Predicting Serious Injury and Fatality Exposure in Construction Industry
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Notice bibliographique
Résumé
Even though the construction industry has been investing heavily in safety management activities, the fatality rates plateaued over the past years. To take proactive action and prevent such severe incidents in work environments, the ability to make robust predictions related to serious injury and fatality (SIF) exposure is key. Only through such reliable predictions, decision-makers can design on-point interventions, allocate safety resources, and make safety process improvements that could save lives. However, making safety predictions has been a constant challenge for safety researchers due (1) the multi-faceted and dynamic nature of safety systems, and (2) data availability issues caused by dependence on rare and highly contextual incident data. This dissertation therefore aims to (1) to create comprehensive and prioritized list of predictors that includes attributes related to the businesses, projects, and crews to fully capture the construction environments; (2) to evaluate the strengths and weaknesses of existing safety performance measurement metrics to choose a dependent variable that could be used in building robust predictive models; (3) to propose High Energy and Controls Assessment (HECA) as a SIF-focused metric that has statistical predictive power and sufficient data generation capacity; and (4) to build a predictive model to forecast SIF exposure through the analysis of an empirical dataset. To establish the predictive model for SIF exposure, 693 field crew observations were made from 28 businesses and 74 projects in the United States and Canada. This dataset is the first of its kind that includes both safety success and exposure to SIF. Along with these observations, information about the business, project, and crew were collected as potential predictors of SIF exposure. Analysis of this empirical dataset allowed the development of a multi-layer perceptron model that could effectively differentiate safety success from an exposure case using non-linear decision boundaries. Future researchers could use this dissertation in designing improved predictive models, choosing robust variables, and creating new research questions for safety interventions. Future research should seek to address safety data collection challenges through automation that could reduce bias, increase the quality and volume of the data that will avail the generation of better predictive models.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle