Soil parameter identification using a genetic algorithm

• Published in: International journal for numerical and analytical methods in geomechanics Vol. 32; no. 2; pp. 189 - 213
• Main Authors: Levasseur, S., Malécot, Y., Boulon, M., Flavigny, E.
• Format: Journal Article Web Resource
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 10.02.2008; Wiley; John Wiley & Sons
• Subjects: Applied sciences; Buildings. Public works; Civil engineering; Computation methods. Tables. Charts; Engineering Sciences; Engineering, computing & technology; Exact sciences and technology; excavation; finite element method; genetic algorithms; Geotechnics; Ingénierie civile; Ingénierie, informatique & technologie; inverse analysis; Mechanics; objective function; parameter identification; parameter identification; pressuremeter test; Soil investigations. Testing; Structural analysis. Stresses; finite element method; Constitutive equation; Property of soil; Identification; genetic algorithms; Optimization; Inverse problem; Excavations; Finite element method; Genetic algorithm; excavation; Pressuremeter; Objective function; inverse analysis; Gradient method; pressuremeter test; Geotechnics; parameter identification; constitutive equation; sheet piled wall; Genetic algorithms; genetic algorithm; soil property; finite element method; Soil parameter; in situ measurement; displacement; Coulomb criterion; retaining wall; geotechnical property; topology; Error function; Excavation; Mohr theory; Soils; Piles; pressuremeter; Constitutive models; Pressure measurement; Retaining walls
• ISSN: 0363-9061; 1096-9853; 1096-9853
• DOI: 10.1002/nag.614

This paper is dedicated to the identification of constitutive parameters of the Mohr–Coulomb constitutive model from in situ geotechnical measurements. A pressuremeter curve and the horizontal displacements of a sheet pile wall retaining an excavation are successively used as measurements. Two kinds of optimization algorithms are used to minimize the error function, the first one based on a gradient method and the second one based on a genetic algorithm. The efficiency of each algorithm related to the error function topology is discussed. Finally, it is shown that the use of a genetic algorithm to identify the soil parameters seems particularly suitable when the topology of the error function is complex. Copyright © 2007 John Wiley & Sons, Ltd.