Guiding adaptive fire testing through expected information gain

This paper presents initial insights into a fire design acceptance framework that applies Adaptive Fire Testing (AFireTest) principles to support more informed and efficient planning of testing campaigns. A core feature of AFireTest is the pre-execution quantification of a test’s expected utility, evaluated in terms of campaign objectives such as uncertainty reduction, cost-efficiency, and environmental impact. We illustrate this evaluation with a bench-scale flammability testing case study. In this context, we use Expected Information Gain (EIG) to estimate how varying trial numbers are expected to reduce uncertainty in ignition time predictions. Results show that EIG stabilises between 10 and 15 trials, suggesting a rational stopping point for the test campaign. Validation with experimental data demonstrates the reliability of EIG as a tool for guiding test design.