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Record W6991019988

Evaluation of the PM4Sand Constitutive Model for the Prediction of Earthquake-Induced & Static Liquefaction in Hydraulic Fills

2019· dissertation· en· W6991019988 on OpenAlexaboutno aff

Bibliographic record

VenueResearch Repository (Delft University of Technology) · 2019
Typedissertation
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
FundersTechnische Universiteit Delft
KeywordsParametric statisticsShakedownLiquefactionHazardPiezometer
DOInot available

Abstract

fetched live from OpenAlex

The earthquake-induced liquefaction is a high-risk phenomenon for dredging industries, which need to set strict requirements in order to avoid potential disastrous effects for the project. Different types of liquefaction exist which can be triggered over a wide range of soil types and for different loading conditions. The liquefaction triggering due to an earthquake event is dependent on the soil behaviour under undrained cyclic loading. The assessment of the liquefaction hazard during an earthquake is mainly based so far on empirical procedures. The most common used in practise is the NCEER method (Youd & Idriss, 2001) which is established according to empirical evaluation of field observations and in-situ testing. However, the NCEER method can be inaccurate for the design primarily due to its empirical nature as it is capturing different soil types and loading conditions. For that purpose, advanced constitutive models can provide more precise assessments as they can be calibrated for specific site conditions. Such a model is the PM4Sand, which is very attractive for practical applications because there are only a few model parameters to be determined in the calibration process.The first part of the current thesis project includes the validation of the PM4Sand model for both earthquake-induced and static liquefaction according to undrained Cyclic Direct Simple Shear (CDSS) tests and undrained Direct Simple Shear (DSS) respectively, performed on Ottawa F-65 Sand. The influence of the model parameters is examined throughout a parametric assessment analysis. It was observed, that the model approximates well the general features of both cyclic and static loading. Regarding cyclic loading, it produced similar responses in terms of excess pore pressures generation and stress paths even though it slightly overpredicts the cyclic resistance for small number of loading cycles and underpredicts the cyclic resistance for large number of loading cycles. Regarding static liquefaction, even if the model had initially overestimated the response, it was able to simulate successfully the static liquefaction behaviour after a recalibration process was established.The next part of the project includes the performance of the PM4Sand model for the prediction of earthquake-induced liquefaction in hydraulic fills, which are analysed for several different seismic motions. The fill is placed over a different range of relative densities and it is modelled in Plaxis software as a 1-D soil column. The fill layers that are prone to liquefy, are modelled with the PM4Sand model while the layers that are not susceptible to liquefaction are modelled with Hardening Soil Small (HSS) model. The PM4Sand layer is calibrated according to factors that are accounting for the in-situ state of the fill and the magnitude of the earthquake motions. The dynamic analyses are performed with and without consolidation and the lateral boundaries used are tied degrees of freedom. The results in terms of excess pore pressures generation are examined throughout the whole earthquake motion. Moreover, the onset of liquefaction in the hydraulic fill is captured when the excess pore pressure ratio has reached a value of around 1.0 (ru≈1). It is shown, that the PM4Sand model is indeed applicable for the prediction of earthquake-induced and static liquefaction in hydraulic fills. The effect of the in-situ state of the fill, in particular the relative density, has a critical role on the liquefaction susceptibility, which is a lot representative to what has been observed in reality. According to PM4Sand model, the loosely-packed fills DR=30% and DR=40%) are indeed more susceptible to liquefaction compared to the densely-packed fills (DR=50% and DR=60%) which showed less or even no liquefaction potential due to the earthquake events. On the other hand, the largest drawback of the NCEER method it its empirical nature, as for the current project it is proved to be conservative for the design. More specifically, it predicted liquefaction for almost all the hydraulic fills (DR=30% to DR=60%) analyzed for all different earthquake motions. Regarding the dynamic analyses with consolidation, the results related to the earthquake-induced liquefaction of the fills are more representative to realistic conditions as there is a better distribution of excess pore pressures along the soil column with respect to the dynamic analyses without consolidation. For the latter type of analysis, in the loosely-packed fills (DR=30% and DR=40%) there is a better diffusion of excess pore pressures more for the signals of low dominant frequencies regardless the peak ground acceleration values of the input signal. In the densely-packed fills (DR=50% and DR=60%) the same phenomenon takes pace more for the signals of high dominant frequencies. However, a localization of liquefied zones is observed in distinct parts along the fill layer for the rest of the signals.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.515

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.059
GPT teacher head0.292
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2019
Admission routes1
Has abstractyes

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