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Record W4402481714 · doi:10.1201/9781003483755-4

Bridge management – past, present, and future

2024· book-chapter· en· W4402481714 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

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsStantec (Canada)
Fundersnot available
KeywordsBridge (graph theory)EngineeringForensic engineeringConstruction engineeringMedicine

Abstract

fetched live from OpenAlex

The history of bridge engineering is a story of human innovation. As long as humans have existed there has been a need to bridge gaps such as streams, gullies, canyons etc. The history of bridge management and bridge management systems (BMS) is also a story of innovation as bridge owners eventually began to be responsible for many structures, constructed using various materials, exposed to varying demands. BMS are powerful computer solutions that help bridge owners responsibly manage lifecycle costs, risk, determine optimized priority programs, report current and forecasted condition and other performance measures. Today BMS help sustainably maintain the inventory while considering a variety of socio-economic factors and are proving instrumental in helping owners manage the effects of climate change and extreme weather.In this lecture, we will take a high level review of bridge management practices in the past, present, and postulate where bridge management might be heading in the future.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.733
Threshold uncertainty score0.875

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)0.0010.000

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 head0.007
GPT teacher head0.193
Teacher spread0.186 · 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