Project description
Building new knowledge-based fire testing approaches
All construction products need to meet specific fire safety requirements. Standardised tests are currently used to verify this, but these tests fall short of providing an in-depth understanding of how construction products will perform during a fire. Since there are many uncertainties, new tools are needed. The ERC-funded AFireTest project will develop testing whereby optimum fire tests are determined from an infinite number of possible test specifications. An innovative case study will be undertaken, using modern glazing and load bearing glass. Moreover, a framework for advanced “grey” surrogate modelling will be developed, combining the pattern identification strengths of machine learning. To encourage uptake of the new framework, AFireTest will develop a methodology for cost-benefit evaluation of fire safety compliance frameworks.
Objective
The current fire safety paradigm is based on a set of standardized tests which have been developed as part of a prescriptive design framework, and do not provide in-depth understanding of construction products’ fire performance. The resulting incomplete fire performance characterization hampers the much needed innovation in the built environment. The current fire safety paradigm also places tremendous emphasis on the expertise of controlling bodies (AHJ), making them responsible both for the specification of detailed prescriptive rules, and for the acceptance of performance based designs. This is unsustainable in the face of innovation.
AFireTest strives to induce a paradigm shift in fire safety science and engineering (FSSE). The core of AFireTest is the development of Adaptive Fire Testing whereby optimum fire tests are determined from the infinite number of possible test specifications through the maximum expected net information gain (Value of Information, VoI). This will be developed using modern glazing and load bearing glass as innovative case study, resulting in breakthroughs in fire performance understanding. Secondly, a framework for advanced ‘grey’ surrogate modelling will be developed, combining the pattern identification strengths of machine learning with fundamental FSSE constraints. This will introduce a powerful new tool to FSSE and enable the VoI optimization. A grey modelling approach will also be developed for quasi-instantaneous building specific risk evaluations, allowing a new approach to the AHJ acceptance of fire designs. The future operationalization of the new framework for fire design acceptance will require large follow-up investments. Thus, stakeholder buy-in is crucial. Therefore, AFireTest will develop a methodology for the cost-benefit evaluation of fire safety frameworks. For the first time, fire safety approaches will be evaluated from the perspective of Law and Economics, laying the groundwork for an entirely new field of study.
Fields of science
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Topic(s)
Funding Scheme
HORIZON-ERC - HORIZON ERC GrantsHost institution
9000 Gent
Belgium