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Record W2105464591 · doi:10.4236/ojg.2013.33024

Design of Foundations Built on a Shallow Depth (Less than 4 m) of Egyptian Macro-Porous Collapsible Soils

2013· article· en· W2105464591 on OpenAlexaff
Tahar Ayadat, Adel Hanna

Bibliographic record

VenueOpen Journal of Geology · 2013
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsConcordia University
Fundersnot available
KeywordsSettlement (finance)Geotechnical engineeringAridSoil waterGeologyFoundation (evidence)Shallow foundationMining engineeringCivil engineeringEngineeringGeographySoil scienceArchaeologyBearing capacity

Abstract

fetched live from OpenAlex

It is nowadays well reported that collapsible soils spread in many countries, including United States, Russia, China, South America (e.g. Brazil), South and North Africa (e.g. Egypt, Algeria), Middle East (e.g. Saudi Arabia) and many countries in Eastern Europe. In general, collapsible soils are located in arid and semi-arid regions around the world. This special type of soil is characterized by abrupt reduction in strength, excessive and sudden settlement when it becomes wet leading to failure of the structure. Construction on such a kind of soil is one of the prominent problems in geotechnical engineering. The main objectives of this study are reporting geological and geotechnical zonation maps for potentially collapsible soils in inhabited areas in Egypt. Furthermore, a design technique for foundations built on a shallow depth of Egyptian macro-porous collapsible soils (less than 4 m) is developed. The design method includes a design chart for soil collapse field classification in terms of the most governing parameters, a method for foundation settlement estimation based on a correlation between the wetting-induced collapse strain and the applied pressure, and a design practice to guide practicing engineers to select the appropriate foundation system to construct on such soil with a great degree of confidence and safety.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.571
Threshold uncertainty score0.507

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 science0.0010.000
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 head0.040
GPT teacher head0.267
Teacher spread0.227 · 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

Citations14
Published2013
Admission routes1
Has abstractyes

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