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MODELLING EVACUATION STRATEGIES UNDER DYNAMIC CONDITIONS DUE TO OBSTACLE LOCATIONS BASED ON A SEMANTIC 3D BUILDING MODELS
MODELLING EVACUATION STRATEGIES UNDER DYNAMIC CONDITIONS DUE TO OBSTACLE LOCATIONS BASED ON A SEMANTIC 3D BUILDING MODELS
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MODELLING EVACUATION STRATEGIES UNDER DYNAMIC CONDITIONS DUE TO OBSTACLE LOCATIONS BASED ON A SEMANTIC 3D BUILDING MODELS
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MODELLING EVACUATION STRATEGIES UNDER DYNAMIC CONDITIONS DUE TO OBSTACLE LOCATIONS BASED ON A SEMANTIC 3D BUILDING MODELS
MODELLING EVACUATION STRATEGIES UNDER DYNAMIC CONDITIONS DUE TO OBSTACLE LOCATIONS BASED ON A SEMANTIC 3D BUILDING MODELS

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MODELLING EVACUATION STRATEGIES UNDER DYNAMIC CONDITIONS DUE TO OBSTACLE LOCATIONS BASED ON A SEMANTIC 3D BUILDING MODELS
MODELLING EVACUATION STRATEGIES UNDER DYNAMIC CONDITIONS DUE TO OBSTACLE LOCATIONS BASED ON A SEMANTIC 3D BUILDING MODELS
Journal Article

MODELLING EVACUATION STRATEGIES UNDER DYNAMIC CONDITIONS DUE TO OBSTACLE LOCATIONS BASED ON A SEMANTIC 3D BUILDING MODELS

2023
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Overview
The evacuation path from inside a building to safe point outside becomes highly unpredictable due to changes in the local geometry or presence of obstacles during a disaster like a fire. During emergencies, Evacuees need appropriate information and hence prediction of an unobstructed path, as it emerges, needs to be computed well. Understanding the exits with its allowable people flow rate; the type - door or alternative exit such as windows, balconies, etc.; and its role as a node in the graph network is important to ensure safe and timely evacuation from a building. The study here evaluates how obstacles present in the evacuation route affect the removal of the last person. These obstacles, such as furniture, decrease the flow rate at which evacuees can escape. A subspace model is proposed for geometric spaces or carpet areas containing obstacles and is used to compute the shortest obstacle-free paths. The occupancy is considered within the subspaces containing obstacles. The proposed method clearly shows that a graph-based path generation using a subspace model improves the computation time, can be dynamically adapted, and can be scalable across geometric spaces. The results clearly show the impact of the obstacles, with a 2× to 6× rise when compared to obstacle-free scenarios.