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Two 4D Models Effective in Reducing False Alarms for Struck-by-Equipment Hazard Prevention

2016· article· en· W2345457541 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

VenueJournal of Computing in Civil Engineering · 2016
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsFalse positive paradoxCuboidHazardComputer scienceFalse positives and false negativesFalse alarmIntersection (aeronautics)Pairwise comparisonData miningPosition (finance)EngineeringSimulationReal-time computingArtificial intelligenceTransport engineering

Abstract

fetched live from OpenAlex

Over the past decade, several smart and automated systems have been developed to address the issue of struck-by hazards in construction—that is, workers on foot struck by equipment or equipment struck by equipment. False alarms (false positives and false negatives) are common in such systems, but methods for limiting struck-by hazards have not yet been thoroughly studied or tested for real-world implementations. This study presents two novel four-dimensional (4D) [time and three-dimensional (3D) space] models, a time-sphere model and a time-cuboid model, that are effective in reducing the rate of false alarms. In each developed 4D model, (1) entities’ state information, including 3D position, orientation (roll, pitch, and yaw), and velocity, is acquired and analyzed over time; and (2) the hazardous area around equipment or workers is represented by a sphere or a cuboid with the warning distance adjusted and updated according to the entities’ collected state information; and (3) unsafe-proximity query rules identify and predict contact collisions using relative position, moving direction, speed, and a pairwise 3D unsafe-proximity query. The effectiveness of the developed 4D models was evaluated through simulation and field experiments; however, the data were not wirelessly communicated because the focus of the study was on development, analysis, and comparison of two models for safety hazard identification. The obtained false positive and false negative rates indicate that the two developed 4D models have a strong capability for reducing false alarms. The obtained reduced alarm percentages imply that on average 65% of the alarms triggered by the most prevalent method can be averted by using the time-sphere model and 81% can be reduced by using the time-cuboid model. Furthermore, three major categories of findings are summarized: model comparison, model analysis, and the relationship between alert zone dimensions and model performance. The developed rigorous 4D models can also be employed for several types of contact collision that involve temporal and permanent site facilities, materials transported in air, and equipment and workers on foot. Reduced false alarms will improve construction safety, productivity, and mobility.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.340
Threshold uncertainty score0.410

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
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 integrity0.0000.001
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 head0.422
Teacher spread0.379 · 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