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Record W4297348884 · doi:10.56952/arma-2022-0430

Thermal Imaging for Rockfall Detection

2022· article· en· W4297348884 on OpenAlexaboutno aff
Edward C. Wellman, K. W. Schafer, C. P. Williams, Brad Ross

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMineral Processing and Grinding
Canadian institutionsnot available
Fundersnot available
KeywordsRockfallEnvironmental scienceRemote sensingVisibilityGeologyMeteorologyLandslideGeotechnical engineeringGeography

Abstract

fetched live from OpenAlex

ABSTRACT: Under a NIOSH research grant, the Geotechnical Center of Excellence (GCE) evaluated the capability and limitations of thermal cameras to detect rockfall events and the conditions that can lead to rockfall. This two-year study tested four different thermal imaging cameras in controlled rockfall experiments and long-term surveillance tests in various mines and conditions. The cameras can detect rockfall events at less than their resolution as rockfall creates larger craters, impacts, and dust trails visible in the thermal IR. The surveillance test determined that the cameras work in both day and night conditions under a wide range of conditions. However, fog, heavy precipitation (rain, snow, hail), and dust limited visibility in the thermal IR. Four thermal infrared cameras were acquired for the project. The cameras were installed on a mobile mine monitoring platform. The project tested thermal imaging cameras’ effectiveness to detect and record rockfall events and rockfall hazards in surface mining operations as a method to protect mine workers from the risks of rockfalls. One of the primary findings of the project is that off-the-shelf cameras can be used to increase situational awareness at the mines in low-light conditions. This presentation will document camera selection, resolution, and results from the project. 1. INTRODUCTION The Thermal Imaging project for Rockfall Detection Mobile Monitoring Platform (MMP) has been deployed at seven different open-pit mines for over one year. Since April 7, 2021, the MMP has been deployed with all four thermal cameras installed and operational at seven Western US and Canada mine sites. The system was initially deployed at two different mine sites in Arizona starting in January 2021 with three cameras. The research project has tested thermal imaging cameras’ effectiveness in detecting and recording rockfall events and rockfall hazards in surface mining operations as a method to protect mine workers from the risks of rockfalls. One of the primary goals is to identify cost-effective, off-the-shelf systems that can be integrated with existing slope monitoring systems or installed separately as a part of routine mine observations.

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 score0.279
Threshold uncertainty score0.152

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 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

Citations3
Published2022
Admission routes1
Has abstractyes

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