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Record W2734618222 · doi:10.24928/2017/0057

Design-Build and Design-Bid-Build in Construction- a Comparative Review

2017· review· en· W2734618222 on OpenAlexaff
Titouan Plusquellec, Nadia Lehoux, Yan Cimon

Bibliographic record

VenueAnnual Conference of the International Group for Lean Construction · 2017
Typereview
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsIntegrated project deliveryScheduleComputer scienceQuality (philosophy)SustainabilityKey (lock)Work (physics)Design–buildSystems engineeringProcess managementRisk analysis (engineering)Engineering managementProject managementEngineeringBusiness

Abstract

fetched live from OpenAlex

This paper aims at synthesizing relevant findings about the Design-Build (DB) construction delivery system from previous studies. It focuses especially on cost, schedule, quality, and various sustainability aspects associated with a construction project, as they are widely used as performance indicators in this industry. This work has been done through a literature review and a content analysis, focusing on papers comparing Design-Build to the classical Design-Bid-Build delivery system. The results of the literature review show that Design-Build outperforms Design-Bid-Build in terms of cost and schedule growth as well as in terms of delivery speed, all for a similar quality. Furthermore, it may suggest that Design-Build could help in reaching highly sustainable goals while being a key step in the implementation of lean construction. This study might allow owners to save time in their investigation about the performance of Design-Build, and even lead them to reconsider their project delivery system choice when planning for new project launches.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.390
GPT teacher head0.461
Teacher spread0.071 · 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.

Study designOther design
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

Citations2
Published2017
Admission routes1
Has abstractyes

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