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Record W3129431246 · doi:10.1016/j.procs.2021.01.264

Simulation of ground bearing pressure profile under hydraulic crane outrigger mats for the verification of 16-point combined loading

2021· article· en· W3129431246 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

VenueProcedia Computer Science · 2021
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsOutriggerFinite element methodComputer scienceModular designStructural engineeringPoint (geometry)Bearing (navigation)Hydraulic pressureMarine engineeringMechanical engineeringMathematicsEngineeringArtificial intelligenceGeometry

Abstract

fetched live from OpenAlex

The modular construction approach relies heavily on mobile cranes. With the increase in weight of modules, the ground bearing pressure (GBP) applied by the hydraulic crane also increases. To avoid ground failure, the primary technique is to determine the GBP by calculating the resulting force at each outrigger and assuming it is distributed uniformly over the surface area of the mat under the outrigger. Finite element analysis (FEA) indicates that the pressure profile beneath the crane mat is not uniform in nature, which means the traditional method of calculating the GBP is inaccurate. In this study, a new method is proposed to determine the GBP profile. The proposed methodology can determine the GBP at 16 points, the four corners of each of the four outriggers. For a theoretical case study, a hydraulic crane Grove GMK 7550 with three different payloads is examined and the results are verified by using FEA.

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: none
Teacher disagreement score0.916
Threshold uncertainty score0.235

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 head0.016
GPT teacher head0.234
Teacher spread0.217 · 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