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Record W4412691769 · doi:10.3390/buildings15152643

Seismic Performance of Large Underground Water Tank Structures Considering Fluid–Structure Interaction

2025· article· en· W4412691769 on OpenAlexaff
Fang-Yuan Xu, Chengshun Xu, M. Hesham El Naggar, Xiuli Du

Bibliographic record

VenueBuildings · 2025
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsWestern University
FundersNatural Science Foundation of Beijing Municipality
KeywordsFluid–structure interactionSeismic analysisStructural engineeringPetroleum engineeringGeologyGeotechnical engineeringEngineeringFinite element method

Abstract

fetched live from OpenAlex

The widespread application of large underground water tank structures in urban areas necessitates reliable design guidelines to ensure their safety as critical infrastructure. This paper investigated the seismic response of large underground water tank structures considering fluid–structure interaction (FSI). Coupled Eulerian–Lagrangian (CEL) was employed to analyze the highly nonlinear FSI caused by intense fluid sloshing during earthquakes. The patterns of fluid sloshing amplitude observed from the finite element model were summarized based on analyses of fluid velocity, hydrodynamic stress components, and overall kinetic energy. In addition, the seismic response of the water tank structure was thoroughly assessed and compared with the simulation results of the empty tank structure. The results indicate that significant fluid sloshing occurs within the structure under seismic excitation. The amplitude of fluid sloshing increases horizontally from the center toward the edges of the structure, corresponding to higher hydrodynamic loads at the side area of the structure. By comparing the analysis results of the water tank structure with and without water, it was concluded that FSI is the primary cause of structural damage during an earthquake. The hydrodynamic loads on the roof, diversion walls, and external walls lead to significant localized damage.

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 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 score0.234
Threshold uncertainty score0.493

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 head0.005
GPT teacher head0.233
Teacher spread0.228 · 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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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