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Record W3083778987 · doi:10.36680/j.itcon.2020.024

Construction 4.0: a survey of research trends

2020· article· en· W3083778987 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Information Technology in Construction · 2020
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsDigitizationStructuringConstruction industryQuality (philosophy)EngineeringEmerging technologiesIndustry 4.0Construction managementConstruction engineeringBusinessEngineering managementComputer scienceCivil engineeringTelecommunications

Abstract

fetched live from OpenAlex

The fourth industrial revolution, called Industry 4.0, is transforming decision-making through the increasing use of information and digitization technologies. While Industry 4.0 is expanding rapidly in manufacturing industries, its induced transformations are gradually affecting other sectors, including the construction industry. In recent years, the use of 4.0 technologies in the construction industry, termed as ‘Construction 4.0’, has increased, mostly due to the immense potential of Industry 4.0 for improving the performance of construction projects and structuring their underlying management processes. This paper proposes a classification of existing literature on applications of Construction 4.0 technologies to allow for a better analysis of trends and gaps in the research. A total of nearly 200 research papers between 2009 and 2020 were reviewed and analyzed. Overall, the analysis shows that research on Construction 4.0 is closely aligned with the construction phase. Also, the most researched topics seem to be related to the management processes of quality, risk, and health 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.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.901
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.277
Teacher spread0.252 · 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