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Record W1998279864 · doi:10.1108/09699981011038079

A multi‐attribute ranking method for bridge management

2010· article· en· W1998279864 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

VenueEngineering Construction & Architectural Management · 2010
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsConcordia University
Fundersnot available
KeywordsAnalytic hierarchy processRanking (information retrieval)Computer scienceFlexibility (engineering)Bridge (graph theory)Rank (graph theory)Asset managementMultiple-criteria decision analysisRisk analysis (engineering)Decision support systemOperations researchData miningManagement scienceEngineeringMachine learningMathematicsBusiness

Abstract

fetched live from OpenAlex

Purpose A bridge network is a major capital asset that requires continuing investment in order to maintain the network within acceptable limits of safety and serviceability. Ranking and prioritizing procedures have been widely used by several departments of transportation to select bridges for intervention and to distribute the available funds among competing projects. The available ranking and prioritizing procedures have various drawbacks, and an improved, rational ranking and prioritizing procedure is needed. The paper aims to address these issues. Design/methodology/approach The requirements and characteristics of an innovative ranking and prioritizing method are identified during interviews with professionals involved in bridge management. Based on these requirements, multi‐attribute utility theory (MAUT) is selected to develop the method. A technique to develop utility functions based on the analytical hierarchy process (AHP) is discussed. A hierarchy structure that captures the decision‐making elements is presented. A case study is used to demonstrate the applicability and the validity of the proposed ranking method. Findings The research findings have identified the decision objectives and the criteria essential to rank and prioritize bridge projects, and these are included within a framework to rank and prioritize bridge projects while incorporating experts' input in the process. Practical implications The proposed framework includes weights for the various objectives and recommends utility functions to evaluate the different attributes. In addition, the framework provides flexibility to adjust the weights and to modify the utility functions to reflect network‐specific characteristics. This method can be used by departments of transportation to rank bridges in a network, even incorporating conflicting criteria, and it can be integrated within an already implemented bridge management methodology. Originality/value Ranking and prioritizing projects are essential steps in bridge management. Current methods for ranking and prioritizing bridge projects are associated with various drawbacks. This paper proposes an innovative ranking method for bridge networks, based on MAUT. This theory provides flexibility for the decision makers in expressing their degree of satisfaction with each bridge attribute.

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 categoriesMeta-epidemiology (narrow)
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 score0.463
Threshold uncertainty score1.000

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 communication0.0000.000
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 head0.006
GPT teacher head0.230
Teacher spread0.224 · 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