Technical Papers
Feb 28, 2022

Application of an Optimization Algorithm for Calibrating Soil Bounding Surface Plasticity Models for Cyclic Loading

Publication: International Journal of Geomechanics
Volume 22, Issue 5

Abstract

Advanced constitutive models require several input parameters, some of which represent intrinsic soil properties and some mathematical fitting parameters. Calibrating these modeling parameters is often a challenging component of using a constitutive model in numerical analyses, especially when models are to be calibrated against several data sets simultaneously and/or when there is no conventional geotechnical relation for some parameters. The calibration of constitutive models for cyclic loading is an even more challenging task compared with monotonic loading due to the nonlinearity of soil behavior with stress/strain reversals, amplitude, and the number of loading cycles. In this study, the efficiency of the Gauss–Newton trust-region optimization (GNO) algorithm for calibrating constitutive models for cyclic behavior is evaluated by applying it to three recently developed advanced bounding surface plasticity constitutive models for clays and sands and comparing the outcomes with laboratory test results. The GNO algorithm is shown to be an accurate and time-efficient alternative tool for calibrating the cyclic constitutive models studied.

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Conclusions

In this study, the GNO was adjusted to calibrate constitutive models developed for cyclic loading applications. The modifications applied to the GNO may be summarized as follows: (1) application of a rank-one estimation of the Jacobian matrix using the Broyden’s method (Broyden 1965), instead of the full calculation at every iteration, in order to reduce the computation time; (2) employment of the simplified approach of Aster et al. (2012) in order to find the Levenberg–Marquardt parameter, λ, so as to reduce the complications associated with finding this parameter; (3) assignment of weights proportional to the inverse of the variance of the data points to ensure that all data had the same effect on the calibration results and to reduce a bias induced at high strain levels; (4) use of the forward difference method instead of the central difference method in order to reduce the number of calculations by half at each iteration and to double the calibration speed; and (5) introduction of a new approach to calibrating constitutive models developed for cyclic loading applications based on CSR-versus-N curves. When compared with the Liu et al. (2016) algorithm, the runtime was decreased by a factor of about 10, on average.
The use of the GNO algorithm for calibrating soil constitutive models was evaluated for cyclic loading, and cases in which models were to be calibrated against an extensive data set were presented. This algorithm was applied to three advanced constitutive models for cyclic loading applications—the SANICLAY bounding surface model of Seidalinov and Taiebat (2014), the SANICLAY bounding surface with hybrid flow rule model of Shi et al. (2018), and the Dafalias and Manzari (2004) (DM04) model.
The application of the GNO algorithm to the SANICLAY-B model showed that it could retrieve the input parameters that were used to generate the target data. Furthermore, application of the GNO algorithm to the SANICLAY-H model for San Francisco YBM showed that calibrating the model in terms of the CSR-versus-N curve to reach a given shear strain level is an efficient approach compared with calibrating against shear strains, as it results in faster convergence of the calibration process and a good match of the CSR-versus-N curve, although giving a misfit of stress–strain behavior following liquefaction. Selection of the calibration approach depends on the problem at hand and the objectives of the numerical simulations being performed. Finally, the GNO algorithm was efficient in calibrating the DM04 model against laboratory test data provided by the LEAP, and simulations using the model calibrated with the algorithm showed close results to the experimental data.
In all three example applications of the calibration approach, the GNO algorithm proved to be a useful tool for calibrating advanced constitutive models under cyclic loading. Using such algorithms provides an alternative tool to the conventional trial-and-error method of calibrating soil constitutive models and can sometimes improve the speed of calibration when dealing with a large data set. However, the fidelity of a calibration is only as good as the constitutive model being calibrated, and users should be aware of their constitutive model’s limitations. For instance, the SANICLAY-H model used here was not able to capture the rate of pore water pressure generation but performed well when predicting the development of large shear strains upon cyclic loading. Despite these limitations, the algorithm can be useful for calibrating complex constitutive models.
It should be underlined that the GNO algorithm was only used to calibrate input parameters without physical meaning that could not be extracted from laboratory or field test results directly. Therefore, using such algorithms does not violate the physical meaning of an input parameter, when they have one. Users should ensure that their final values are in the range recommended by the model’s developers, and, when available, the model’s input parameters should be based on laboratory or field test data. In all the examples presented herein, convergence was reached before 100 iterations, and it is therefore suggested that no more than 100 iterations be used, except where the algorithm does not converge. Once a model is calibrated, whether using the presented GNO or any other procedure, it should be validated by simulating test conditions not included in a given database in order to validate that the model’s response is realistic.
Last but not least, the GNO algorithm can be used with any constitutive model. In order to do so, users should first implement the models in a single-element fashion and then identify the input parameters to be calibrated for said model. As mentioned, only parameters without a physical meaning should be calibrated, and, therefore, users should compute other parameters from available laboratory test data and/or empirical relationships. Finally the algorithm can be used by feeding it any relevant laboratory test data. Alternatively, the algorithm can also be coupled with existing numerical modeling software to calibrate advanced constitutive models already implemented on such platforms, waiving the need for a “homemade” implementation.

