Fire Safety Engineering is required at every stage in the life cycle of modern-day buildings. Fire safety design, detection and suppression, and emergency response are all vital components of Structural Fire Safety but are usually perceived as independent issues. Sensor deployment and exploitation is now common place in modern buildings for means such as temperature, air quality and security management. Despite the potential wealth of information these sensors could afford fire fighters, the design of sensor networks within buildings is entirely detached from procedures associated to emergency management. The experiences of Dalmarnock Fire Test Two showed that streams of raw data emerging from sensors lead to a rapid information overload and do little to improve the understanding of the complex phenomenon and likely future events during a real fire. Despite current sensor technology in other fields being far more advanced than that of fire, there is no justification for more complex and expensive sensors in this context. In isolation therefore, sensors are not sufficient to aid emergency response.udFire modelling follows a similar path. Two studies of Dalmarnock Fire Test One demonstrate clearly the current state of the art of fire modelling. A Priori studies by Rein et al. 2009 showed that blind prediction of the evolution of a compartment fire is currently beyond the state of the art of fire modelling practice. A Posteriori studies by Jahn et al. 2007 demonstrated that even with the provision of large quantities of sensor data, video footage, and prior knowledge of the fire; producing a CFD reconstruction was an incredibly difficult, laborious, intuitive and repetitive task.udFire fighting is therefore left as an isolated activity that does not benefit from sensor data or the potential of modelling the event. In isolation sensors and fire modelling are found lacking. Together though they appear to form the perfect compliment. Sensors provide a plethora of information which lacks interpretation. Models provide a method of interpretation but lack the necessary information to make this output robust. Thus a mechanism to achieve accurate, timely predictions by means of theoretical models steered by continuous calibration against sensor measurements is proposed.
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