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Record W2328463100 · doi:10.1061/40937(261)57

Sustainability, Whole Life Costs, and Information and Communication Technologies: A Review of Published Works

2007· review· en· W2328463100 on OpenAlexaff
Mohamed Issa, Jeff H. Rankin, A. John Christian

Bibliographic record

Venuenot available
Typereview
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsSustainabilityInformation and Communications TechnologyProcess (computing)Process managementWork (physics)Computer scienceKnowledge managementBusinessRisk analysis (engineering)Management scienceEngineering

Abstract

fetched live from OpenAlex

This paper reviews research work that has attempted to assess the financial benefits of adopting sustainable design and construction practices, and identifies a connection to information and communication technology (ICT) used to facilitate the adoption of those practices. A review of the literature found that: researchers disagree on the precise impact of sustainability upon the whole life costs (WLC) of buildings; the impact of sustainability upon usage costs tends to be ignored despite their considerable size; and the use and adoption of ICT tools remains costly, limited and inefficient in sustainable projects. Future research needs therefore to focus on: investigating a sufficient number of conventional and sustainable buildings, assessing all types of long-term costs incurred in those buildings, evaluating buildings' usage costs in more details, and using empirical documented cost data whenever these are available. Future research also needs to show ways of improving sustainable design and construction processes by: assessing the specific impact of ICT tools on related processes, assessing the collaborative decision-making process in general, and developing a comprehensive model to ensure effective adoption and implementation of ICT solutions that have shown to improve design and construction processes.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.958
Threshold uncertainty score0.580

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.013
GPT teacher head0.274
Teacher spread0.261 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations3
Published2007
Admission routes1
Has abstractyes

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