Computers and Concrete

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Numerical data-driven machine learning model to predict the strength reduction of fire damaged RC columns

  • HyunKyoung Kim (Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology) ;
  • Hyo-Gyoung Kwak (Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology) ;
  • Ju-Young Hwang (Department of Civil Engineering, Dong-Eui University)
  • Received : 2023.05.24
  • Accepted : 2023.08.31
  • Published : 2023.12.25
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Abstract

The application of ML approaches in determining the resisting capacity of fire damaged RC columns is introduced in this paper, on the basis of analysis data driven ML modeling. Considering the characteristics of the structural behavior of fire damaged RC columns, the representative five approaches of Kernel SVM, ANN, RF, XGB and LGBM are adopted and applied. Additional partial monotonic constraints are adopted in modelling, to ensure the monotone decrease of resisting capacity in RC column with fire exposure time. Furthermore, additional suggestions are also added to mitigate the heterogeneous composition of the training data. Since the use of ML approaches will significantly reduce the computation time in determining the resisting capacity of fire damaged RC columns, which requires many complex solution procedures from the heat transfer analysis to the rigorous nonlinear analyses and their repetition with time, the introduced ML approach can more effectively be used in large complex structures with many RC members. Because of the very small amount of experimental data, the training data are analytically determined from a heat transfer analysis and a subsequent nonlinear finite element (FE) analysis, and their accuracy was previously verified through a correlation study between the numerical results and experimental data. The results obtained from the application of ML approaches show that the resisting capacity of fire damaged RC columns can effectively be predicted by ML approaches.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2023R1A2C2005101).

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