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Record W4409502267 · doi:10.5006/mpwt19-14244

The Quebec Bridge Collapse (1907): an Engineer’S Perspective to Avoid Catastrophic Failures

2019· article· en· W4409502267 on OpenAlexaboutno aff
Saleem Parvez Mistry

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsBridge (graph theory)Perspective (graphical)Forensic engineeringCatastrophic failureEngineeringComputer scienceMaterials scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract The failure assessment of the collapse of the Quebec Bridge in 1907 conducted by the Royal Commission is discussed in the following paper. The Quebec Bridge is a 987.5 m long; 29 m wide; and 104 m high riveted steel truss structure which collapsed not once but twice during construction. The reason for the bridge failure was attributed to member behavior and stability proved by experimental work conducted following the collapse by Royal Commission. The bridge was finally completed in 1917 and has been in operation since then. The lessons learned from the bridge collapse were pivotal in the advancement of engineering design, fabrication and formation of the two organizations, namely - the American Association of State Highway and Transportation Officials (AASHTO) in 1914 and American Institute of Steel Construction (AISC) in 1921. The author highlights the importance of validating the design criteria and specifications by material and load testing, conducting peer reviews, design control, and paying attention to details. The lessons learned reinforce the need to establish and monitor shop fabrication practices, inspection procedures and gates (witness, hold and review points) to safely complete the execution of any civil engineering project, be it onshore or offshore construction.

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 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: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.544

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 head0.005
GPT teacher head0.216
Teacher spread0.211 · 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 teacher head, 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

Citations0
Published2019
Admission routes1
Has abstractyes

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