Title:
Regression-Based Surrogate Models for the Probabilistic Study of Fire Exposed Composite Structures Considering Tensile Membrane Action
Author(s):
Ranjit Kumar Chaudhary, Ruben Van Coile, and Thomas Gernay
Publication:
Symposium Paper
Volume:
350
Issue:
Appears on pages(s):
123-131
Keywords:
composite slab, machine learning, residual capacity, failure probability, fire
DOI:
10.14359/51734318
Date:
11/1/2021
Abstract:
The probabilistic study of fire exposed structures is laborious and computationally challenging, especially when using advanced numerical models. Moreover, fragility curves developed through traditional approaches apply only to a particular design (structural detailing, fire scenario). Any alteration in design necessitates the computationally expensive re-evaluation of the fragility curves. Considering the above challenges, the use of surrogate models has been proposed for the probabilistic study of fire exposed structures. Previous contributions have confirmed the potential of surrogate models for developing fragility curves for single structural members including reinforced concrete slabs and columns. Herein, the potential of regression-based surrogate models is investigated further with consideration of structural systems. Specifically, an advanced finite element model for evaluating the fire performance of a composite slab panel acting in tensile membrane action is considered. A surrogate model is developed and used to establish fire fragility curves. The results illustrate the potential of surrogate modeling for probabilistic structural fire design of composite structures.
Related References:
1. Gillie, M., Usmani, A. S., and Rotter, J. M., 2001, “A structural analysis of the first Cardington test, Journal of Constructional Steel Research”, 57(6), 581-601.
2. Wald, F., Da Silva, L.S., Moore, D.B., Lennon, T., Chladna, M., Santiago, A., Beneš, M. and Borges, L., 2006, “Experimental behaviour of a steel structure under natural fire”, Fire Safety Journal, 41(7), 509-522.
3. Gernay, T., Khorasani, N.E. and Garlock, M., 2016, “Fire fragility curves for steel buildings in a community context: A methodology”, Engineering Structures, 113, 259-276.
4. Gernay, T., Khorasani, N.E. and Garlock, M., 2019, “Fire fragility functions for steel frame buildings: sensitivity analysis and reliability framework”, Fire Technology, 55(4), 1175-1210.
5. Chaudhary, R.K., Roy, T. and Matsagar, V., 2020, “Member and structural fragility of reinforced concrete structure under fire”, Journal of Structural Fire Engineering, 11(4), 409-435.
6. McKinney, J. and Ali, F., 2014, “Artificial Neural Networks for the Spalling Classification & Failure Prediction Times of High Strength Concrete Columns”, Journal of Structural Fire Engineering, 5(3), 203-214.
7. Seitllari, A. and Naser, M.Z., 2019, “Leveraging artificial intelligence to assess explosive spalling in fireexposed RC columns”, Comput. Concr, 24, 271-282.
8. Naser, M. Z., 2019, “Fire resistance evaluation through artificial intelligence-A case for timber structures”, Fire safety journal, 105, 1-18.
9. Chaudhary R.K., Jovanovic B., Van Coile R., Gernay T., (2020, “Generalized fragility curves for concrete columns exposed to fire through surrogate modelling”, 11th International Conference on Structures in Fire, 322-332.
10. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V. and Vanderplas, J., 2011, “Scikit-learn: Machine learning in Python. the Journal of machine Learning research”, 12, 2825-2830.
11. Gernay, T., Van Coile, R., Khorasani, N.E. and Hopkin, D., 2019, “Efficient uncertainty quantification method applied to structural fire engineering computations”, Engineering Structures, 183, 1-17.
12. Franssen, J.M. and Gernay, T., 2017, “Modeling structures in fire with SAFIR®: Theoretical background and capabilities”, Journal of Structural Fire Engineering, 8(3), 300-323.
13. Gernay, T., Millard, A., and Franssen, J. M., 2013, “A multiaxial constitutive model for concrete in the fire situation: Theoretical formulation”, International Journal of Solids and Structures, 50(22-23), 3659-3673.
14. Gernay, T. and Franssen, J.M., 2012, “A formulation of the Eurocode 2 concrete model at elevated temperature that includes an explicit term for transient creep”, Fire Safety Journal, 51, 1-9.
15. Qureshi, R., Ni, S., Elhami Khorasani, N., Van Coile, R., Hopkin, D. and Gernay, T., 2020, “Probabilistic Models for Temperature-Dependent Strength of Steel and Concrete”, Journal of Structural Engineering, 146(6), 04020102.
16. Elhami Khorasani, N., Gernay, T., and Fang, C., 2019, “Parametric study for performance-based fire design of US prototype composite floor systems”, Journal of Structural Engineering, 145(5), 04019030.
17. Chaudary RK, Van Coile R, Gernay T. (2021). “Potential of Surrogate Modelling for Probabilistic Fire Analysis of Structures”. Fire Technology, doi: 10.1007/s10694-021-01126-w.
18. James, G., Witten, D., Hastie, T. and Tibshirani, R., 2013, “An introduction to statistical learning”, New York: springer , 112.
19. Khorasani, N.E., Gardoni, P. and Garlock, M., 2015, “Probabilistic fire analysis: material models and evaluation of steel structural members”. Journal of Structural Engineering, 141(12), 04015050.