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Record W2753805124 · doi:10.20381/ruor-20850

Integrating Bridge Information Modeling (BRIM), Bridge Sustainability Rating System (BRSRS), Bridge Environmental Performance Strategy Mapping (BREPSM) and Cost Estimating At the Conceptual Design Stage

2017· dissertation· en· W2753805124 on OpenAlexaboutno aff
Aly Elgayar

Bibliographic record

VenueuO Research (University of Ottawa) · 2017
Typedissertation
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsnot available
Fundersnot available
KeywordsBridge (graph theory)SustainabilityConceptual designEngineeringConceptual frameworkStage (stratigraphy)Computer scienceSystems engineeringConstruction engineeringMechanical engineeringGeologyEcology

Abstract

fetched live from OpenAlex

Bridges are crucial infrastructure for urban development as cities rely heavily on various modes of transportation for access and mobility. In an effort to fill the gap in the knowledge and methodology used in the construction of sustainable bridges, a model is developed using the concept of BrIM having the capabilities to develop bridges at the conceptual design stage, which offers ample versatility to influence stakeholders’ decisions towards sustainable bridge design. The model incorporates a knowledge-based decision support system and four modules namely: BrIM module; the first ever Bridge Sustainability Rating System (BrSRS) module; Bridge Environmental Performance Strategy Map (BrEPSM) module; and a conceptual cost estimating module. The model takes fundamental data input and processes it through the knowledge-based system established based on MTO’s Highway Geometric Design and the Navigational Waterways Clearance guidelines. The sustainability capabilities of the model are broken into two sub- modules; a BrSRS was developed by using the amalgamation of various existent highways and roads sustainability rating systems and by considering the introduction of bridge design. The system mimics the style of LEED as users can select from a weighted list of sustainable construction activities and materials to accumulate credits towards a sustainability classification. The second includes a BrEPSM that the forecasts footprints levels of bridge projects based on 5 footprint indicators namely; carbon; water; energy; emissions; and work environment with data obtained from Statistics Canada pertaining to each footprint illustrated on a radar graph. The third module takes the knowledge-based output and presents it in 3D mode via AutoCAD allowing users to alter the drawing’s dimensions and accordingly the model reiterates the calculations based on the changes made in the 3D CAD model. The final module generates an approximate cost estimate of the conceptually designed bridge, which is ideal for the feasibility study of the project.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0040.005
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.047
GPT teacher head0.274
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations0
Published2017
Admission routes1
Has abstractyes

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