Workplace safety is under serious threat due to the increasing trend of active shooter violence in recent years. Therefore, it becomes essential that the safety of a workplace is rigorously and, most importantly, methodologically assessed against active shooter violence. To serve this purpose, this study proposes a machine learning-optimization framework to assess the safety of a building against possible active shooter violence. First, several state-of-the-art machine learning models are employed to predict an agent’s movement decisions (with directions) under different violence scenarios. The predictions are then utilized in a mixed-integer linear programming model to maximize the agent’s utility under a possible active shooter violence situation. The machine learning models and the proposed optimization model considered several building-specific (e.g., staircase/hiding room capacities, building orientation) and agent-specific (e.g., herding behavior, cognitive delay) attributes to realistically capture the violent situation. The performance of the proposed machine learning-optimization framework is assessed on a two-storied test building. Results indicate that the building configuration (e.g., number and location of the staircases, hiding rooms, exits) as well as agent behaviors, such as herding behavior and cognitive delay, play an important role in the recovery/casualty of civilians under a crisis situation.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-6492 |
| Date | 11 May 2022 |
| Creators | Kerlin, Joseph G |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
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