language:English
Published: Bognor Regis Wiley Subscription Services, Inc 01.01.2018
Subjects:
Algorithms
Approximation
Clay
Comparative analysis
Comparative studies
Computer simulation
Convergence
Error functions
Evolutionary algorithms
Excavation
finite element method
Genetic algorithms
Geotechnical engineering
hybrid algorithm
Identification
Identification methods
Mathematical models
optimization
Parameter identification
Parameters
parameters identification
Particle swarm optimization
pressuremeter
Search algorithms
Simplex method
Simulated annealing
Soft clay
Soil
Soils
Solutions
Tests
ISSN:0363-9061, 1096-9853
Online Access:Get full text
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Summary:Summary A comparative study of optimization techniques for identifying soil parameters in geotechnical engineering was first presented. The identification methodology with its 3 main parts, error function, search strategy, and identification procedure, was introduced and summarized. Then, current optimization methods were reviewed and classified into 3 categories with an introduction to their basic principles and applications in geotechnical engineering. A comparative study on the identification of model parameters from a synthetic pressuremeter and an excavation tests was then performed by using 5 among the mostly common optimization methods, including genetic algorithms, particle swarm optimization, simulated annealing, the differential evolution algorithm and the artificial bee colony algorithm. The results demonstrated that the differential evolution had the strongest search ability but the slowest convergence speed. All the selected methods could reach approximate solutions with very small objective errors, but these solutions were different from the preset parameters. To improve the identification performance, an enhanced algorithm was developed by implementing the Nelder‐Mead simplex method in a differential algorithm to accelerate the convergence speed with strong reliable search ability. The performance of the enhanced optimization algorithm was finally highlighted by identifying the Mohr‐Coulomb parameters from the 2 same synthetic cases and from 2 real pressuremeter tests in sand, and ANICREEP parameters from 2 real pressuremeter tests in soft clay.
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ISSN:0363-9061
1096-9853
DOI:10.1002/nag.2714