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Record W3022835966 · doi:10.18280/ijsdp.150318

Development and Analysis of Prefabricated Concrete Buildings in Chengdu, China

2020· article· en· W3022835966 on OpenAlexvenueno aff
Gang Yao, Mingpu Wang, Yang Yang, Jun Li

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2020
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsnot available
FundersFundamental Research Funds for the Central Universities
KeywordsChinaArchitectural engineeringCivil engineeringEngineeringForensic engineeringEnvironmental scienceGeographyArchaeology

Abstract

fetched live from OpenAlex

Prefabricated construction is an inevitable trend in the reform of modern construction, playing an important role in sustainable development and conserving resources. Based on field investigation, questionnaire survey, and the collection and analysis of relevant data, the prefabricated concrete buildings of Chengdu (China) were researched. The development status of prefabricated concrete buildings, production base of prefabricated components, price of precast concrete, and the scale of building construction industry were analyzed and studied. The development obstacles of the prefabricated concrete building were presented and relevant suggestions were provided. The research shows that the production base of prefabricated component is reasonably laid out by forming a radiation circle with the reasonable radius. The price of the prefabricated component is controlled by the average reduction of 1039.8 yuan/m 3 , and the proportion of prefabricated concrete buildings is adjusted by an increase of 8% to 10% per year. This can promote the development of prefabricated concrete buildings in Chengdu. The incomplete standardization system is the most serious development obstacle, and the establishment of local standards is an effective development proposal. This research provides a reference for the development of prefabricated concrete buildings in large and medium-sized cities.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.010
GPT teacher head0.220
Teacher spread0.209 · 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 designObservational
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

Citations7
Published2020
Admission routes1
Has abstractyes

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