Current fire safety requirements rely on a limited set of standardized fire tests. These tests do not give as a complete picture of how products and buildings behave in real fires. Fire safety in our buildings is really achieved by (i) learning from disasters; (ii) active fire and rescue service intervention; (iii) improvements in other domains such as reliability of electrical appliances; and (iv) a large degree of overinvestment, where expensive safety measures are required also where they provide limited benefit. This approach to fire safety breaks down when new materials and products are introduced in society.
Within the AFireTest project, we fundamentally rethink how fire performance is tested, how fire safety is demonstrated for individual buildings, and how authorities can adjust their regulations accordingly. Instead of relying on a fixed set of standard tests, the project introduces the idea of Adaptive Fire Testing: fire tests are specified in order to obtain a real understanding of a products behaviour in fire. We develop a methodology where test protocols are specified so that we learn as much as possible from each test. The project uses glazing as a case study, as glazing behaviour in fire determines the oxygen supply to the fire, but is still very poorly understood.
The new testing approach we develop relies on probabilistic calculations where we evaluate a very large number of possible scenarios. This quickly becomes computationally very demanding. The standard response is to adopt machine learning, but such models often defy physical laws. In safety critical environments, such as fire safety, we want to be able to trust in realism of our computer simulations. Therefore, within the AFireTest project we invest in physics-informed surrogate modelling: machine learning approaches where we are certain that the results align with physical laws.
Beyond technical innovation, AFireTest also recognizes that fire safety decisions have legal, economic, and societal dimensions. Currently, fire safety requirements are often updated following public outcry after major disasters. Such approach almost unavoidable results in avoidable societal losses (lives lost, resources spent on safety measures that do not help...). Within the AFireTest project, we develop a framework for evaluating the costs and benefits of regulatory proposals regarding fire safety. Adopting such framework will transform discussions on fire safety regulations from a meeting of subjective opinions to a fact-based argumentation on input values and model assumptions.