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Soil-Pile-Quay Wall System with Liquefaction-Induced Lateral Spreading: Experimental Investigation, Numerical Simulation, and Global Sensitivity Analysis

2018· article· en· W2889927321 on OpenAlexaff
Lei Su, Hua‐Ping Wan, Yong Li, Xianzhang Ling

Bibliographic record

VenueJournal of Geotechnical and Geoenvironmental Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEarthquake shaking tableGeotechnical engineeringPileLiquefactionSoil liquefactionSensitivity (control systems)Settlement (finance)Nonlinear systemSubmarine pipelineGeologyStructural engineeringEngineeringComputer science

Abstract

fetched live from OpenAlex

Extensive damage to offshore and port structures supported on piles behind a quay wall has been frequently reported as a result of soil liquefaction and lateral spreading in earthquakes. This study aims to explore the dynamic behavior of a soil-pile-quay wall (SPQW) system subjected to liquefaction-induced lateral spreading in terms of experimental investigation, numerical simulation, and global sensitivity analysis (GSA). A large-scale (1g) shake-table experiment on a SPQW system is presented in detail, including sensor arrangement, model configuration, and experimental results. Typical liquefaction phenomena, such as sand boils and ground settlement, were observed during the test. The shake-table experiment results were used to validate a three-dimensional (3D) nonlinear finite-element (FE) model developed for dynamic analysis of a fully coupled soil-water system. This FE model accounts for the interactions of the soil, pile, and quay wall through explicitly modeling them as an integrated system. Based on the validated FE model, a GSA was performed to further investigate how variations in system properties influence the dynamic responses of the SPQW system. The GSA with high computational efficiency was implemented using the polynomial chaos expansion (PCE) surrogate model, and the GSA results indicate the relative importance of modeling parameters, which provides insightful information about the system behavior. The presented work provides useful guidance on experimental and numerical simulations of typical SPQW system.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.058
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.200
Teacher spread0.194 · 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.

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".

Quick stats

Citations39
Published2018
Admission routes1
Has abstractyes

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