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Uncertainty quantification of ultimate compressive strength of CCFST columns using hybrid machine learning model

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Abstract

This study aims to estimate the value and quantify the uncertainty of the compressive strength of circular concrete-filled steel tube (CCFST) columns under eccentric loading using a hybrid machine learning model, namely PANN, which is a combination of particle swarm optimization (PSO) and artificial neural networks (ANNs). A dataset of 241 experiments is collected and used to construct such a hybrid model. The PANN performance is then compared with other data-mining techniques such as, multiple linear regression (MLR) and multiple adaptive regression spline (MARS), and benchmarked against four design codes, including EC4, ACI, AISC, and AIJ. The obtained results in terms of R of 0.995, RMSE of 11.2, and MAPE of 0.154 demonstrate that PANN is the best model serving as an efficient alternative approach to predict the compressive strength of the CCFST column under eccentric loading. The CCFST ultimate strength is also quantified uncertainty with configuration randomness (e.g., geometry, material, and loading configurations) based on Monte Carlo simulation. The simulation findings indicate the column diameter is the most sensitive to the output, the short columns are less sensitive than the long ones, and the sensitivity of CCFST columns towards uncertainty decreases when the eccentricity increasing.

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Acknowledgements

This research was supported bythe National Research Foundation of Korea (NRF) funded by the Korean government (MSIT) (No. 2018R1A2A2A05018524 and No. 2019R1A4A1021702).

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  1. Department of Civil and Environmental Engineering, Sejong University, 98 Gunja-dong, Gwangjin-gu, Seoul, 143-747, South Korea

    Mai-Suong T. Nguyen, Minh-Chien Trinh & Seung-Eock Kim

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Nguyen, MS.T., Trinh, MC. & Kim, SE. Uncertainty quantification of ultimate compressive strength of CCFST columns using hybrid machine learning model. Engineering with Computers 38 (Suppl 4), 2719–2738 (2022). https://doi.org/10.1007/s00366-021-01339-1

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