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Record W4409210446 · doi:10.1061/9780784486085.042

Module Land Transport Stability for Energy and Industrial Facilities

2025· article· en· W4409210446 on OpenAlexaff
Felipe Mejia, Alvaro Campos, Silky Wong, David Kerins, Luca Magenes

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Resilience and Vulnerability Analysis
Canadian institutionsDow Chemical (Canada)
Fundersnot available
KeywordsEnergy (signal processing)Stability (learning theory)Environmental scienceComputer sciencePhysics

Abstract

fetched live from OpenAlex

A key aspect of modular construction is the interface between engineering and moving operations. While conventional “stick built” operations consider construction loads as operational load cases, modular construction involves moving and lifting operations of very significant loads and sizes, which require a more formal approach to between the engineering, planning, and execution of such operations. Overlooking the engineering of these critical moving operations at the front end of the project can result in costly failures with severe outcomes involving personnel safety, project costs, and schedule overruns. To avoid unnecessarily costly or unsafe designs at the later stages of a project, the interface points between equipment or module and trailer must be established starting in the beginning stages of the project among the project team. The land transport stability is determined by checking axle loadings, combined geometric stability, dynamic stability angle, and trailer spine beam analysis. A calculation example of these checks is also included in the Appendix of this paper. An SPMT transport stability calculation example is included in the Appendix of this paper. The calculation example, information, resources, and recommended future works provided in this paper will give practicing engineers a more thorough understanding of land transport module designs and analyses.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score0.299

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 head0.014
GPT teacher head0.211
Teacher spread0.197 · 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
Published2025
Admission routes1
Has abstractyes

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