Data Availability Statement

Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request. For instance, the MATLAB implementation of the GNO, as a helpful tool for calibrating plasticity models, is available upon request to scholars and practitioners.

Acknowledgments

Funding from the National Science and Engineering Research Council (NSERC) of Canada (RGPIN-2017-05756) and the Fonds de Recherche Nature et Technologies du Québec (FRQNT) is gratefully acknowledged. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of these funding agencies.

Notation

The following symbols are used in this paper:
A0 and nd
dilatancy parameters;
ad
damage parameter for SANICLAY-B;
C
parameter that governs the rate of rotational hardening;
c, λc, e0, and ξ
critical-state parameters;
cd
damage parameter for SANICLAY-H;
d
dilatancy parameter;
e
void ratio;
eg, Ag, ng
fitting parameters for the maximum shear modulus;
e0
initial void ratio;
G0
elasticity constants;
H
plastic modulus;
hc
parameter that adjusts the magnitude of the plastic modulus under compression in SANICLAY-H;
he
parameter that adjusts the magnitude of the plastic modulus under extension in SANICLAY-H;
h0
parameter that adjusts the magnitude of the plastic modulus in SANICLAY-B;
IW
identity matrix with different dimensions to Iθ;
Iθ
identity matrix;
i
iteration number index;
J
Jacobian matrix;
ki
destructuration parameter;
L
plastic multiplier (loading index);
Mc
critical state ratio under compression;
Md
dilatancy line;
Me
critical state ratio under extension;
m
yield surface parameter;
N
total number of model parameters;
N
variable limiting the rotation of the bounding surface;
p
mean effective stress;
pat
atmospheric pressure;
(p¯,q¯)
image stress;
(pc, qc)
projection center;
p0
initial confining stress;
p0
isotropic hardening variable;
pα
dummy variable to adjust the size of the plastic potential;
q
deviatoric stress;
Rdi
flow direction of the deviatoric strain;
Rvi
flow direction of the volumetric strain;
r(θ)
residual vector;
S
total number of data points;
u
pore water pressure;
W
diagonal weight matrix;
w
vector of the data points’ weight;
x
parameter limiting the upper bound rotation of the bounding surface;
yexp
vector of target data;
y(θ)
vector of simulation results;
z
fabric parameter;
zmax and cz
fabric dilatancy tensor parameters;
α
back-stress ratio;
α
rotational hardening variable;
αb
bounding (limiting) value of α;
γ
shear strain;
γ0.7
parameter of elastic stiffness degradation rate;
δθ
small perturbations in input parameters;
ɛa
axial cyclic strain amplitude;
ɛq
deviatoric strain;
η¯
image stress ratio;
θ
vector of input parameters;
κ
slope of the recompression line in the e − ln(p) space;
λ
slope of the virgin compression line in the e − ln(p) space;
λ
Levenberg–Marquardt damping parameter;
σvc
vertical consolidation stress;
ν
Poisson's ratio;
ψ
state parameter;
ω
hybrid flow rule parameter; and
〈 〉
Macaulay brackets.

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International Journal of Geomechanics
Volume 22Issue 5May 2022

History

Received: Jul 28, 2020
Accepted: Dec 20, 2021
Published online: Feb 28, 2022
Published in print: May 1, 2022
Discussion open until: Jul 28, 2022

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Authors

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Mohammad Zarrabi, Ph.D., Aff.M.ASCE [email protected]
Geotechnical Engineering Specialist, WSP/Golder, Montréal, Canada. Email: [email protected]
Lecturer, Dept. of Civil and Environmental Engineering, Univ. of California Los Angeles, Los Angeles 90095, CA. ORCID: https://orcid.org/0000-0001-9226-3999. Email: [email protected]
Assistant Professor, Dept. of Civil, Geological and Mining Engineering, Polytechnique Montréal, Montréal, Canada (corresponding author). ORCID: https://orcid.org/0000-0001-6776-2227. Email: [email protected]

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