Synapse
⌘+K
Synapse
PulseExploreJournal ClubResearchersJournals
Instagram
HomeJournal ClubExplore
October 3, 2025EPRA International Journal of Environmental Economics Commerce and Educational ManagementOpen Access

AI-Powered Predictive Risk Assessment Models for Preventing Workplace Accidents in the U.S. Mining Industry: Strengthening Safety Under Msha Regulation

View Full Paper
Ask AI
Bookmark
Share

Authors

KAKayode Agbolahan AjiboseTATobias Kwame AdukpoSASeyram Yawa Adza

Discussion

Loading...

Member takes

Overview

This analysis reveals AI predicts workplace accidents in the U.S. mining sector, suggesting improved HR-led safety measures.

Key Points

  • Predictive risk assessment models reduce machinery-related fatalities by 24%, indicating significant safety improvements.
  • Two linear regression models achieved 70-76% accuracy, allowing for risk detection up to 48 hours in advance.
  • The Total Mine Risk framework was validated against U.S. mining datasets to support HR strategies for safety.
  • Transparent AI analytics enhance employee trust while positioning HR as a strategic partner in accident prevention.

Cite This Study

Ajibose et al. (2025) studied this question.

synapsesocial.com/papers/68e02f46f0e39f13e7fa2c8ehttps://doi.org/10.36713/epra24263
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Investigating advanced technologies to enhance worker safety in hazardous mining environments: A Review2025 · 2 citations
  2. 2Risk Mapping and Comprehensive Safety Analysis in The Indian Mining Industry2026
  3. 3Risk Mapping and Comprehensive Safety Analysis in The Indian Mining Industry2026
  4. 4Predictive Modelling of Workplace Hazards and Accident Probabilities in Ghana’s Mining Sector2026
  5. 5Mining Safety Through Artificial Intelligence: A Survey2024 · 2 citations