ANN-Based Axial Strength Prediction of Short Columns with Double and Bar-Reinforced Concrete-Filled Steel Tubes Subjected to Concentric and Eccentric Loading

Sarkar, Sajal; Chakraborty, Sukanta; Nayak, Sanket

doi:10.1007/s13369-023-08285-8

Cited by 1 publication

(1 citation statement)

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“…Notably, rectangular Concrete-Filled Steel Tube Structural Columns (CFST-SCs) are often constructed using concrete with average strength characteristics. When exposed to elevated temperatures, CFSTs made from the aforementioned concretes exhibit enhanced physical and mechanical properties (Han et al, 2007;Sarkar et al, 2023). This phenomenon is particularly advantageous, as it contributes to the improved performance of CFST columns under challenging conditions.…”

Section: Preparation Of Specimenmentioning

confidence: 99%

Predicting the fire-induced structural performance of steel tube columns filled with SFRC-enhanced concrete: using artificial neural networks approach

George,

Zumba,

Procel Silva

et al. 2024

Front. Built Environ.

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Predicting the axial Shortening strength of concrete-filled steel tubular (CFST) columns is an important problem that this study attempts to solve for civil engineering projects. We suggest using a deep learning-based artificial neural network (ANN) model to address this issue, taking into account the intricate relationship between steel tube and core concrete. The model, called ANN-SFRC (Steel Fibre Reinforced Concrete), surpasses an R2 threshold of 0.90 and achieves impressive R2 values across different types of CFST columns. Compared to traditional linear regression methods, the ANN-SFRC model significantly improves accuracy, with an observed inaccuracy of less than 3% compared to actual values. With its reliable approach to forecasting the behavior of CFST columns under axial compression, this high-performance instrument enhances safety and accuracy during the design and planning stages of civil engineering.

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Section: Preparation Of Specimenmentioning

confidence: 99%