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Record W2058203450 · doi:10.1139/l07-089

Minimisation des surcoûts dans un contexte hors délai : cas des projets de construction dans les pays en développement

2007· article· en· W2058203450 on OpenAlexvenueno aff
Paul Louzolo-Kimbembé, Chrispin Pettang, Thomas Tamo Tatiétsé

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2007
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsCost overrunTotal costContext (archaeology)Duration (music)Computer scienceCost analysisOperations managementOperations researchConstruction industryMathematicsEngineeringEconomicsConstruction engineeringGeographyAccounting

Abstract

fetched live from OpenAlex

Cost overruns are frequent in the construction industry. They become greater the more the construction project is delayed. In developing countries, this situation is recurring and constitutes a significant financial problem. In a time-overrun context, our objective is to find a method that can enable us to reduce the delay while minimizing the subsequent cost overruns. We thus developed a mathematical model named CCOMTOC (construction cost optimisation model in time-overrun context). The model was tested and results reveal that the reduction of cost overruns is effective. We showed that in a time-overrun context, according to the importance of delay penalties, we obtain two distinct situations. On the basis of the reference cost calculated for a maximum compression of tasks within the normal duration, we note that, for low delay penalties, the total cost after further delays to the estimated completion time decreases as we deviate from the initial completion time. On the other hand, for relatively high delay penalties, the cost first passes by a minimum before increasing regularly during the further delays to the estimated completion time.Key words: time overrun, optimization, linear programming, time skid, delay make-up, cost overrun.

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)0.004
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.334
Threshold uncertainty score0.676

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.048
GPT teacher head0.293
Teacher spread0.245 · 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 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

Citations1
Published2007
Admission routes1
Has abstractyes

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