MétaCan
Menu
Back to cohort
Record W2615132213 · doi:10.1061/9780784480045.018

Laboratory Testing of Enhancing the Bearing Capacity of Strip Footing with Woven Geotextiles

2016· article· en· W2615132213 on OpenAlexaff
Shengmin Wu, Jiunnren Lai, Chiung-Fen Cheng, Guo-Hao Lai, Chun-Jung Wei

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsTerrafix Geosynthetics (Canada)
Fundersnot available
KeywordsBearing capacityGeotechnical engineeringReinforcementTerzaghi's principleBearing (navigation)GeotextileStructural engineeringGeologyMaterials scienceEngineeringComputer sciencePore water pressure

Abstract

fetched live from OpenAlex

Laboratory sand box tests were performed in this study to investigate the improvement in bearing capacity of a strip footing reinforced with woven geotextile in dry sand. The test cell is 0.9m wide, 0.9m long and 1.0m high. A hydraulic loading system was used to apply the normal force to a 0.85m × 0.1m × 0.05m (length × width × height) strip footing. The unreinforced bearing capacity was obtained and compared with value calculated using Terzaghi’s equation. Various parameters such as: burial depth, length of reinforcement, number of layers and distance between layers were varied to investigate their effects on the bearing capacity. Results of these tests indicate that the optimum burial depth of reinforcement is about 0.4 times the width of footing, with a bearing capacity ratio (BCR) of about 1.67. The optimum reinforcement length is about 3~4 times the width of footing with a BCR value of 1.81. The improvements in bearing capacity obtained from laboratory testing are in accord with previous numerical simulation. However, the loading behaviors are quite different due to the failure mechanism assumed in the numerical simulation.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.386
Threshold uncertainty score0.194

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 head0.011
GPT teacher head0.168
Teacher spread0.157 · 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 designBench or experimental
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

Citations6
Published2016
Admission routes1
Has abstractyes

Explore more

Same topicGeotechnical Engineering and Soil StabilizationFrench-language works237,207