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Evaluating uncertainty in steel-composite structure under fire : application of the ME-MDRM

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Abstract
Probabilistic applications of structural fire engineering will be central to the realisation of complex buildings, where reliance on precedent / experience is insufficient, and an adequate level of safety must be explicitly demonstrated. The practical application of probabilistic methods in structural fire safety has, to date, been limited to simplistic structural systems due to a lack of an efficient, unbiased calculation methodology. Herein, two examples are presented whereby the Maximum Entropy Multiplicative Dimensional Reduction Method, ME-MDRM for short, is applied to estimate the probability density function of performance metrics output from finite element analyses. Given a limited number of model realisations (order of 101), it is shown that the MEMDRM can give valuable insight into the distribution of failure time, and maximum supported load under ISO fire conditions for a composite column, and composite slab panel, respectively. The former is benchmarked against Monte Carlo Simulations, with excellent agreement. Finally, limitations in the stochastic data and in the application of the ME-MDRM itself are discussed, with future research needs identified.
Keywords
fire, probability, SAFIR, numerical model, maximum entropy, ME-MDRM

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Chicago
Van Coile, Ruben, Gernay Thomas, Elhami Khorasani Negar, and Danny Hopkin. 2018. “Evaluating Uncertainty in Steel-composite Structure Under Fire : Application of the ME-MDRM.” In Proceedings of the 10th International Conference on Structures in Fire. Belfast.
APA
Van Coile, R., Thomas, G., Negar, E. K., & Hopkin, D. (2018). Evaluating uncertainty in steel-composite structure under fire : application of the ME-MDRM. Proceedings of the 10th International Conference on Structures in Fire. Presented at the 10th International Conference on Structures in Fire, Belfast.
Vancouver
1.
Van Coile R, Thomas G, Negar EK, Hopkin D. Evaluating uncertainty in steel-composite structure under fire : application of the ME-MDRM. Proceedings of the 10th International Conference on Structures in Fire. Belfast; 2018.
MLA
Van Coile, Ruben, Gernay Thomas, Elhami Khorasani Negar, et al. “Evaluating Uncertainty in Steel-composite Structure Under Fire : Application of the ME-MDRM.” Proceedings of the 10th International Conference on Structures in Fire. Belfast, 2018. Print.
@inproceedings{8566497,
  abstract     = {Probabilistic applications of structural fire engineering will be central to the realisation of complex buildings, where reliance on precedent / experience is insufficient, and an adequate level of safety must be explicitly demonstrated. The practical application of probabilistic methods in structural fire safety has, to date, been limited to simplistic structural systems due to a lack of an efficient, unbiased calculation methodology. Herein, two examples are presented whereby the Maximum Entropy Multiplicative Dimensional Reduction Method, ME-MDRM for short, is applied to estimate the probability density function of performance metrics output from finite element analyses. Given a limited number of model realisations (order of 101), it is shown that the MEMDRM can give valuable insight into the distribution of failure time, and maximum supported load under ISO fire conditions for a composite column, and composite slab panel, respectively. The former is benchmarked against Monte Carlo Simulations, with excellent agreement. Finally, limitations in the stochastic data and in the application of the ME-MDRM itself are discussed, with future research needs identified.},
  author       = {Van Coile, Ruben and Thomas, Gernay and Negar, Elhami Khorasani and Hopkin, Danny},
  booktitle    = {Proceedings of the 10th International Conference on Structures in Fire},
  keyword      = {fire,probability,SAFIR,numerical model,maximum entropy,ME-MDRM},
  language     = {eng},
  location     = {Belfast},
  title        = {Evaluating uncertainty in steel-composite structure under fire : application of the ME-MDRM},
  year         = {2018},
}