Machine learning-based surrogate model for calibrating fire source properties in FDS models of façade fire tests

Abstract

Calibration is an important step in the development of predictive numerical models that involves adjusting input parameters not easily measured in experiments to improve the predictive accuracy of the numerical model compared to the real system. For complex models of façade fires, model calibration can be difficult due to the large number of input parameters that need to be calibrated simultaneously. This paper proposes a machine-learning-based surrogate modelling technique to help with calibrating the fire source in simulations of façade fire tests. Two case studies are presented to assess the feasibility of the proposed method: a simple fire source with a single burner surface based on the JIS A 1310:2015 test, and a complex fire source of a wooden crib based on the BS 8414-2:2015 test. The properties of the fire sources are calibrated based on thermocouple temperatures measured near the cladding surface. In both case studies, the ML-based surrogate model successfully calibrated the fire source properties, resulting in a high level of agreement between the calibrated model and results for experiments (average error = 2.8% and 14.3% for case studies 1 and 2). The proposed method can be applied for various optimisation problems in fire engineering research and design.