Artificial Cognition to Predict and Explain the Potential Unsafe Behaviors of Construction Workers
Why this work is in the frame
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Bibliographic record
Abstract
Unsafe behavior is considered the primary cause of construction safety accidents. However, the main measures for unsafe behavior management are real-time monitoring and postevent correction, which cannot prevent unsafe behavior. Therefore, this study attempted to construct an artificial cognition approach to predict the potential unsafe behavior of workers and explain why workers engage in unsafe behaviors. First, based on the cognitive model of unsafe behavior, data on workers were collected with a questionnaire, and the cognitive model was validated. Second, the cognitive process of unsafe behaviors was analyzed using latent class analysis, and the cognitive characteristics of four types of unsafe behaviors were obtained. Subsequently, with the cognitive model of unsafe behavior as the input attribute, seven types of algorithms (gradient Boosting, random forest, naïve bayes, back propagation, K-nearest neighbor, logistic regression, and support vector machine) were used to construct artificial cognition to predict the potential unsafe behaviors of workers. The results showed that all seven algorithms performed well for prediction. Thus, artificial cognition that simulates the cognitive process of unsafe behavior is not limited to particular algorithms. Finally, artificial cognition was empirically validated in a construction project. The findings demonstrated that artificial cognition could effectively predict the potential unsafe behavior of workers and provide an explanation for why workers engage in unsafe behaviors.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it