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Record W4407983912 · doi:10.18280/ijsse.150116

Design of Worker Rotation for a Precast Concrete Pole Factory Based on Mental Workload

2025· article· en· W4407983912 on OpenAlexvenueno aff
Anizar Anizar, Aulia Ishak, Rabeka Gracia Gurusinga, Dian Hermansyah

Bibliographic record

VenueInternational Journal of Safety and Security Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsnot available
FundersUniversitas Sumatera Utara
KeywordsPrecast concreteFactory (object-oriented programming)WorkloadEngineeringRotation (mathematics)Structural engineeringComputer science

Abstract

fetched live from OpenAlex

This study examines the effect of temperature and noise on workers' mental workload and evaluate the effectiveness of job rotation in mitigating these impacts.The manufacturing plant has a temperature of 35.86 degrees Celsius from the pile formation process and a noise level of 89.32 dBA from the process of releasing piles from the mould.High temperatures and noisy work environments cause workers to feel stressed and fatigued, leading to longer task completion times.The factory implements 2 work shifts per day, but if the daily production target is not met, there will be additional work shifts.Who will work in this irregular addition of work shift is also unclear.The extra shift workers come from the second shift workers of that day and the first shift workers the following day.Additional working hours increase the mental workload for the workers.This problem can be solved through job rotation.This research is classified as explanatory research with the object of study being the work environment and workers of the pile manufacturing factory.The mental workload is measured using the subjective workload assessment technique (SWAT).The novelty of this research lies in the design of work rotation based on mental workload, where in 2 shifts, workers rotate every four hours.

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: none
Teacher disagreement score0.961
Threshold uncertainty score0.335

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 head0.218
Teacher spread0.211 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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