Selection of sand models and identification of parameters using an enhanced genetic algorithm

Abstract

Summary Numerous constitutive models of granular soils have been developed during the last few decades. As a consequence, how to select an appropriate model with the necessary features based on conventional tests and with an easy way of identifying parameters for geotechnical applications has become a major issue. This paper aims to discuss the selection of sand models and parameters identification by using genetic algorithm. A real‐coded genetic algorithm is enhanced for the optimization with high efficiency. Models with gradually varying features (elastic‐perfectly plastic modelling, nonlinear stress–strain hardening, critical state concept and two‐surface concept) are selected from numerous sand models as examples for optimization. Conventional triaxial tests on Hostun sand are selected as the objectives in the optimization. Four key points are then discussed in turn: (i) which features are necessary to be accounted for in constitutive modelling of sand; (ii) which type of tests (drained and/or undrained) should be selected for an optimal identification of parameters; (iii) what is the minimum number of tests that should be selected for parameter identification; and (iv) what is the suitable and least strain level of objective tests to obtain reliable and reasonable parameters. Finally, a useful guide, based on all comparisons, is provided at the end of the discussion. Copyright © 2015 John Wiley & Sons, Ltd.

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

“…Additionally, the objective error calculated by this function is a dimensionless variable; thus, any difference in error can be avoided for different objectives with different variables. Due to the stability of Equation , many researchers have adopted it as the error function to conduct the optimizations …”

“…The procedures presented above, and others, which are not discussed here, are summarized in Figure . Most identification procedures are based on 2 different codes: an FEM code (eg, PLAXIS, FLAC, and ABAQUS) or a single Gauss point integration of a constitutive model (eg, Jin et al and Ye et al) for the simulation, and the search method code for finding the optimal solution.…”

“…For all these cases, a common requirement is to adequately obtain the soil properties and the design parameters from laboratory or field tests measurements (Yao et al). Thus, the parameter identification methods play an important role in finite element or analytical solution‐based analyses used for design and construction purposes, based on which the selection of constitutive models can further be conducted for obtaining more adequate values of soil parameters when an appropriate model is selected (Jin et al).…”

Investigation of Collapsed Building Incidents on Soft Marine Deposit: Both from Social and Technical Perspectives

“…Therefore, due to the occurrence of creep, the decrease in the pore volume for Wenzhou clay could be more pronounced, thus promoting the development of differential settlement (δ). According to Yin [55,56], the tertiary creep of a soil contributes significantly to the long-term deformation of foundations of the highly sensitive clays [57][58][59]. Thus, this creep behavior becomes one of the significant factors contributing to this incident.…”

Análise inversa da curva do ensaio pressiométrico utilizando o modelo hiperbólico

“…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).…”