This paper investigates and presents the numerical modeling and validation of the response
of a uniform clean sand using monotonic and cyclic laboratory tests as well as a centrifuge model test comprised of a submerged slope. The dynamic response of the sand is modeled using a critical state compatible, stress ratio-based, bounding surface plasticity constitutive model (PM4Sand), implemented in the commercial finite-difference platform FLAC, and PM4Sand’s performance is evaluated against a comprehensive testing program comprised of laboratory data and a well-instrumented centrifuge model test. Three different calibrations informed by the lab and centrifuge data are performed and the goodness of the predictions is discussed. Conclusions are drawn with regards to the performance of the simulations against the laboratory and centrifuge data, and recommendations about the calibration of the model are provided.
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Cone Penetration Testing to Constrain the Calibration Process of a Sand Plasticity Model for Nonlinear Deformation Analysis
A reliable prediction of liquefaction-induced damage typically requires nonlinear deformation analyses
with an advanced constitutive soil model calibrated to the site conditions. The calibration of constitutive
models can be performed by relying primarily on a combination of commonly available properties and empirical
or semi-empirical relationships, on laboratory tests on site-specific soils, on in-situ penetration tests, or
a combination thereof. Chiaradonna et al. (2022) described a laboratory-based calibration approach of the
PM4Sand constitutive model and evaluated the prediction accuracy against the response of a centrifuge experiment
of a submerged slope. This paper addresses an alternate calibration approach in which the PM4Sand model is calibrated
using centrifuge in-situ CPT data. The model performance for the resulting calibration is evaluated against
the centrifuge experimental data and prior simulations from Chiaradonna et al. (2022). In this case, the CPT-based
calibration resulted in more accurate estimations of the dynamic response and permanent displacements.
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- NSF-PAR ID:
- 10335420
- Date Published:
- Journal Name:
- Cone Penetration Testing 2022
- Page Range / eLocation ID:
- 325-331
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Liquefaction under cyclic loads can be predicted through advanced (liquefaction-capable) material constitutive models. However, such constitutive models have several input parameters whose values are often unknown or imprecisely known, requiring calibration via lab/in-situ test data. This study proposes a Bayesian updating framework that integrates probabilistic calibration of the soil model and probabilistic prediction of lateral spreading due to seismic liquefaction. In particular, the framework consists of three main parts: (1) Parametric study based on global sensitivity analysis, (2) Bayesian calibration of the primary input parameters of the constitutive model, and (3) Forward uncertainty propagation through a computational model simulating the response of a soil column under earthquake loading. For demonstration, the PM4Sand model is adopted, and cyclic strength data of Ottawa F-65 sand from cyclic direct simple shear tests are utilized to calibrate the model. The three main uncertainty analyses are performed using quoFEM, a SimCenter open-source software application for uncertainty quantification and optimization in the field of natural hazard engineering. The results demonstrate the potential of the framework linked with quoFEM to perform calibration and uncertainty propagation using sophisticated simulation models that can be part of a performance-based design workflow.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1201/9781003308829-43
