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Record W7047538529

A framework for estimating the total cost of buried municipal infrastructure renewal projects

2009· dissertation· en· W7047538529 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2009
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicSuperconducting and THz Device Technology
Canadian institutionsnot available
Fundersnot available
KeywordsTrenchless technologySewerageWork (physics)Total costCost estimateCost–benefit analysisExternalityMunicipal or urban engineeringIndirect costs
DOInot available

Abstract

fetched live from OpenAlex

As Canadian municipalities venture into rehabilitation and replacement of extensively deteriorated underground water distribution and sewerage assets, municipal decision-makers, engineering and construction research bodies and the public all feel that this type of construction work can have adverse effects on the society. This thesis reviews these negative impacts which include, but are not limited to damage of nearby buried and above-ground infrastructure, disruption of traffic, loss of accessibility to businesses, health hazards to workers and the public, and finally environmental pollution and damage. There is presently no accepted practice-oriented method for the evaluation of these social, economic and environmental impacts. This research project proposes a framework to enable municipalities, utility agencies and contracting firms to quantitatively estimate the total cost to society of buried municipal infrastructure renewal projects using open trench, or trenchless construction methods. The total cost of a project is the sum of all the direct and indirect costs borne by the client organization, and external costs borne by society. The external costs can be separated into three components: social, economic and environmental costs. Use of the proposed methodology in a case study of a water main rehabilitation project using trenchless technologies in the city of Montreal, Canada, revealed that the indirect and external costs of the project were approximately 25 percent of its direct costs. The most significant cost components were those attributable to increased vehicular travel time and lost business income.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.436
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.022
GPT teacher head0.285
Teacher spread0.263 · 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 designTheoretical or conceptual
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
Published2009
Admission routes1
Has abstractyes

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