Machine learning driven smart fire safety design of false ceiling and emergency response

In modern buildings, false ceilings are widely used for building services systems and aesthetic purposes, but they also pose challenges in terms of fire safety. A fire accident typically results from the failure of multiple safety measures or components. In many fire accidents, fire and smoke reached the false ceiling and kept spreading in the interstitial space without any detection. This study first generates a numerical database of false ceiling fire scenarios by varying the room dimensions, false-ceiling leakage area, fire size and locations. Then, a smart model based on machine learning to predict the fire smoke motion below and above the false ceiling is developed. The trained model is capable to predict the activation time of fire detectors and sprinklers for any given false ceiling design and fire scenario. This methodology enables a designer to generate multiple fire scenarios and determine the available safe egress time (ASET) for performance-based fire engineering designs especially in terms of fire detection time. In case of a real fire, with the data feed from the fire sensor network, the trained machine learning model can further predict the critical building fire events with the false ceiling, such as multi-compartment fires, smoke in the evacuation path, and structural failures. This work proposes a smart framework for improving the building fire safety design of false ceilings and the sensor-driven fire forecast to support firefighting.It enhances the emergency response processes by enabling dynamic risk assessment through prediction of critical events.