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Computationally efficient estimation of the probability density function for the load bearing capacity of concrete columns exposed to fire

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Abstract
Concrete columns are critical for the stability of structures in case of fire. In order to allow for a true Perfor-mance Based Design, the design should be based on considerations of risk and reliability. Consequently, the probability density function (PDF) which describes the load-bearing capacity of concrete columns during fire exposure has to be assessed. As second order effects can be very significant for columns, traditional probabil-istic methods to determine the PDF become very computationally expensive. More precisely, for most current numerical calculation tools (e.g. Finite Element), the computational requirements are so high that traditional Monte Carlo simulations become infeasible for any practical application. In order to tackle this, a computa-tionally very efficient method is presented and applied in this paper. The method combines the Maximum En-tropy Principle together with the Multiplicative Dimensional Reduction Method, and Gaussian Interpolation, resulting in an estimation of the full PDF requiring only a very limited number of numerical calculations. Alt-hough the result is necessarily an approximation, it gives very good assessment of the PDF and it is a signifi-cant step forward towards true risk- and reliability-based structural fire safety.
Keywords
concrete column, fire, maximum entropy, probability density functionn, ME-MDRM

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Chicago
Van Coile, Ruben, Georgios Balomenos, Mahesh Pandey, Robby Caspeele, Pieterjan Criel, Lijie Wang, and Strauss Alfred. 2016. “Computationally Efficient Estimation of the Probability Density Function for the Load Bearing Capacity of Concrete Columns Exposed to Fire.” In Proceedings of the 2016 International Symposium of the International Association for Life-Cycle Civil Engineering (IALCCE 2016). Delft.
APA
Van Coile, R., Balomenos, G., Pandey, M., Caspeele, R., Criel, P., Wang, L., & Alfred, S. (2016). Computationally efficient estimation of the probability density function for the load bearing capacity of concrete columns exposed to fire. Proceedings of the 2016 International Symposium of the International Association for Life-Cycle Civil Engineering (IALCCE 2016). Presented at the International Symposium of the International Association for Life-Cycle Civil Engineering (IALCCE 2016), Delft.
Vancouver
1.
Van Coile R, Balomenos G, Pandey M, Caspeele R, Criel P, Wang L, et al. Computationally efficient estimation of the probability density function for the load bearing capacity of concrete columns exposed to fire. Proceedings of the 2016 International Symposium of the International Association for Life-Cycle Civil Engineering (IALCCE 2016). Delft; 2016.
MLA
Van Coile, Ruben, Georgios Balomenos, Mahesh Pandey, et al. “Computationally Efficient Estimation of the Probability Density Function for the Load Bearing Capacity of Concrete Columns Exposed to Fire.” Proceedings of the 2016 International Symposium of the International Association for Life-Cycle Civil Engineering (IALCCE 2016). Delft, 2016. Print.
@inproceedings{8130731,
  abstract     = {Concrete columns are critical for the stability of structures in case of fire. In order to allow for a true Perfor-mance Based Design, the design should be based on considerations of risk and reliability. Consequently, the probability density function (PDF) which describes the load-bearing capacity of concrete columns during fire exposure has to be assessed. As second order effects can be very significant for columns, traditional probabil-istic methods to determine the PDF become very computationally expensive. More precisely, for most current numerical calculation tools (e.g. Finite Element), the computational requirements are so high that traditional Monte Carlo simulations become infeasible for any practical application. In order to tackle this, a computa-tionally very efficient method is presented and applied in this paper. The method combines the Maximum En-tropy Principle together with the Multiplicative Dimensional Reduction Method, and Gaussian Interpolation, resulting in an estimation of the full PDF requiring only a very limited number of numerical calculations. Alt-hough the result is necessarily an approximation, it gives very good assessment of the PDF and it is a signifi-cant step forward towards true risk- and reliability-based structural fire safety.},
  author       = {Van Coile, Ruben and Balomenos, Georgios and Pandey, Mahesh and Caspeele, Robby and Criel, Pieterjan and Wang, Lijie and Alfred, Strauss},
  booktitle    = {Proceedings of the 2016 International Symposium of the International Association for Life-Cycle Civil Engineering (IALCCE 2016)},
  isbn         = {9781138028470},
  language     = {eng},
  location     = {Delft},
  pages        = {8},
  title        = {Computationally efficient estimation of the probability density function for the load bearing capacity of concrete columns exposed to fire},
  year         = {2016},
}