Human behavior has become a significant concern in almost every economic activity. Human errors are on the top accident list, leading to death tolls, material losses, and environmental damages. The lack of large and good-quality datasets for Human Reliability Assessment (HRA) studies is still a problem in applications of safety sciences, and the present work proposes an approach to collecting HRA data from simulator utilizing Game Engines (GE) 3D-Virtual Environments (VE). As validation, an experiment was conducted for an Oil and Gas refinery evacuation scenario under toxic cloud release, with a detailed description of environmental development. Two variables were analyzed: evacuation time and individual risk exposure. Then, a Bayesian Belief Network (BBN) was created to investigate the tool for HRA, considering variables related to training, visibility, and complexity. The study provides valuable insights into human behavior and the generation of datasets, representing a helpful tool for data collection.
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