Single‐and multi‐objective genetic algorithm optimization for identifying soil parameters

Abstract

SUMMARYThis paper discusses the quality of the procedure employed in identifying soil parameters by inverse analysis. This procedure includes a FEM-simulation for which two constitutive models-a linear elastic perfectly plastic Mohr-Coulomb model and a strain-hardening elasto-plastic model-are successively considered. Two kinds of optimization algorithms have been used: a deterministic simplex method and a stochastic genetic method. The soil data come from the results of two pressuremeter tests, complemented by triaxial and resonant column testing. First, the inverse analysis has been performed separately on each pressuremeter test. The genetic method presents the advantage of providing a collection of satisfactory solutions, among which a geotechnical engineer has to choose the optimal one based on his scientific background and/or additional analyses based on further experimental test results. This advantage is enhanced when all the constitutive parameters sensitive to the considered problem have to be identified without restrictions in the search space. Second, the experimental values of the two pressuremeter tests have been processed simultaneously, so that the inverse analysis becomes a multi-objective optimization problem. The genetic method allows the user to choose the most suitable parameter set according to the Pareto frontier and to guarantee the coherence between the tests. The sets of optimized parameters obtained from inverse analyses are then used to calculate the response of a spread footing, which is part of a predictive benchmark. The numerical results with respect to both the constitutive models and the inverse analysis procedure are discussed.

An efficient parameter identification procedure for soft sensitive clays

“…Since SA depends strongly on the initial sets of parameters (individuals), the way of overcoming this drawback is to carry out multiple optimizations starting from different initial sets of parameters. As suggested by Papon et al (2012), 10 optimizations with different initial sets of parameters generated by ULH were carried out. All individuals whose errors are lower than a reference value, are called "satisfactory".…”

“…These applications involved different kinds of tests, such as laboratory tests (Calvello and Finno, 2004) and field tests (Lecampion et al, 2002;Yin and Hicher, 2008;Papon et al, 2012), which were performed on different soils. Different constitutive models, such as the Mohr-Coulomb model (Papon et al, 2012), hardening soil model (Calvello and Finno, 2004;Papon et al, 2012), and modified Cam clay model (Dano et al, 2007), have been adopted and combined with these tests in the optimization process to identify their parameters. However, no studies about the identification of parameters relating to creep combined with destructuration of structured clays using optimization techniques has been reported.…”

Análise inversa da curva do ensaio pressiométrico utilizando o modelo hiperbólico

“…Estas dificuldades levaram a experiências bem sucedidas, onde se procurou identificar os parâmetros que definem o comportamento do solo por meio de análises inversas de ensaios de campo (CALVELLO; FINNO, 2004;LEVASSEUR et al, 2008;HICHER, 2008;ARDALAN;ESLAMI;NARIMAN-ZADEH, 2009;PAPON, 2012;ZHANG, GALLIPOLI;AUGARDE, 2013) A análise inversa (ou retroanálise) permite a identificação de parâmetros por meio da formulação de um problema matemático, geralmente de minimização de uma determinada função (função objetivo), cuja solução fornece o conjunto de parâmetros do modelo que representam a menor diferença entre os valores medidos no campo e os calculados numericamente.…”

“…Dentre as principais técnicas aplicadas em geotecnia destaca-se o algoritmo genético (AG) proposto por Holland (1992), um mecanismo de busca adaptativa que se baseia no princípio Darwiniano de seleção natural e reprodução genética. Jin et al (2016) destacam que devido a sua robustez, eficiência e capacidade de fornecer um conjunto de soluções próximas da solução ideal ao invés de uma única resposta, o que o torna mais prático do ponto de vista geotécnico, faz do AG uma das técnicas mais aplicadas para solução dos problemas em geotecnia (SAMARAJIVA; MACARI; WATHUGALA, 2005;LEVASSEUR et al, 2008;BAROTH;MALECOT, 2010;ROKONUZZAMAN;SAKAI, 2010;HELENO, 2011;PAPON, 2012;JIN et al, 2016;YIN et al, 2017) Dentro desse contexto, utilizar os resultados de medições diretas possíveis de serem feitas no campo para, por meio de análises inversas, estimar parâmetros de modelos constitutivos de comportamento do solo é um caminho que tem se mostrado promissor (LEDESMA; GENS; ALONSO, 1996;ZENTAR;MOULIN, 2001;FINNO, 2004;CALVELLO, 2005;LEVASSEUR et al, 2008;HICHER, 2008;BAROTH;MALECOT, 2010;HELENO, 2011;PAPON, 2012;ZHAO et al, 2015;YIN et al, 2017).…”

Permissible range of model parameters for natural fine-grained materials

“…Nevertheless, some are not directly measurable, such as some parameters used to describe the evolution of anisotropy and structure (material degradation). These parameters are estimated through indirect methods, such as calibration of the model response against the soil response measured in non-standard laboratory tests or optimisation methods [3,12,15,16,20,25,27]. For optimisation methods, however, it is of paramount importance to know the bounds for the values of these parameters prior to calibration.…”