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Record W2594236441 · doi:10.3390/app7020180

Construction Industrialization in China: Current Profile and the Prediction

2017· article· en· W2594236441 on OpenAlexaff
Yingbo Ji, Fadong Zhu, Hong Li, Mohamed Al‐Hussein

Bibliographic record

VenueApplied Sciences · 2017
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsUniversity of Alberta
FundersNational Key Research and Development Program of ChinaMinistry of Science and Technology of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsIndustrialisationChinaDelphi methodRegional scienceConstruction industryEconomic growthBusinessGeographyEngineeringPolitical scienceEconomicsConstruction engineeringComputer science

Abstract

fetched live from OpenAlex

The ongoing undertaking of construction industrialization in China is redefining the industry and creating a new era for building construction. In order to identify the construction industrialization status and progress, a national survey is conducted across 19 key provinces and municipalities in China. Based on the collected data, construction industrialization is analyzed from various perspectives: (1) the industrialized building floor area is profiled using maps with colours showing the different levels of construction industrialization in China as of 2014; and (2) structural types and building types are analyzed for industrialized construction, and it is found that reinforced concrete is the predominant structure type, accounting for 77.1% of total floor area of industrialized construction in 2014. The industrialization trends are also predicted for the following five years using Holt’s and Delphi method. This research reveals the status and the promising trends of construction industrialization in China.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.066
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.006
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.258
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), 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

Citations55
Published2017
Admission routes1
Has abstractyes

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