The impact of sampling methods on evacuation model convergence and egress time

Abstract

Abstract Simulating human behaviour in fire is often one of the main challenges in designing complex buildings, structures or sites for the life safety of occupants. In fact, evacuation simulations represent a fundamental input to assess fire safety performance using a risk analysis approach. The variability in evacuee behaviours (e.g. pre-evacuation delays and uncongested walking speed) can be probabilistically simulated in egress models using distribution functions. The application of probabilistic simulations requires the input distributions to be sampled. This paper describes a series of eight repeated trial evacuations that were carried out using a classroom-based scenario. The paper then investigates how four different sampling methods (namely Simple Random, Stratified, Inversed Stratified and Halton) affect the ability of a computational egress tool to reach convergence when determining the total time for occupants to leave the room. The analysis found that the Stratified and the Inverse Stratified sampling approaches require the least number of simulation runs to converge while the Halton sampling approach needs the greatest number of simulation runs. Moreover, the results indicate that the Halton sampling generates the highest variance for the simulated total evacuation time and thus is more effective at examining scenarios that utilise the extreme ends of the distribution functions.