Testing for Knowledge: Maximizing Information Obtained from Fire Tests, Using Machine Learning Techniques

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Current fire testing procedures for various materials are targeted primarily at compliance. This however introduces a risk of inadequate performance when the actual fire exposure differs from the test situation, be it in duration, maximum temperature, the HRR gradient or other. The proposed research project aims to provide a more complete characterization of the performance of materials or configurations by providing a procedure for targeted testing.As a PhD student, I will be working under the supervision of Dr. Grunde Jomaas from the University of Edinburgh and with the guidance of Dr. Ruben Van Coile from the University of Ghent to touch upon the engineering aspects of the problem as well as the main aspect which is fire safety.The project plan consists of a phased approach, working from specific applications towards a generalized framework.In phase 1 advanced regression techniques are applied to develop surrogate models for established fire tests, i.e. the Cone Calorimeter test and the Corner Burner test. After training the surrogate model with available test data (training set), and determining model architecture through cross validation data (cross validation set), the surrogate model is applied to predict fire test results of unseen samples (test set).In phase 2 the surrogate models are applied to generalize the knowledge obtained from the fire tests. This expansion of the field of application is associated with a loss of confidence in the simulation results. This loss of confidence can, naturally, be reduced through testing. By investigating the underlying mathematics of the surrogate models, test setups can be identified whose result is expected to result in the greatest benefit in confidence for the surrogate model. The surrogate models will be updated iteratively in function of additional test data which are specifically performed for their 'knowledge benefit'. Validation of the approach will be done through the execution of the specific identified tests.Phase 3 derives a general procedure for 'Testing for Knowledge' to be applied in conjunction with any series of fire tests, including the testing of innovative materials or designs. This is considered of great importance as it allows to obtain a comprehensive understanding of fire performance using the least possible number of tests. By iteratively testing set ups which are considered to result in the greatest 'knowledge benefit', a comprehensive fire engineering understanding is obtained at minimal cost.In phase 4 the proposed procedure is validated by application to innovative fire testing (new material, new configuration or other). The specifics of the tests will be determined with respect to testing requirements at the time (i.e. specific material or configuration whose performance needs to be better understood for commercial purposes at the time).