Plasticity modeling of liquefaction effects under sloping ground and irregular cyclic loading conditions

Ziotopoulou, Katerina; Boulanger, Ross W.

doi:10.1016/j.soildyn.2016.02.013

Cited by 62 publications

(18 citation statements)

References 9 publications

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“…Boulanger and Idriss, 2014; Kayen et al, 2013); and (T3) wholly mechanistic constitutive models, which typically require many soil and model parameters, and which are generally limited to use by geoengineers trained in computational mechanics (e.g. Byrne et al, 2004; Cubrinovski and Ishihara, 1998; Ziotopoulou and Boulanger, 2016). While advances to “T3” models and the enabling technologies have grown their use, their application to predicting liquefaction triggering remains relatively rare.…”

Section: Geotechnical and Geospatial Liquefaction Modelsmentioning

confidence: 99%

Field assessment of liquefaction prediction models based on geotechnical versus geospatial data, with lessons for each

2020

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Semi-empirical models based on in situ geotechnical tests have been the standard-of-practice for predicting soil liquefaction since 1971. More recently, prediction models based on free, readily available data were proposed. These “geospatial” models rely on satellite remote-sensing to infer subsurface traits without in situ tests. Using 15,223 liquefaction case-histories from 24 earthquakes, this study assesses the performance of 23 models based on geotechnical or geospatial data using standardized metrics. Uncertainty due to finite sampling of case-histories is accounted for and used to establish statistical significance. Geotechnical predictions are significantly more efficient on a global scale, yet successive models proposed over the last 20 years show little or no demonstrable improvement. In addition, geospatial models perform equally well for large subsets of the data—a provocative finding given the relative time- and cost-requirements underlying these predictions. Through this performance comparison, lessons for improving each class of model are elucidated in detail.

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Section: Geotechnical and Geospatial Liquefaction Modelsmentioning

confidence: 99%

Field assessment of liquefaction prediction models based on geotechnical versus geospatial data, with lessons for each

2020

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“…is the plastic multiplier (Appendix A), while ℎ is the counterpart of the hardening coefficient defined with respect to the memory surface -its expression is specified later on. Yimsiri and Soga (2010) and Ziotopoulou and Boulanger (2016), who noted that sand behaviour at large strain levels is mainly governed by the current relative density:…”

Section: Memory Surface and Its Evolutionmentioning

confidence: 99%

“…Special mention in this context goes to the SANISAND04 model proposed by Dafalias and Manzari (2004), built on Manzari and Dafalias (1997) and forefather of several later formulations (Zhang and Wang 2012;Boulanger and Ziotopoulou 2013;Dafalias and Taiebat 2016;Petalas et al 2019). Among these, the PM4Sand model (Boulanger and Ziotopoulou 2013;Ziotopoulou and Boulanger 2016) possesses remarkable capabilities to reproduce undrained cyclic behaviour, including the simulation of pore pressure build-up, liquefaction triggering and, in medium-dense/dense sands, 'cyclic mobility' (Elgamal 2 Liu et al, July 14, et al 2003) -in turn associated with transient regains in shear resistance, and gradual shear strain accumulation at vanishing confinement. Cyclic mobility is relevant to the serviceability of earth structures and foundations under prolonged cyclic loading (Ziotopoulou and Boulanger 2016;Kementzetzidis et al 2019), as well as to seismic site response (Roten et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Memory-Enhanced Plasticity Modeling of Sand Behavior under Undrained Cyclic Loading

Liu

Diambra

Abell

et al. 2020

J. Geotech. Geoenviron. Eng.

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This work presents a critical state plasticity model for predicting the response of sands to cyclic loading. The well-known bounding surface SANISAND framework by Dafalias and Manzari (2004) is enhanced with a 'memory surface' to capture micro-mechanical, fabric-related processes directly effecting cyclic sand behaviour. The resulting model, SANISAND-MS, was recently proposed by Liu et al. (2019), and successfully applied to the simulation of drained sand ratcheting under thousands of loading cycles. Herein, novel ingredients are embedded into Liu et al. (2019)'s formulation to better capture the effects of fabric evolution history on sand stiffness and dilatancy. The new features enable remarkable accuracy in simulating undrained pore pressure build-up and cyclic mobility behaviour in medium-dense/dense sand. The performance of the upgraded SANISAND-MS is validated against experimental test results from the literature-including undrained cyclic triaxial tests at varying cyclic loading conditions and pre-cyclic consolidation histories. The proposed modelling platform will positively impact the study of relevant cyclic/dynamic problems, for instance, in the fields of earthquake and offshore geotechnics.

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“…The other 19 (2 flags and 17 secondary parameters) can be left with their preset default values if no other information is available or calibrated to the desired response based on the available lab data. Pertinent information on the parameters of PM4Sand and their calibrated values are given in the following section, while extensive information on the model is provided by Boulanger and Ziotopoulou (2015) and Ziotopoulou and Boulanger (2016).…”

Section: Modeling Approachmentioning

confidence: 99%

Numerical Simulations of Selected LEAP Centrifuge Experiments with PM4Sand in FLAC

Montgomery

Ziotopoulou

2019

Model Tests and Numerical Simulations of Liquefaction and Lateral Spreading

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Increasing the confidence in estimates of liquefaction-induced deformations from numerical models requires careful validation of both constitutive models and numerical simulation approaches. This validation should be performed with proper consideration of the intended use of the simulation results and the uncertainty inherent in comparing numerical simulations to laboratory or physical models. This paper describes the comparison of a set of calibrated numerical simulations to the results of centrifuge experiments with a submerged, liquefiable slope. These simulations were performed as part of the 2017 LEAP simulation exercise using the constitutive model PM4Sand and the numerical platform FLAC. The simulation results were compared to those from the centrifuge to identify which aspects of the experimental response the simulations were adequately able to capture. This paper provides a description of the simulation approach including the constitutive model and numerical platform. The process of calibrating PM4Sand to the results of laboratory experiments is described. This is followed by a description of the system-level simulations and a comparison of the simulation results with the experimental results. A sensitivity study was performed to examine trends in the simulation response and a discussion of the findings from this study is presented.

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Plasticity modeling of liquefaction effects under sloping ground and irregular cyclic loading conditions

Cited by 62 publications

References 9 publications

Field assessment of liquefaction prediction models based on geotechnical versus geospatial data, with lessons for each

Field assessment of liquefaction prediction models based on geotechnical versus geospatial data, with lessons for each

Memory-Enhanced Plasticity Modeling of Sand Behavior under Undrained Cyclic Loading

Numerical Simulations of Selected LEAP Centrifuge Experiments with PM4Sand in FLAC

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