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Record W2955074025 · doi:10.29173/mocs109

Assessing the Environmental Implications of Industrialized Housing: a Systematic Literature Review

2019· article· en· W2955074025 on OpenAlexvenueno aff
Firehiwot Kedir, Daniel Hall

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2019
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental impact assessmentPrefabricationLife-cycle assessmentProcess (computing)Systematic reviewEngineeringBusinessRisk analysis (engineering)Architectural engineeringOperations managementComputer scienceCivil engineeringProduction (economics)EconomicsPolitical science

Abstract

fetched live from OpenAlex

Industrialized Housing (IH), also referred to as prefabrication, preassembly, modularization, and/or off-site fabrication, is a growing strategy for constructing housing. IH offers potential for significant reduction of environmental impact in comparison to traditional housing construction. Past research used methods such as environmental impact assessment on given case study buildings or expert’s opinions to identify the benefits and drawbacks present on the lifecycle of houses constructed partially or fully using IH methods. Nevertheless, this literature is scattered across several sources and units of analysis. The specific factors of IH that contribute to environmental impact reduction have not been comprehensively reviewed and summarized from design considerations up to the end of life possibilities. In this paper, a systematic literature review is performed on the environmental implications of the industrialized way of constructing residential buildings. From a review of 49 journal publications, this paper identifies 18 key factors that influence the environmental performance of such residential buildings. These factors are categorized into the following lifecycle phases of the IH process: a) system design, b) material design, c) manufacturing and logistics, d) transportation and assembly, e) Operational phase, and f) end of life. Findings reveal the importance of decisions made in design phases such as choice of materials, which in turn show a snowball effect throughout the phases. A final category – g) support and hindrance of IH - includes a discussion of external factors such as building codes and regulatory policies and their impact on IH performance.

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: Systematic review · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.288
Threshold uncertainty score0.608

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 communication0.0000.001
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 head0.218
Teacher spread0.208 · 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 designSystematic review
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

Citations4
Published2019
Admission routes1
Has abstractyes

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