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Risk Assessment of Construction Organization Plans for Bridge Projects

2013· article· en· W2062871318 on OpenAlexaff
Yu Qian Wang, Guo Yan Bian, Yi Li

Bibliographic record

VenueAdvanced materials research · 2013
Typearticle
Languageen
FieldEngineering
TopicEvaluation and Optimization Models
Canadian institutionsMinistry of Transportation of Ontario
Fundersnot available
KeywordsBridge (graph theory)Plan (archaeology)Risk management planEngineeringAnalytic hierarchy processRisk managementConstruction engineeringRisk assessmentRisk analysis (engineering)Construction managementConstruction site safetyProcess (computing)IT risk managementCivil engineeringComputer scienceBusinessOperations research

Abstract

fetched live from OpenAlex

This article filled up the blank between risk management in design stage and construction stage of bridge engineering, and proposed the concept of carrying out safety risk management through making prevention measures in construction organization plan. Based on the intrinsic safety theory, five elements, i.e. human, object, environment, system and method in bridge engineering construction management plans were analyzed by analytic hierarchy process and systemic analysis method. Fifteen crucial sub-elements were summarized and index system method was established for the safety risk assessment of sub-elements in construction management plans. Based on the research of the transformation of risk sources in construction management plan to risk events in construction period, expert survey method was established for the safety risk assessment of sub-elements in construction management plans.

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)0.004
metaresearch head score (Gemma)0.008
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.063
GPT teacher head0.386
Teacher spread0.323 · 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
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
Published2013
Admission routes1
Has abstractyes

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