IMPLEMENTATION OF OPERATIONAL AND COMPUTATIONAL SAFETY MODEL TO MITIGATE AND REDUCE INCIDENCE OF HIGHER SEVERITY EVENTS AT LAWRENCE LIVERMORE NATIONAL LABORATORY
Authors
Lara, Raul B., Jr.
Subjects
Lawrence Livermore National Laboratory
LLNL
safety
data modeling
Department of Energy
DOE
nuclear safety
health and safety
injury
illness
occupational health and safety
occupational health and safety management systems
OHSMS
industrial safety
nuclear
health
nuclear operations
research and development
potential serious injuries or fatalities
pSIF
fatalities
LLNL
safety
data modeling
Department of Energy
DOE
nuclear safety
health and safety
injury
illness
occupational health and safety
occupational health and safety management systems
OHSMS
industrial safety
nuclear
health
nuclear operations
research and development
potential serious injuries or fatalities
pSIF
fatalities
Advisors
Crook, Matthew R.
Nakasaki, Steve, Lawrence Livermore National Laboratory
Date of Issue
2024-09
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
High-severity events, such as serious injuries or fatalities, pose significant risks to workers, the environment, national security, and the reputation of national research laboratories, such as Lawrence Livermore National Laboratory (LLNL). A case study of 1,081 injury and illness cases was performed to assess the feasibility of implementing a framework to mitigate potential serious injuries or fatalities (pSIF) at LLNL. Review of the cases data quality showed that >86% of incidents had sufficient information to adopt the framework at LLNL.Additionally, the case study reviewed institutional responses to the incidents. A computational model was developed to simulate pSIF incident distributions to deal with limitations from the case study, as well as to simulate institutional response. The findings concluded that while pSIF incidents were rare (<1% of total cases), the framework can improve organizational risk management by providing a consistent approach to incident response. It also suggests that resource allocation should focus on the highest risk areas, including noise exposure, overexertion, and repetitive motion. The computational model and framework offers a structured approach to reduce pSIF incidents, ultimately contributing to a safer research environment at LLNL. Although implementing the framework can enhance risk management, it requires commitment to quality data collection, incident classification, and integrated management systems for maximum efficacy.
Type
Thesis
Description
Series/Report No
Department
Organization
Identifiers
NPS Report Number
Sponsors
Lawrence Livermore National Laboratory
Funder
Format
Citation
Distribution Statement
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