MétaCan
Menu
Back to cohort
Record W4391598582 · doi:10.2749/newdelhi.2023.0464

Case Study of Rochdale Canal Bridge Assessment using Non-Linear Finite Element analysis

2023· article· en· W4391598582 on OpenAlex
Sai Anoop Aditya Voleti, Sreejith Parayil, Abani Kumar Satapathy

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

VenueReport · 2023
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsSNC-Lavalin (Canada)
Fundersnot available
KeywordsStructural engineeringBucklingFinite element methodBridge (graph theory)SkewEngineeringComputer science

Abstract

fetched live from OpenAlex

<p>Rochdale Canal Bridge is a single skew span railway under bridge to be assessed under Civils Assessment Framework Agreement 2020-2024. The scope of work is limited to the decks supporting the railway lines.</p><p>The methodology used to develop a finite element model that accurately represents the structure, the complexities of the structure due to load effects from neighbouring decks etc., are also discussed in the paper. Furthermore, material non-linearity was considered along with geometric non- linearity. The most onerous buckling mode derived from linear Eigen value buckling analysis is used for the initial geometry imperfection of structure. Non-linear analysis was carried out at both SLS and ULS to identify the failure modes through excess stress, strain, yielding and buckling. Through this study, the reserve strength of the structure was captured and is found to be adequate for the current loading which is an improvement from previous assessment. The assessment of these kind of bridges and suggesting strengthening measures etc, will help reduce the environmental impact as compared to the new constructions thereby contributing to sustainability.</p>

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: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.043
GPT teacher head0.332
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