228 - Quantifying the expected utility of fire tests with Andrea Franchini

What do you expect from running a fire test? I would hope that it improves my state of knowledge. But do they do this? We often pursue them blindly, but it seems there is a way to do this in an informed way. In this episode we explore a rigorous, practical way to select and design experiments by asking a sharper question: which test delivers the most decision-changing information for the least cost, time, and impact. With Dr. Andrea Franchini of Ghent University, we unpack a Bayesian framework that simulates possible outcomes before you touch a sample, updates your state of knowledge, and quantifies the utility of that update as uncertainty reduction, economic value, or environmental benefit.First, we reframe testing around information gain. Starting from a prior distribution for the parameter you care about, we model candidate experiments and compute how each would shift the posterior. The gap between prior and posterior is the signal; diminishing returns tell you when to stop. In the cone calorimeter case on PMMA ignition time, early trials yield large gains, then the curve flattens, revealing a rational stopping point and a transparent way to plan sample counts and budgets. The same structure scales from simple statistical models to high-fidelity or surrogate models when physics and geometry matter.Then we tackle a post-fire decision with real financial stakes: repair a reinforced concrete slab, or accept residual risk. We connect Eurocode-based thermal analysis to two test options—rebound hammer temperature proxies and discoloration depth—and compute their value of information. By translating updated probabilities of exceeding 600°C into expected costs of repair versus undetected failure, we show how to choose the test that pays back the most. In the studied scenario, the rebound hammer provides higher value, even after accounting for testing costs, but the framework adapts to different buildings, cost ratios, and risk appetites.Beyond pass-fail, this approach helps optimize sensor layouts, justify added instrumentation, and balance multiple objectives—uncertainty, money, and environmental impact—without slipping into guesswork. If you’re ready to move from ritual testing to evidence that changes outcomes, this conversation maps the path. Papers to read after this:Which test is the best? Choosing the fire test that maximizes the information gainQuantifying the expected utility of fire tests and experiments before execution----The Fire Science Show is produced by the Fire Science Media in collaboration with OFR Consultants. Thank you to the podcast sponsor for their continuous support towards our mission.