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Mechanistically Informed Machine Learning and Artificial Intelligence in Fire Engineering and Sciences

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Abstract

Fire is a chaotic and extreme phenomenon. While the past few years have witnessed the success of integrating machine intelligence (MI) to tackle equally complex problems in parallel fields, we continue to shy away from leveraging MI to study fire behavior or to evaluate fire performance of materials and structures. In order to advocate for the use of MI, this review showcases the merit of adopting mechanistically-informed MI to answer some of the burning questions, multi-dimensional and ill-defined problems fire engineers and scientists are facing. This review also sympathizes with the fact that a traditional curriculum does not often cover principles of MI and hence it starts by introducing a number of machine learning (ML) and artificial intelligence (AI) techniques such as deep learning, metaheuristics, decision trees, random forest, support vector machines etc. Then, this review details recommended procedures associated with preparing databases and carrying out a proper MI-tailored fire analysis via examples; to enable researchers and practitioners from implementing MI with ease. Towards the end of this review, a number of concerns and challenges are identified to stimulate the curiosity of interested readers and accelerate future research works within fire engineering and sciences (FES).

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taken from Choi and Chen—H5 [98])

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Notes

  1. In this review, machine intelligence refers to techniques belonging to “machine learning” (ML) and “artificial intelligence” (AI).

  2. Mechanistic models are commonly divided into deterministic and stochastic models. In the first type of models, results are fully determined by values of chosen inputs, as well as initial boundary conditions. As such, the output of a deterministic model for a given set of inputs yield same results every time the model is applied. In contrast, stochastic models accommodate inherent parametric randomness that is reflected by distributions rather than nominal values. Thus, the same inputs and boundary conditions may result in a range of outputs [130].

  3. In the majority of cases, a MI algorithm will devise an implicit function. Only a few algorithms can devise an explicit function. One should note that in all cases, such a mapping function may not be even formulated using traditional methods.

  4. An ANN algorithm with 2+ hidden layers is formally referred to as “deep learning”.

  5. One should note that the number of classes and classifications of heuristics and metaheuristics significantly varies across different works/disciplines.

  6. A similar logic can also be used to assess the raised questions 2 and 3.

  7. For the sake of completeness, the possible features may or may not be identical in each of the phenomena at hand.

  8. If information on other fire conditions are available, then “temperature–time curve” can be considered a feature.

  9. Of course, information on these assumptions can be updated if the user decides to develop a much more realistic MI model.

  10. Ongoing MI algorithms are now being developed to quantitively measure the magnitude of fire-damage i.e. 20% loss of reinforcement etc.

  11. Excel contains a command “RAND” that can be used to generate random numbers assigned to all observations.

  12. One should also note that validation and testing sets can also be combined into one set.

  13. One should keep in mind that the formulated hypothesis can, and in many instances dedicate, the type of the algorithm to be used.

  14. One should also note that the bulk of the reviewed works covered the use of MI on wildfires as such events have been fully documented and naturally contains large datasets.

  15. The use of FE simulations to generate observations is an option. However, since we continue to lack a standardized simulation procedure, and knowing the amount of assumptions used in developing FE model, a MI user should be cautious of this practice.

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Acknowledgement

The author would like to thank the Editor-in-Chief, Prof. G. Rein, as well as the editorial team at Fire Technology for sharing the J.M. Watts’ editorial, and for their support of this work, and of this special issue dedicated to “Smart systems in Fire Engineering”. Finally, the author is dedicating this work to his sister-in-law who is battling Stage 4 cancer at the moment; “Muradjia, you got this”.

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  1. Glenn Department of Civil Engineering, Clemson University, Clemson, USA

    M. Z. Naser

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Naser, M.Z. Mechanistically Informed Machine Learning and Artificial Intelligence in Fire Engineering and Sciences. Fire Technol 57, 2741–2784 (2021). https://doi.org/10.1007/s10694-020-01069-8

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