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Record W2004170337 · doi:10.1139/l07-112

Sustainable precast concrete foundation system for residential construction

2008· article· en· W2004170337 on OpenAlexafffundvenueabout
Haitao Yu, Mohamed Al‐Hussein, Reza Nasseri, Roger Cheng

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsAlberta Conservation AssociationUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsConstructabilityPrecast concreteEngineeringCivil engineeringFoundation (evidence)Framing (construction)Construction engineeringArchitectural engineeringStandardizationStructural systemExpanded polystyreneStructural engineeringComputer science

Abstract

fetched live from OpenAlex

Residential construction has changed little in decades. In North America, houses are constructed predominantly with cast-in-place (CIP) concrete basement foundations and “custom-built” wood platform – framing structures. To improve the productivity of residential construction, a precast concrete foundation (PCF) system was developed by the University of Alberta and its industrial partners. Although the PCF system can be built much faster and better than conventional CIP foundations, it has not been considered a viable alternative in residential construction due to the belief that it is costly and less flexible. To overcome this perception, special research efforts were made to address issues of manufacturability, constructability, and standardization. This paper proposes an innovative design of the PCF system that satisfies functional requirements, while obtaining a minimum total cost and achieving flexibility. The unique features include a modularized rib structure, external insulation, and simplified bolted connections. A comparative analysis between PCF and traditional CIP concrete foundation systems is also presented.

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: none
Teacher disagreement score0.730
Threshold uncertainty score0.489

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 head0.007
GPT teacher head0.170
Teacher spread0.164 · 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

Citations12
Published2008
Admission routes4
Has abstractyes

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