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Record W2617952501 · doi:10.11159/icepr17.132

Reliability and Uncertainty in Analysis of Rammed Earth Walls

2017· article· en· W2617952501 on OpenAlexvenueno aff
Ehsan Kianfar, Vahab Toufigh

Bibliographic record

VenueProceedings of the World Congress on New Technologies · 2017
Typearticle
Languageen
FieldEngineering
TopicHygrothermal properties of building materials
Canadian institutionsnot available
Fundersnot available
KeywordsRammed earthReliability (semiconductor)Earth (classical element)Computer scienceReliability engineeringAstrobiologyGeotechnical engineeringGeologyEngineeringMathematicsPhysics

Abstract

fetched live from OpenAlex

This paper deals with reliability analysis and uncertainty of rammed earth (RE) structures as a widely used sustainable and environment-friendly structure. Due to lack of comprehensive design standards, the engineers often rely on "rule-of-thumb" methods, which leads to quite conservative or unsafe designs. In this study, uncertainty of load and resistance parameters were included in analysis of rammed earth structures. The reliability index and failure probability of RE structures were evaluated using First-Order-Reliability-Method (FORM). The analysis was performed based on the different loads and resistance random variables parameters. Based on the results, the recommended wall thickness by various codes are quite conservative. On the other hands, larger wall thickness is required under severe loadings conditions. The compressive strength of unstabilized materials under severe loading conditions should be more than minimum recommended. The sensitivity analysis on the random variables indicates that the compressive strength and the environmental loads factors are the most important random variables that contribute to reliability of the structures.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.015
GPT teacher head0.235
Teacher spread0.220 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on New TechnologiesSame topicHygrothermal properties of building materialsFrench-language works237,207