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Testing for knowledge : maximising information obtained from fire tests by using machine learning techniques

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Abstract
A machine learning (ML) algorithm was applied to predict the onset of flashover in 1:5 scale Room Corner Test experiments with sandwich panels. Towards this end, a penalized logistic regression model was chosen to detect the relevant variables and consequently provided a tool that can be used to make predictions of unseen samples. The method indicates that a deeper understanding of the contributing factors leading to flashover can be achieved. Furthermore, it allows a more nuanced ranking than currently offered by the commonly used classification methods for reaction to fire tests. The proposed methodology shows a substantial value in terms of guidance for future large and intermediate scale testing. In particular, it is foreseen that the method will be extremely useful for assessing and understanding the behaviour of innovative materials and design solutions.
Keywords
fire, machine learning, room corner test, flashover, logistic regression

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Please use this url to cite or link to this publication:

MLA
Dexters, Arjan et al. “Testing for Knowledge : Maximising Information Obtained from Fire Tests by Using Machine Learning Techniques.” Proceedings of Interflam 2019. 2019. Print.
APA
Dexters, A., Leisted, R., Van Coile, R., Welch, S., & Jomaas, G. (2019). Testing for knowledge : maximising information obtained from fire tests by using machine learning techniques. Proceedings of Interflam 2019. Presented at the Interflam 2019.
Chicago author-date
Dexters, Arjan, Rolff Leisted, Ruben Van Coile, Stephen Welch, and Grunde Jomaas. 2019. “Testing for Knowledge : Maximising Information Obtained from Fire Tests by Using Machine Learning Techniques.” In Proceedings of Interflam 2019.
Chicago author-date (all authors)
Dexters, Arjan, Rolff Leisted, Ruben Van Coile, Stephen Welch, and Grunde Jomaas. 2019. “Testing for Knowledge : Maximising Information Obtained from Fire Tests by Using Machine Learning Techniques.” In Proceedings of Interflam 2019.
Vancouver
1.
Dexters A, Leisted R, Van Coile R, Welch S, Jomaas G. Testing for knowledge : maximising information obtained from fire tests by using machine learning techniques. Proceedings of Interflam 2019. 2019.
IEEE
[1]
A. Dexters, R. Leisted, R. Van Coile, S. Welch, and G. Jomaas, “Testing for knowledge : maximising information obtained from fire tests by using machine learning techniques,” in Proceedings of Interflam 2019, Egham, United Kingdom, 2019.
@inproceedings{8622485,
  abstract     = {A machine learning (ML) algorithm was applied to predict the onset of flashover in 1:5 scale Room Corner Test experiments with sandwich panels. Towards this end, a penalized logistic regression model was chosen to detect the relevant variables and consequently provided a tool that can be used to make predictions of unseen samples. The method indicates that a deeper understanding of the contributing factors leading to flashover can be achieved. Furthermore, it allows a more nuanced ranking than currently offered by the commonly used classification methods for reaction to fire tests. The proposed methodology shows a substantial value in terms of guidance for future large and intermediate scale testing. In particular, it is foreseen that the method will be extremely useful for assessing and understanding the behaviour of innovative materials and design solutions. },
  author       = {Dexters, Arjan and Leisted, Rolff and Van Coile, Ruben and Welch, Stephen and Jomaas, Grunde},
  booktitle    = {Proceedings of Interflam 2019},
  keywords     = {fire,machine learning,room corner test,flashover,logistic regression},
  language     = {eng},
  location     = {Egham, United Kingdom},
  pages        = {12},
  title        = {Testing for knowledge : maximising information obtained from fire tests by using machine learning techniques},
  year         = {2019},
}