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Record W4412912608 · doi:10.1016/j.compag.2025.110698

A triaxial in-situ stress measurement system for investigating radial variability of the vertical-to-lateral pressure ratio in grain silos

2025· article· en· W4412912608 on OpenAlexafffund
George Dyck, Adam Rogers, Michael D. Montross, Kurt Hildebrand, Aaron P. Turner, Jitendra Paliwal, Barry Farmer, Carlos A. Jarro

Bibliographic record

VenueComputers and Electronics in Agriculture · 2025
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsUniversity of WinnipegBrandon UniversityUniversity of Manitoba
FundersUniversity of Manitoba
KeywordsInformation siloGeotechnical engineeringStructural engineeringStress (linguistics)In situEngineeringGeologySiloMaterials scienceMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

Accurately measuring the three-dimensional stress state within bulk granular materials remains a significant challenge in instrumentation, limiting the validation and improvement of predictive models for bulk solid structures, such as grain silos. In this study, we introduce a novel triaxial pressure sensor for in-situ measurements in grain silos. This work aims to measure vertical and radial pressure distributions in bulk solids. The sensor was developed to allow for potential refinement and validation of mathematical models, focusing on the coefficient of vertical-to-lateral pressure ratio ( k ). We cover the design, calibration, and initial application of the sensor in a large-scale grain silo experimental setup. The sensor consisted of three orthogonal piston-style pressure cells housed in a 3D-printed spherical shell, with an estimated accuracy of ± 0.35 kPa. Initial experimental measurements were obtained using the sensors placed in soft red winter wheat (13.5% moisture content) within a silo measuring 1.8 m in diameter and 6 m in height. Our preliminary data indicated differences in pressure readings at different radial positions, with calculated k -values of 0.23 near the wall, 0.17 at mid-radius, and 0.37 at the centre. While these observations suggest potential variations in k across the silo radius, additional experimental runs would be necessary to establish statistical reliability. The data were fit to Janssen’s model by exploring the parameter space of the material properties ( μ , ϕ , R ). This sensor technology represents an advancement in measurement capability that could potentially contribute to more accurate mathematical models for bulk solid storage management in the future. • Developed a novel triaxial pressure sensor for measuring vertical and radial pressures in grain silos. • Sensor achieves ±0.35 kPa accuracy using three orthogonal pressure cells in a spherical housing. • Preliminary experimental data reveal radial variation in vertical-to-lateral pressure ratio (k). • Results indicate the need for refined mathematical models incorporating radial variation of properties.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.527
Threshold uncertainty score0.389

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 head0.007
GPT teacher head0.195
Teacher spread0.188 · 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 designBench or experimental
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

Citations1
Published2025
Admission routes2
Has abstractyes

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