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Steel and Composite Structures
  Volume 33, Number 4, November25 2019 , pages 583-594

Prediction of ultimate load capacity of concrete-filled steel tube columns using multivariate adaptive regression splines (MARS)
Cigdem Avci-Karatas

    In the areas highly exposed to earthquakes, concrete-filled steel tube columns (CFSTCs) are known to provide superior structural aspects such as (i) high strength for good seismic performance (ii) high ductility (iii) enhanced energy absorption (iv) confining pressure to concrete, (v) high section modulus, etc. Numerous studies were reported on behavior of CFSTCs under axial compression loadings. This paper presents an analytical model to predict ultimate load capacity of CFSTCs with circular sections under axial load by using multivariate adaptive regression splines (MARS). MARS is a nonlinear and non-parametric regression methodology. After careful study of literature, 150 comprehensive experimental data presented in the previous studies were examined to prepare a data set and the dependent variables such as geometrical and mechanical properties of circular CFST system have been identified. Basically, MARS model establishes a relation between predictors and dependent variables. Separate regression lines can be formed through the concept of divide and conquers strategy. About 70% of the consolidated data has been used for development of model and the rest of the data has been used for validation of the model. Proper care has been taken such that the input data consists of all ranges of variables. From the studies, it is noted that the predicted ultimate axial load capacity of CFSTCs is found to match with the corresponding experimental observations of literature.
Key Words
    concrete-filled steel tube column (CFSTC); concrete; steel; modeling; ultimate axial load capacity; multivariate adaptive regression splines (MARS); composite structures; statistical modeling technique; nonlinear regression model
Department of Transportation Engineering, Faculty of Engineering, Yalova University, Yalova, 77200, Turkey.

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