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Record W4238283988 · doi:10.22215/etd/2021-14615

Evaluation of Seismic Behaviour of Hollow Fibre-Reinforced Polymer (FRP) Piles using Shake Table Testing

2021· dissertation· en· W4238283988 on OpenAlexafffundabout
Mohammadamin Hosseini

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsCarleton University
FundersUniversity of OttawaUniversité Laval
KeywordsEarthquake shaking tableGeotechnical engineeringFibre-reinforced plasticPileFoundation (evidence)Structural engineeringStiffnessGeologyMaterials scienceEngineering

Abstract

fetched live from OpenAlex

This study aimed at investigating the seismic performance of hollow FRP piles compared to traditional piles in fine sand and soft clay deposits using shaking table tests. A laminar shear box with a dimension of 1.0 m × 1.0 m and a depth of 1.0 m was employed to contain the soil medium and allow the soil to respond in the same fashion as the free field. Two types of composite group piles (2×2) made of Carbon Fibre-Reinforced Polymer (CFRP) and Glass Fibre-Reinforced Polymer (GFRP) along with a series of Aluminium piles were manufactured and embedded as frictional and end-bearing piles within the soil. Several monitoring instruments were used to observe the soil-pile response under variety of ground motions adopted from the 2010 Val-des-Bois Earthquake in Canada and the 1995 Kobe Earthquake. Seismic response of the foundation was strongly dependent on the stiffness provided by the soil, which was a function of the degree of softening and intensity of shaking. In both soil profiles, the foundation motion of model piles was higher than those of the free field under various intensity of input motions (0.02-0.16g). This was attributed to strong interaction between the soil and the foundation, and flexural stiffness of the model piles. However, the frequency content of the amplified motions appeared to be consistent for all model piles.

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 score0.041
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
Bibliometrics0.0000.001
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 head0.032
GPT teacher head0.260
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.

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

Citations2
Published2021
Admission routes3
Has abstractyes

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Same topicGeotechnical Engineering and Soil StabilizationFrench-language works237,207