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Record W2156344559 · doi:10.5555/2429759.2430112

Simulation of mobile falsework utilization methods in bridge construction

2012· article· en· W2156344559 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWinter Simulation Conference · 2012
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBridge (graph theory)EngineeringConstruction engineeringShoringConstruction managementSequence (biology)Civil engineeringComputer scienceStructural engineering

Abstract

fetched live from OpenAlex

Scaffolds and shoring systems are generally referred to as the falsework in bridge construction, serving as temporary structures to support bridge span construction. The falsework cost usually accounts for 50--70% of the total project concrete budget. Falsework installation and advancing methods can greatly impact the completion time and actual cost. Thus, simulation can be instrumental in planning bridge construction operations and analyzing various options by evaluating postulated what-if scenarios. This study uses a previously constructed bridge in Sweden as a case study to test three feasible construction sequence alternatives. One of these alternatives was implemented on the actual construction of this bridge. Modeling was performed in Simphony, which captures the unique construction sequence requirements and constraints, resulting in project durations for each alternative. Results from simulation experiments were corroborated by the construction engineer who had worked on the bridge project in terms of the advantages that each alternative method possesses.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.800
Threshold uncertainty score0.606

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.070
GPT teacher head0.358
Teacher spread0.289 · 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